diff --git a/.env.example b/.env.example index 80e2286caec..812986dca30 100644 --- a/.env.example +++ b/.env.example @@ -394,24 +394,6 @@ IMAGE_TOOLS_DEBUG=false # CONTEXT_COMPRESSION_THRESHOLD=0.85 # Compress at 85% of context limit # Model is set via compression.summary_model in config.yaml (default: google/gemini-3-flash-preview) -# ============================================================================= -# RL TRAINING (Tinker + Atropos) -# ============================================================================= -# Run reinforcement learning training on language models using the Tinker API. -# Requires the rl-server to be running (from tinker-atropos package). - -# Tinker API Key - RL training service -# Get at: https://tinker-console.thinkingmachines.ai/keys -# TINKER_API_KEY= - -# Weights & Biases API Key - Experiment tracking and metrics -# Get at: https://wandb.ai/authorize -# WANDB_API_KEY= - -# RL API Server URL (default: http://localhost:8080) -# Change if running the rl-server on a different host/port -# RL_API_URL=http://localhost:8080 - # ============================================================================= # SKILLS HUB (GitHub integration for skill search/install/publish) # ============================================================================= diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index 76580d6e8e5..00000000000 --- a/.gitmodules +++ /dev/null @@ -1,3 +0,0 @@ -[submodule "tinker-atropos"] - path = tinker-atropos - url = https://github.com/nousresearch/tinker-atropos diff --git a/AGENTS.md b/AGENTS.md index da9f903eefb..d5d32f99c3d 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -56,7 +56,6 @@ hermes-agent/ ├── tui_gateway/ # Python JSON-RPC backend for the TUI ├── acp_adapter/ # ACP server (VS Code / Zed / JetBrains integration) ├── cron/ # Scheduler — jobs.py, scheduler.py -├── environments/ # RL training environments (Atropos) ├── scripts/ # run_tests.sh, release.py, auxiliary scripts ├── website/ # Docusaurus docs site └── tests/ # Pytest suite (~17k tests across ~900 files as of May 2026) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 4bbc3c67c70..9cbc26112f6 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -91,9 +91,6 @@ export VIRTUAL_ENV="$(pwd)/venv" # Install with all extras (messaging, cron, CLI menus, dev tools) uv pip install -e ".[all,dev]" -# Optional: RL training submodule -# git submodule update --init tinker-atropos && uv pip install -e "./tinker-atropos" - # Optional: browser tools npm install ``` @@ -196,7 +193,6 @@ hermes-agent/ │ ├── skills/ # Bundled skills (copied to ~/.hermes/skills/ on install) ├── optional-skills/ # Official optional skills (discoverable via hub, not activated by default) -├── environments/ # RL training environments (Atropos integration) ├── tests/ # Test suite ├── website/ # Documentation site (hermes-agent.nousresearch.com) │ diff --git a/README.md b/README.md index 7e71632c310..efe5515f4d8 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ Use any model you want — [Nous Portal](https://portal.nousresearch.com), [Open Scheduled automationsBuilt-in cron scheduler with delivery to any platform. Daily reports, nightly backups, weekly audits — all in natural language, running unattended. Delegates and parallelizesSpawn isolated subagents for parallel workstreams. Write Python scripts that call tools via RPC, collapsing multi-step pipelines into zero-context-cost turns. Runs anywhere, not just your laptopSeven terminal backends — local, Docker, SSH, Singularity, Modal, Daytona, and Vercel Sandbox. Daytona and Modal offer serverless persistence — your agent's environment hibernates when idle and wakes on demand, costing nearly nothing between sessions. Run it on a $5 VPS or a GPU cluster. -Research-readyBatch trajectory generation, Atropos RL environments, trajectory compression for training the next generation of tool-calling models. +Research-readyBatch trajectory generation, trajectory compression for training the next generation of tool-calling models. --- @@ -175,8 +175,6 @@ uv pip install -e ".[all,dev]" scripts/run_tests.sh ``` -> **RL Training (optional):** The RL/Atropos integration (`environments/`) — see [`CONTRIBUTING.md`](https://github.com/NousResearch/hermes-agent/blob/main/CONTRIBUTING.md#development-setup) for the full setup. - --- ## Community diff --git a/README.zh-CN.md b/README.zh-CN.md index ea7fea8dcce..9a964574413 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -23,7 +23,7 @@ 定时自动化内置 cron 调度器，支持向任何平台投递。日报、夜间备份、周审计——全部用自然语言描述，无人值守运行。 委派与并行生成隔离子代理处理并行工作流。编写 Python 脚本通过 RPC 调用工具，将多步管道压缩为零上下文开销的轮次。 随处运行六种终端后端——本地、Docker、SSH、Daytona、Singularity 和 Modal。Daytona 和 Modal 提供 Serverless 持久化——代理环境空闲时休眠、按需唤醒，空闲期间几乎零成本。$5 VPS 或 GPU 集群都能跑。 -研究就绪批量轨迹生成、Atropos RL 环境、轨迹压缩——用于训练下一代工具调用模型。 +研究就绪批量轨迹生成、轨迹压缩——用于训练下一代工具调用模型。 --- @@ -161,12 +161,6 @@ uv pip install -e ".[all,dev]" python -m pytest tests/ -q ``` -> **RL 训练（可选）：** 如需参与 RL/Tinker-Atropos 集成开发： -> ```bash -> git submodule update --init tinker-atropos -> uv pip install -e "./tinker-atropos" -> ``` - --- ## 社区 diff --git a/agent/display.py b/agent/display.py index 6c5c970aeff..cdfc88f46a3 100644 --- a/agent/display.py +++ b/agent/display.py @@ -240,21 +240,6 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int | None = None) - msg = msg[:17] + "..." return f"to {target}: \"{msg}\"" - if tool_name.startswith("rl_"): - rl_previews = { - "rl_list_environments": "listing envs", - "rl_select_environment": args.get("name", ""), - "rl_get_current_config": "reading config", - "rl_edit_config": f"{args.get('field', '')}={args.get('value', '')}", - "rl_start_training": "starting", - "rl_check_status": args.get("run_id", "")[:16], - "rl_stop_training": f"stopping {args.get('run_id', '')[:16]}", - "rl_get_results": args.get("run_id", "")[:16], - "rl_list_runs": "listing runs", - "rl_test_inference": f"{args.get('num_steps', 3)} steps", - } - return rl_previews.get(tool_name) - key = primary_args.get(tool_name) if not key: for fallback_key in ("query", "text", "command", "path", "name", "prompt", "code", "goal"): @@ -981,15 +966,6 @@ def get_cute_tool_message( if action == "list": return _wrap(f"┊ ⏰ cron listing {dur}") return _wrap(f"┊ ⏰ cron {action} {args.get('job_id', '')} {dur}") - if tool_name.startswith("rl_"): - rl = { - "rl_list_environments": "list envs", "rl_select_environment": f"select {args.get('name', '')}", - "rl_get_current_config": "get config", "rl_edit_config": f"set {args.get('field', '?')}", - "rl_start_training": "start training", "rl_check_status": f"status {args.get('run_id', '?')[:12]}", - "rl_stop_training": f"stop {args.get('run_id', '?')[:12]}", "rl_get_results": f"results {args.get('run_id', '?')[:12]}", - "rl_list_runs": "list runs", "rl_test_inference": "test inference", - } - return _wrap(f"┊ 🧪 rl {rl.get(tool_name, tool_name.replace('rl_', ''))} {dur}") if tool_name == "execute_code": code = args.get("code", "") first_line = code.strip().split("\n")[0] if code.strip() else "" diff --git a/cli-config.yaml.example b/cli-config.yaml.example index 3f98b8868ec..f5fb7156380 100644 --- a/cli-config.yaml.example +++ b/cli-config.yaml.example @@ -457,7 +457,7 @@ prompt_caching: # Two stores: MEMORY.md (agent's notes) and USER.md (user profile). # Character limits keep the memory small and focused. The agent manages # pruning -- when at the limit, it must consolidate or replace entries. -# Disabled by default in batch_runner and RL environments. +# Disabled by default in batch_runner. # memory: # Agent's personal notes: environment facts, conventions, things learned @@ -715,10 +715,9 @@ platform_toolsets: # todo - todo (in-memory task planning, no deps) # tts - text_to_speech (Edge TTS free, or ELEVENLABS/OPENAI/MINIMAX/MISTRAL key) # cronjob - cronjob (create/list/update/pause/resume/run/remove scheduled tasks) -# rl - rl_list_environments, rl_start_training, etc. (requires TINKER_API_KEY) # # PRESETS (curated bundles): -# hermes-cli - All of the above except rl + send_message +# hermes-cli - All of the above except send_message # hermes-telegram - terminal, file, web, vision, image_gen, tts, browser, # skills, todo, cronjob, send_message # hermes-discord - Same as hermes-telegram @@ -744,7 +743,6 @@ platform_toolsets: # session_search - Search and recall past conversations (FTS5 + Gemini Flash summarization) # tts - Text-to-speech (Edge TTS free, ElevenLabs, OpenAI, MiniMax, Mistral) # cronjob - Schedule and manage automated tasks (CLI-only) -# rl - RL training tools (Tinker-Atropos) # # Composite toolsets: # debugging - terminal + web + file (for troubleshooting) diff --git a/environments/README.md b/environments/README.md deleted file mode 100644 index 3936e1f35bc..00000000000 --- a/environments/README.md +++ /dev/null @@ -1,324 +0,0 @@ -# Hermes-Agent Atropos Environments - -This directory contains the integration layer between **hermes-agent's** tool-calling capabilities and the **Atropos** RL training framework. It provides everything needed to run agentic LLMs through multi-turn tool-calling loops, score their output with arbitrary reward functions, and feed results into Atropos for training or evaluation. - -## Architecture Overview - -``` - Atropos Framework - ┌───────────────────────┐ - │ BaseEnv │ (atroposlib) - │ - Server management │ - │ - Worker scheduling │ - │ - Wandb logging │ - │ - CLI (serve/process/ │ - │ evaluate) │ - └───────────┬───────────┘ - │ inherits - ┌───────────┴───────────┐ - │ HermesAgentBaseEnv │ hermes_base_env.py - │ - Terminal backend │ - │ - Tool resolution │ - │ - Agent loop │ - │ - ToolContext │ - │ - Async patches │ - └───────────┬───────────┘ - │ inherits - ┌─────────────────┼─────────────────┐ - │ │ │ - TerminalTestEnv HermesSweEnv TerminalBench2EvalEnv - (stack testing) (SWE training) (TB2 benchmark eval) -``` - -### Inheritance Chain - -**BaseEnv** (from `atroposlib`) is the Atropos base class. It provides: -- Server management (OpenAI-compatible API servers, VLLM, SGLang) -- Worker scheduling for parallel rollouts -- Wandb integration for metrics and rollout logging -- CLI interface with three subcommands: `serve`, `process`, `evaluate` -- `evaluate_log()` for saving eval results to JSON + samples.jsonl - -**HermesAgentBaseEnv** (`hermes_base_env.py`) extends BaseEnv with hermes-agent specifics: -- Sets `os.environ["TERMINAL_ENV"]` to configure the terminal backend (local, docker, ssh, singularity, modal, daytona, vercel_sandbox) -- Resolves hermes-agent toolsets via `_resolve_tools_for_group()` (calls `get_tool_definitions()` which queries `tools/registry.py`) -- Implements `collect_trajectory()` which runs the full agent loop and computes rewards -- Supports two-phase operation (Phase 1: OpenAI server, Phase 2: VLLM ManagedServer) -- Applies monkey patches for async-safe tool operation at import time - -Concrete environments inherit from `HermesAgentBaseEnv` and implement: -- `setup()` -- Load dataset, initialize state -- `get_next_item()` -- Return the next item for rollout -- `format_prompt()` -- Convert a dataset item into the user message -- `compute_reward()` -- Score the rollout using ToolContext -- `evaluate()` -- Periodic evaluation logic - -## Core Components - -### Agent Loop (`agent_loop.py`) - -`HermesAgentLoop` is the reusable multi-turn agent engine. It runs the same pattern as hermes-agent's `run_agent.py`: - -1. Send messages + tools to the API via `server.chat_completion()` -2. If the response contains `tool_calls`, execute each one via `handle_function_call()` (which delegates to `tools/registry.py`'s `dispatch()`) -3. Append tool results to the conversation and go back to step 1 -4. If the response has no tool_calls, the agent is done - -Tool calls are executed in a thread pool (`run_in_executor`) so backends that use `asyncio.run()` internally (Modal, Docker) don't deadlock inside Atropos's event loop. - -Returns an `AgentResult` containing the full conversation history, turn count, reasoning content per turn, tool errors, and optional ManagedServer state (for Phase 2). - -### Tool Context (`tool_context.py`) - -`ToolContext` is a per-rollout handle that gives reward/verification functions direct access to **all** hermes-agent tools, scoped to the rollout's `task_id`. The same `task_id` means the terminal/browser session is the SAME one the model used during its rollout -- all state (files, processes, browser tabs) is preserved. - -```python -async def compute_reward(self, item, result, ctx: ToolContext): - # Run tests in the model's terminal sandbox - test = ctx.terminal("pytest -v") - if test["exit_code"] == 0: - return 1.0 - - # Check if a file was created - content = ctx.read_file("/workspace/solution.py") - if content.get("content"): - return 0.5 - - # Download files locally for verification (binary-safe) - ctx.download_file("/remote/output.bin", "/local/output.bin") - - return 0.0 -``` - -Available methods: -- **Terminal**: `terminal(command, timeout)` -- run shell commands -- **Files**: `read_file(path)`, `write_file(path, content)`, `search(query, path)` -- **Transfers**: `upload_file()`, `upload_dir()`, `download_file()`, `download_dir()` -- binary-safe file transfers between host and sandbox -- **Web**: `web_search(query)`, `web_extract(urls)` -- **Browser**: `browser_navigate(url)`, `browser_snapshot()` -- **Generic**: `call_tool(name, args)` -- call any hermes-agent tool by name -- **Cleanup**: `cleanup()` -- release all resources (called automatically after `compute_reward`) - -### Patches (`patches.py`) - -**Problem**: Some hermes-agent tools use `asyncio.run()` internally (e.g., the Modal backend). This crashes when called from inside Atropos's event loop because `asyncio.run()` cannot be nested. - -**Solution**: `ModalEnvironment` uses a dedicated `_AsyncWorker` background thread with its own event loop. The calling code sees a sync interface, but internally all async Modal SDK calls happen on the worker thread so they don't conflict with Atropos's loop. This is built directly into `tools/environments/modal.py` — no monkey-patching required. - -`patches.py` is now a no-op (kept for backward compatibility with imports). - -### Tool Call Parsers (`tool_call_parsers/`) - -Client-side parsers that extract structured `tool_calls` from raw model output text. Used in **Phase 2** (VLLM server type) where ManagedServer's `/generate` endpoint returns raw text without tool call parsing. - -Each parser is a standalone reimplementation of the corresponding VLLM parser's `extract_tool_calls()` logic. No VLLM dependency -- only standard library (`re`, `json`, `uuid`) and `openai` types. - -Available parsers: -- `hermes` -- Hermes/ChatML `` XML format -- `mistral` -- Mistral `[TOOL_CALLS]` format -- `llama3_json` -- Llama 3 JSON tool calling -- `qwen` -- Qwen tool calling format -- `qwen3_coder` -- Qwen3 Coder format -- `deepseek_v3` -- DeepSeek V3 format -- `deepseek_v3_1` -- DeepSeek V3.1 format -- `kimi_k2` -- Kimi K2 format -- `longcat` -- Longcat format -- `glm45` / `glm47` -- GLM model formats - -Usage: -```python -from environments.tool_call_parsers import get_parser - -parser = get_parser("hermes") -content, tool_calls = parser.parse(raw_model_output) -``` - -In Phase 1 (OpenAI server type), these parsers are not needed -- the server handles tool call parsing natively. - -## Two-Phase Operation - -### Phase 1: OpenAI Server (Evaluation / SFT Data Generation) - -Uses `server.chat_completion()` with `tools=` parameter. The server (VLLM, SGLang, OpenRouter, OpenAI) handles tool call parsing natively. Returns `ChatCompletion` objects with structured `tool_calls`. - -- Good for: evaluation, SFT data generation, testing -- Run with: `serve` (with `run-api`), `process`, or `evaluate` subcommands -- Placeholder tokens are created for the Atropos pipeline - -### Phase 2: VLLM ManagedServer (Full RL Training) - -Uses ManagedServer for exact token IDs + logprobs via `/generate`. Client-side tool call parser (from `tool_call_parsers/`) reconstructs structured `tool_calls` from raw output. - -- Good for: full RL training with GRPO/PPO -- Run with: `serve` subcommand -- Real tokens, masks, and logprobs flow through the pipeline - -## Directory Structure - -``` -environments/ -├── README.md # This file -├── __init__.py # Package exports -├── hermes_base_env.py # Abstract base (HermesAgentBaseEnv) -├── agent_loop.py # Multi-turn agent engine (HermesAgentLoop) -├── tool_context.py # Per-rollout tool access for reward functions -├── patches.py # Async-safety patches for Modal backend -│ -├── tool_call_parsers/ # Phase 2 client-side parsers -│ ├── __init__.py # Registry + base class -│ ├── hermes_parser.py -│ ├── mistral_parser.py -│ ├── llama_parser.py -│ ├── qwen_parser.py -│ ├── qwen3_coder_parser.py -│ ├── deepseek_v3_parser.py -│ ├── deepseek_v3_1_parser.py -│ ├── kimi_k2_parser.py -│ ├── longcat_parser.py -│ ├── glm45_parser.py -│ └── glm47_parser.py -│ -├── terminal_test_env/ # Stack validation environment -│ └── terminal_test_env.py -│ -├── hermes_swe_env/ # SWE-bench style training environment -│ └── hermes_swe_env.py -│ -└── benchmarks/ # Evaluation benchmarks - ├── terminalbench_2/ # 89 terminal tasks, Modal sandboxes - │ └── terminalbench2_env.py - ├── tblite/ # 100 calibrated tasks (fast TB2 proxy) - │ └── tblite_env.py - └── yc_bench/ # Long-horizon strategic benchmark - └── yc_bench_env.py -``` - -## Concrete Environments - -### TerminalTestEnv (`terminal_test_env/`) - -A self-contained environment with inline tasks (no external dataset needed) for validating the full stack end-to-end. Each task asks the model to create a file at a known path, and the verifier checks the content matches. - -```bash -# Serve mode (needs run-api) -run-api -python environments/terminal_test_env/terminal_test_env.py serve - -# Process mode (no run-api, saves to JSONL) -python environments/terminal_test_env/terminal_test_env.py process \ - --env.data_path_to_save_groups terminal_test_output.jsonl -``` - -### HermesSweEnv (`hermes_swe_env/`) - -SWE-bench style training environment. The model gets a coding task, uses terminal + file + web tools to solve it, and the reward function runs tests in the same Modal sandbox. - -```bash -python environments/hermes_swe_env/hermes_swe_env.py serve \ - --openai.model_name YourModel \ - --env.dataset_name bigcode/humanevalpack \ - --env.terminal_backend modal -``` - -### TerminalBench2EvalEnv (`benchmarks/terminalbench_2/`) - -**Eval-only** environment for the Terminal-Bench 2.0 benchmark (89 tasks). Each task gets a pre-built Docker Hub image, a natural language instruction, and a test suite. The agent uses terminal + file tools to solve the task, then the test suite verifies correctness. - -Follows the standard Atropos eval pattern (like GPQA, MMLU, etc.): -- Run via `evaluate` subcommand (no `run-api` needed) -- `setup()` loads the dataset, `evaluate()` runs all tasks -- `rollout_and_score_eval()` handles per-task agent loop + test verification -- Downloads verifier output locally for reliable reward checking (Harbor pattern) - -```bash -# Run full benchmark -python environments/benchmarks/terminalbench_2/terminalbench2_env.py evaluate \ - --openai.model_name anthropic/claude-opus-4.6 - -# Run subset of tasks -python environments/benchmarks/terminalbench_2/terminalbench2_env.py evaluate \ - --openai.model_name anthropic/claude-opus-4.6 \ - --env.task_filter fix-git,git-multibranch - -# Skip specific tasks -python environments/benchmarks/terminalbench_2/terminalbench2_env.py evaluate \ - --openai.model_name anthropic/claude-opus-4.6 \ - --env.skip_tasks heavy-task,slow-task -``` - -## Creating a New Environment - -### Training Environment - -1. Create a new directory under `environments/` -2. Create your env file inheriting from `HermesAgentBaseEnv` -3. Implement the four abstract methods + `evaluate()` - -```python -from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig - -class MyEnvConfig(HermesAgentEnvConfig): - pass # Add custom fields as needed - -class MyEnv(HermesAgentBaseEnv): - name = "my-env" - env_config_cls = MyEnvConfig - - @classmethod - def config_init(cls): - env_config = MyEnvConfig( - enabled_toolsets=["terminal", "file"], - terminal_backend="modal", - # ... other config - ) - server_configs = [APIServerConfig(...)] - return env_config, server_configs - - async def setup(self): - self.dataset = load_dataset(...) - self.iter = 0 - - async def get_next_item(self): - item = self.dataset[self.iter % len(self.dataset)] - self.iter += 1 - return item - - def format_prompt(self, item): - return item["instruction"] - - async def compute_reward(self, item, result, ctx): - # ctx gives you full tool access to the rollout's sandbox - test = ctx.terminal("pytest -v") - return 1.0 if test["exit_code"] == 0 else 0.0 - - async def evaluate(self, *args, **kwargs): - # Periodic evaluation logic - ... - -if __name__ == "__main__": - MyEnv.cli() -``` - -### Eval-Only Environment (Benchmark) - -For eval benchmarks, follow the pattern in `terminalbench2_env.py`: -1. Create under `environments/benchmarks/your-benchmark/` -2. Inherit from `HermesAgentBaseEnv` -3. Set eval-only config: `eval_handling=STOP_TRAIN`, `steps_per_eval=1`, `total_steps=1` -4. Stub the training methods (`collect_trajectories`, `score`) -5. Implement `rollout_and_score_eval()` and `evaluate()` -6. Run with `evaluate` subcommand - -## Key Config Fields - -| Field | Description | Default | -|-------|-------------|---------| -| `enabled_toolsets` | Which hermes toolsets to enable | `None` (all) | -| `disabled_toolsets` | Toolsets to disable | `None` | -| `distribution` | Probabilistic toolset distribution name | `None` | -| `max_agent_turns` | Max LLM calls per rollout | `30` | -| `agent_temperature` | Sampling temperature | `1.0` | -| `terminal_backend` | `local`, `docker`, `modal`, `daytona`, `ssh`, `singularity` | `local` | -| `system_prompt` | System message for the agent | `None` | -| `tool_call_parser` | Parser name for Phase 2 | `hermes` | -| `eval_handling` | `STOP_TRAIN`, `LIMIT_TRAIN`, `NONE` | `STOP_TRAIN` | diff --git a/environments/__init__.py b/environments/__init__.py deleted file mode 100644 index 282bc06b0b3..00000000000 --- a/environments/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -Hermes-Agent Atropos Environments - -Provides a layered integration between hermes-agent's tool-calling capabilities -and the Atropos RL training framework. - -Core layers: - - agent_loop: Reusable multi-turn agent loop with standard OpenAI-spec tool calling - - tool_context: Per-rollout tool access handle for reward/verification functions - - hermes_base_env: Abstract base environment (BaseEnv subclass) for Atropos - - tool_call_parsers: Client-side tool call parser registry for Phase 2 (VLLM /generate) - -Concrete environments: - - terminal_test_env/: Simple file-creation tasks for testing the stack - - hermes_swe_env/: SWE-bench style tasks with Modal sandboxes - -Benchmarks (eval-only): - - benchmarks/terminalbench_2/: Terminal-Bench 2.0 evaluation -""" - -try: - from environments.agent_loop import AgentResult, HermesAgentLoop - from environments.tool_context import ToolContext - from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig -except ImportError: - # atroposlib not installed — environments are unavailable but - # submodules like tool_call_parsers can still be imported directly. - pass - -__all__ = [ - "AgentResult", - "HermesAgentLoop", - "ToolContext", - "HermesAgentBaseEnv", - "HermesAgentEnvConfig", -] diff --git a/environments/agent_loop.py b/environments/agent_loop.py deleted file mode 100644 index 7ca3a0f6ddb..00000000000 --- a/environments/agent_loop.py +++ /dev/null @@ -1,534 +0,0 @@ -""" -HermesAgentLoop -- Reusable Multi-Turn Agent Engine - -Runs the hermes-agent tool-calling loop using standard OpenAI-spec tool calling. -Works with any server that returns ChatCompletion objects with tool_calls: - - Phase 1: OpenAI server type (VLLM, SGLang, OpenRouter, OpenAI API) - - Phase 2: ManagedServer with client-side tool call parser - -The loop passes tools= and checks response.choices[0].message.tool_calls, -identical to hermes-agent's run_agent.py. Tool execution is dispatched via -handle_function_call() from model_tools.py. -""" - -import asyncio -import concurrent.futures -import json -import logging -import os -import uuid -from dataclasses import dataclass, field -from typing import Any, Dict, List, Optional, Set - -from model_tools import handle_function_call -from tools.terminal_tool import get_active_env -from tools.tool_result_storage import maybe_persist_tool_result, enforce_turn_budget - -# Thread pool for running sync tool calls that internally use asyncio.run() -# (e.g., the Modal/Docker/Daytona terminal backends). Running them in a separate -# thread gives them a clean event loop so they don't deadlock inside Atropos's loop. -# Size must be large enough for concurrent eval tasks (e.g., 89 TB2 tasks all -# making tool calls). Too small = thread pool starvation, tasks queue for minutes. -# Resized at runtime by HermesAgentBaseEnv.__init__ via resize_tool_pool(). -_tool_executor = concurrent.futures.ThreadPoolExecutor(max_workers=128) - - -def resize_tool_pool(max_workers: int): - """ - Replace the global tool executor with a new one of the given size. - - Called by HermesAgentBaseEnv.__init__ based on config.tool_pool_size. - Safe to call before any tasks are submitted. - """ - global _tool_executor - old_executor = _tool_executor - _tool_executor = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) - old_executor.shutdown(wait=False) - logger.info("Tool thread pool resized to %d workers", max_workers) - -logger = logging.getLogger(__name__) - - -@dataclass -class ToolError: - """Record of a tool execution error during the agent loop.""" - - turn: int # Which turn the error occurred on - tool_name: str # Which tool was called - arguments: str # The arguments passed (truncated) - error: str # The error message - tool_result: str # The raw result returned to the model - - -@dataclass -class AgentResult: - """Result of running the agent loop.""" - - # Full conversation history in OpenAI message format - messages: List[Dict[str, Any]] - # ManagedServer.get_state() if available (Phase 2), None otherwise - managed_state: Optional[Dict[str, Any]] = None - # How many LLM calls were made - turns_used: int = 0 - # True if model stopped calling tools naturally (vs hitting max_turns) - finished_naturally: bool = False - # Extracted reasoning content per turn (from PR #297 helpers) - reasoning_per_turn: List[Optional[str]] = field(default_factory=list) - # Tool errors encountered during the loop - tool_errors: List[ToolError] = field(default_factory=list) - - -def _extract_reasoning_from_message(message) -> Optional[str]: - """ - Extract reasoning content from a ChatCompletion message. - - Handles multiple provider formats: - 1. message.reasoning_content field (some providers) - 2. message.reasoning field (some providers) - 3. message.reasoning_details[].text (OpenRouter style) - - Note: block extraction from content is NOT done here -- that's - handled by the response already in Phase 1 (server does it) or by - ManagedServer's patch in Phase 2. - - Args: - message: The assistant message from ChatCompletion response - - Returns: - Extracted reasoning text, or None if not found - """ - # Check reasoning_content field (common across providers) - if hasattr(message, "reasoning_content") and message.reasoning_content: - return message.reasoning_content - - # Check reasoning field - if hasattr(message, "reasoning") and message.reasoning: - return message.reasoning - - # Check reasoning_details (OpenRouter style) - if hasattr(message, "reasoning_details") and message.reasoning_details: - for detail in message.reasoning_details: - if hasattr(detail, "text") and detail.text: - return detail.text - if isinstance(detail, dict) and detail.get("text"): - return detail["text"] - - return None - - -class HermesAgentLoop: - """ - Runs hermes-agent's tool-calling loop using standard OpenAI-spec tool calling. - - Same pattern as run_agent.py: - - Pass tools= to the API - - Check response.choices[0].message.tool_calls - - Dispatch via handle_function_call() - - Works identically with any server type -- OpenAI, VLLM, SGLang, OpenRouter, - or ManagedServer with a parser. The server determines how tool_calls get - populated on the response. - """ - - def __init__( - self, - server, - tool_schemas: List[Dict[str, Any]], - valid_tool_names: Set[str], - max_turns: int = 30, - task_id: Optional[str] = None, - temperature: float = 1.0, - max_tokens: Optional[int] = None, - extra_body: Optional[Dict[str, Any]] = None, - budget_config: Optional["BudgetConfig"] = None, - ): - """ - Initialize the agent loop. - - Args: - server: Server object with chat_completion() method (OpenAIServer, - ManagedServer, ServerManager, etc.) - tool_schemas: OpenAI-format tool definitions from get_tool_definitions() - valid_tool_names: Set of tool names the model is allowed to call - max_turns: Maximum number of LLM calls before stopping - task_id: Unique ID for terminal/browser session isolation - temperature: Sampling temperature for generation - max_tokens: Max tokens per generation (None for server default) - extra_body: Extra parameters passed to the OpenAI client's create() call. - Used for OpenRouter provider preferences, transforms, etc. - e.g. {"provider": {"ignore": ["DeepInfra"]}} - budget_config: Tool result persistence budget. Controls per-tool - thresholds, per-turn aggregate budget, and preview size. - If None, uses DEFAULT_BUDGET (current hardcoded values). - """ - from tools.budget_config import DEFAULT_BUDGET - self.server = server - self.tool_schemas = tool_schemas - self.valid_tool_names = valid_tool_names - self.max_turns = max_turns - self.task_id = task_id or str(uuid.uuid4()) - self.temperature = temperature - self.max_tokens = max_tokens - self.extra_body = extra_body - self.budget_config = budget_config or DEFAULT_BUDGET - - async def run(self, messages: List[Dict[str, Any]]) -> AgentResult: - """ - Execute the full agent loop using standard OpenAI tool calling. - - Args: - messages: Initial conversation messages (system + user). - Modified in-place as the conversation progresses. - - Returns: - AgentResult with full conversation history, managed state, and metadata - """ - reasoning_per_turn = [] - tool_errors: List[ToolError] = [] - - # Per-loop TodoStore for the todo tool (ephemeral, dies with the loop) - from tools.todo_tool import TodoStore, todo_tool as _todo_tool - _todo_store = TodoStore() - - # Extract user task from first user message for browser_snapshot context - _user_task = None - for msg in messages: - if msg.get("role") == "user": - content = msg.get("content", "") - if isinstance(content, str) and content.strip(): - _user_task = content.strip()[:500] # Cap to avoid huge strings - break - - import time as _time - - for turn in range(self.max_turns): - turn_start = _time.monotonic() - - # Build the chat_completion kwargs - chat_kwargs = { - "messages": messages, - "n": 1, - "temperature": self.temperature, - } - - # Only pass tools if we have them - if self.tool_schemas: - chat_kwargs["tools"] = self.tool_schemas - - # Only pass max_tokens if explicitly set - if self.max_tokens is not None: - chat_kwargs["max_tokens"] = self.max_tokens - - # Inject extra_body for provider-specific params (e.g., OpenRouter - # provider preferences like banned/preferred providers, transforms) - if self.extra_body: - chat_kwargs["extra_body"] = self.extra_body - - # Make the API call -- standard OpenAI spec - api_start = _time.monotonic() - try: - response = await self.server.chat_completion(**chat_kwargs) - except Exception as e: - api_elapsed = _time.monotonic() - api_start - logger.error("API call failed on turn %d (%.1fs): %s", turn + 1, api_elapsed, e) - return AgentResult( - messages=messages, - managed_state=self._get_managed_state(), - turns_used=turn + 1, - finished_naturally=False, - reasoning_per_turn=reasoning_per_turn, - tool_errors=tool_errors, - ) - - api_elapsed = _time.monotonic() - api_start - - if not response or not response.choices: - logger.warning("Empty response on turn %d (api=%.1fs)", turn + 1, api_elapsed) - return AgentResult( - messages=messages, - managed_state=self._get_managed_state(), - turns_used=turn + 1, - finished_naturally=False, - reasoning_per_turn=reasoning_per_turn, - tool_errors=tool_errors, - ) - - assistant_msg = response.choices[0].message - - # Extract reasoning content from the response (all provider formats) - reasoning = _extract_reasoning_from_message(assistant_msg) - reasoning_per_turn.append(reasoning) - - # Check for tool calls -- standard OpenAI spec. - # Fallback: if response has no structured tool_calls but content - # contains raw tool call tags (e.g. ), parse them using - # hermes-agent's standalone parsers. This handles the case where - # ManagedServer's ToolCallTranslator couldn't parse because vLLM - # isn't installed. - if ( - not assistant_msg.tool_calls - and assistant_msg.content - and self.tool_schemas - and "" in (assistant_msg.content or "") - ): - try: - from environments.tool_call_parsers import get_parser - fallback_parser = get_parser("hermes") - parsed_content, parsed_calls = fallback_parser.parse( - assistant_msg.content - ) - if parsed_calls: - assistant_msg.tool_calls = parsed_calls - if parsed_content is not None: - assistant_msg.content = parsed_content - logger.debug( - "Fallback parser extracted %d tool calls from raw content", - len(parsed_calls), - ) - except Exception: - pass # Fall through to no tool calls - - if assistant_msg.tool_calls: - # Normalize tool calls to dicts — they may come as objects - # (OpenAI API) or dicts (vLLM ToolCallTranslator). - def _tc_to_dict(tc): - if isinstance(tc, dict): - return { - "id": tc.get("id", f"call_{uuid.uuid4().hex[:8]}"), - "type": "function", - "function": { - "name": tc.get("function", {}).get("name", tc.get("name", "")), - "arguments": tc.get("function", {}).get("arguments", tc.get("arguments", "{}")), - }, - } - return { - "id": tc.id, - "type": "function", - "function": { - "name": tc.function.name, - "arguments": tc.function.arguments, - }, - } - - # Build the assistant message dict for conversation history - msg_dict: Dict[str, Any] = { - "role": "assistant", - "content": assistant_msg.content or "", - "tool_calls": [_tc_to_dict(tc) for tc in assistant_msg.tool_calls], - } - - # Preserve reasoning_content for multi-turn chat template handling - # (e.g., Kimi-K2's template renders blocks differently - # for history vs. the latest turn based on this field) - if reasoning: - msg_dict["reasoning_content"] = reasoning - - messages.append(msg_dict) - - # Execute each tool call via hermes-agent's dispatch - for tc in assistant_msg.tool_calls: - # Handle both object (OpenAI) and dict (vLLM) formats - if isinstance(tc, dict): - tool_name = tc.get("function", {}).get("name", tc.get("name", "")) - tool_args_raw = tc.get("function", {}).get("arguments", tc.get("arguments", "{}")) - else: - tool_name = tc.function.name - tool_args_raw = tc.function.arguments - - # Validate tool name - if tool_name not in self.valid_tool_names: - tool_result = json.dumps( - { - "error": f"Unknown tool '{tool_name}'. " - f"Available tools: {sorted(self.valid_tool_names)}" - } - ) - tool_errors.append(ToolError( - turn=turn + 1, tool_name=tool_name, - arguments=tool_args_raw[:200], - error=f"Unknown tool '{tool_name}'", - tool_result=tool_result, - )) - logger.warning( - "Model called unknown tool '%s' on turn %d", - tool_name, turn + 1, - ) - else: - # Parse arguments - try: - args = json.loads(tool_args_raw) - except json.JSONDecodeError as e: - args = None - tool_result = json.dumps( - {"error": f"Invalid JSON in tool arguments: {e}. Please retry with valid JSON."} - ) - tool_errors.append(ToolError( - turn=turn + 1, tool_name=tool_name, - arguments=tool_args_raw[:200], - error=f"Invalid JSON: {e}", - tool_result=tool_result, - )) - logger.warning( - "Invalid JSON in tool call arguments for '%s': %s", - tool_name, tool_args_raw[:200], - ) - - # Dispatch tool only if arguments parsed successfully - if args is not None: - try: - if tool_name == "terminal": - backend = os.getenv("TERMINAL_ENV", "local") - cmd_preview = args.get("command", "")[:80] - logger.info( - "[%s] $ %s", self.task_id[:8], cmd_preview, - ) - - tool_submit_time = _time.monotonic() - - # Todo tool -- handle locally (needs per-loop TodoStore) - if tool_name == "todo": - tool_result = _todo_tool( - todos=args.get("todos"), - merge=args.get("merge", False), - store=_todo_store, - ) - tool_elapsed = _time.monotonic() - tool_submit_time - elif tool_name == "memory": - tool_result = json.dumps({"error": "Memory is not available in RL environments."}) - tool_elapsed = _time.monotonic() - tool_submit_time - elif tool_name == "session_search": - tool_result = json.dumps({"error": "Session search is not available in RL environments."}) - tool_elapsed = _time.monotonic() - tool_submit_time - else: - # Run tool calls in a thread pool so backends that - # use asyncio.run() internally (modal, docker, daytona) get - # a clean event loop instead of deadlocking. - loop = asyncio.get_running_loop() - # Capture current tool_name/args for the lambda - _tn, _ta, _tid = tool_name, args, self.task_id - tool_result = await loop.run_in_executor( - _tool_executor, - lambda: handle_function_call( - _tn, _ta, task_id=_tid, - user_task=_user_task, - ), - ) - tool_elapsed = _time.monotonic() - tool_submit_time - - # Log slow tools and thread pool stats for debugging - pool_active = _tool_executor._work_queue.qsize() - if tool_elapsed > 30: - logger.warning( - "[%s] turn %d: %s took %.1fs (pool queue=%d)", - self.task_id[:8], turn + 1, tool_name, - tool_elapsed, pool_active, - ) - except Exception as e: - tool_result = json.dumps( - {"error": f"Tool execution failed: {type(e).__name__}: {str(e)}"} - ) - tool_errors.append(ToolError( - turn=turn + 1, tool_name=tool_name, - arguments=tool_args_raw[:200], - error=f"{type(e).__name__}: {str(e)}", - tool_result=tool_result, - )) - logger.error( - "Tool '%s' execution failed on turn %d: %s", - tool_name, turn + 1, e, - ) - - # Also check if the tool returned an error in its JSON result - try: - result_data = json.loads(tool_result) - if isinstance(result_data, dict): - err = result_data.get("error") - exit_code = result_data.get("exit_code") - if err and exit_code and exit_code < 0: - tool_errors.append(ToolError( - turn=turn + 1, tool_name=tool_name, - arguments=tool_args_raw[:200], - error=str(err), - tool_result=tool_result[:500], - )) - except (json.JSONDecodeError, TypeError): - pass - - tc_id = tc.get("id", "") if isinstance(tc, dict) else tc.id - tool_result = maybe_persist_tool_result( - content=tool_result, - tool_name=tool_name, - tool_use_id=tc_id, - env=get_active_env(self.task_id), - config=self.budget_config, - ) - - messages.append( - { - "role": "tool", - "tool_call_id": tc_id, - "content": tool_result, - } - ) - - num_tcs = len(assistant_msg.tool_calls) - if num_tcs > 0: - enforce_turn_budget( - messages[-num_tcs:], - env=get_active_env(self.task_id), - config=self.budget_config, - ) - - turn_elapsed = _time.monotonic() - turn_start - logger.info( - "[%s] turn %d: api=%.1fs, %d tools, turn_total=%.1fs", - self.task_id[:8], turn + 1, api_elapsed, - len(assistant_msg.tool_calls), turn_elapsed, - ) - - else: - # No tool calls -- model is done - msg_dict = { - "role": "assistant", - "content": assistant_msg.content or "", - } - if reasoning: - msg_dict["reasoning_content"] = reasoning - messages.append(msg_dict) - - turn_elapsed = _time.monotonic() - turn_start - logger.info( - "[%s] turn %d: api=%.1fs, no tools (finished), turn_total=%.1fs", - self.task_id[:8], turn + 1, api_elapsed, turn_elapsed, - ) - - return AgentResult( - messages=messages, - managed_state=self._get_managed_state(), - turns_used=turn + 1, - finished_naturally=True, - reasoning_per_turn=reasoning_per_turn, - tool_errors=tool_errors, - ) - - # Hit max turns without the model stopping - logger.info("Agent hit max_turns (%d) without finishing", self.max_turns) - return AgentResult( - messages=messages, - managed_state=self._get_managed_state(), - turns_used=self.max_turns, - finished_naturally=False, - reasoning_per_turn=reasoning_per_turn, - tool_errors=tool_errors, - ) - - def _get_managed_state(self) -> Optional[Dict[str, Any]]: - """ - Get ManagedServer state if the server supports it. - - Returns state dict with SequenceNodes containing tokens/logprobs/masks, - or None if the server doesn't support get_state() (e.g., regular OpenAI server). - """ - if hasattr(self.server, "get_state"): - return self.server.get_state() - return None diff --git a/environments/agentic_opd_env.py b/environments/agentic_opd_env.py deleted file mode 100644 index c6ed88756bf..00000000000 --- a/environments/agentic_opd_env.py +++ /dev/null @@ -1,1214 +0,0 @@ -""" -AgenticOPDEnv — On-Policy Distillation for Agentic Tool-Calling Tasks -===================================================================== - -First Atropos environment to populate the distill_token_ids / distill_logprobs -fields on ScoredDataGroup, enabling on-policy distillation (OPD) training. - -Key idea (from OpenClaw-RL, Princeton 2026): - Every time an agent receives a next-state signal (tool result, error trace, - test verdict), that signal contains hindsight information about how the - agent's PREVIOUS response could have been better. This environment: - - 1. Runs standard agentic rollouts (tool-calling agent loop) - 2. Walks the conversation to find (assistant_turn, next_state) pairs - 3. Uses an LLM judge to extract "hints" from next-state signals - 4. Builds an enhanced prompt (original context + hint) - 5. Scores the student's response tokens under the enhanced distribution - using VLLM's prompt_logprobs (via Atropos's get_logprobs API) - 6. Packages the teacher's top-K predictions as distill_token_ids / - distill_logprobs on the ScoredDataGroup - -The trainer then computes per-token advantages: - A_t = teacher_logprob(token_t) - student_logprob(token_t) - Positive → teacher approves this token (upweight) - Negative → teacher disapproves (downweight) - -This gives dense, token-level training signal from every tool interaction, -instead of just a scalar reward at the end of the trajectory. - -Task: Coding tasks with test verification (rich next-state signals from -test results, error messages, terminal output). Falls back to built-in -coding problems if no HuggingFace dataset is configured. - -Requirements: - - VLLM backend (server_type: vllm) — needed for prompt logprob scoring - - Phase 2 mode (ManagedServer) — needed for token-level tracking - -Usage: - # Process mode (offline data generation with OPD) - python environments/agentic_opd_env.py process \\ - --env.total_steps 10 --env.group_size 2 \\ - --env.data_path_to_save_groups output.jsonl \\ - --openai.base_url http://localhost:8000/v1 \\ - --openai.model_name Qwen/Qwen3-4B - - # Serve mode (connected to Atropos trainer) - python environments/agentic_opd_env.py serve \\ - --openai.base_url http://localhost:8000/v1 \\ - --openai.model_name Qwen/Qwen3-4B - - # Evaluate mode - python environments/agentic_opd_env.py evaluate \\ - --env.eval_size 10 \\ - --openai.base_url http://localhost:8000/v1 \\ - --openai.model_name Qwen/Qwen3-4B - -Reference: Wang et al., "OpenClaw-RL: Train Any Agent Simply by Talking" - arXiv:2603.10165, March 2026 -""" - -from __future__ import annotations - -import asyncio -import copy -import json -import logging -import os -import random -import re -import sys -import time -import uuid -from pathlib import Path -from typing import Any, Dict, List, Optional, Set, Tuple, Union - -from pydantic import Field - -# Ensure hermes-agent root is on path -_repo_root = Path(__file__).resolve().parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -from atroposlib.envs.base import ScoredDataGroup, ScoredDataItem -from atroposlib.envs.server_handling.server_manager import APIServerConfig -from atroposlib.type_definitions import Item - -from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig -from environments.agent_loop import AgentResult, HermesAgentLoop -from environments.tool_context import ToolContext - -logger = logging.getLogger(__name__) - - -# ═══════════════════════════════════════════════════════════════════════ -# Built-in coding tasks (fallback when no HF dataset is configured) -# ═══════════════════════════════════════════════════════════════════════ - -BUILTIN_CODING_TASKS = [ - { - "task": "Write a Python function `fizzbuzz(n)` that returns a list of strings from 1 to n. " - "For multiples of 3 return 'Fizz', for multiples of 5 return 'Buzz', " - "for multiples of both return 'FizzBuzz', otherwise the number as a string.", - "test_code": ( - "from solution import fizzbuzz\n" - "assert fizzbuzz(15) == ['1','2','Fizz','4','Buzz','Fizz','7','8','Fizz','Buzz','11','Fizz','13','14','FizzBuzz']\n" - "assert fizzbuzz(1) == ['1']\n" - "assert fizzbuzz(0) == []\n" - "print('All tests passed!')\n" - ), - "difficulty": "easy", - }, - { - "task": "Write a Python function `is_palindrome(s)` that checks if a string is a palindrome, " - "ignoring case and non-alphanumeric characters. Return True or False.", - "test_code": ( - "from solution import is_palindrome\n" - "assert is_palindrome('A man, a plan, a canal: Panama') == True\n" - "assert is_palindrome('race a car') == False\n" - "assert is_palindrome('') == True\n" - "assert is_palindrome('Was it a car or a cat I saw?') == True\n" - "print('All tests passed!')\n" - ), - "difficulty": "easy", - }, - { - "task": "Write a Python function `two_sum(nums, target)` that returns the indices of the two " - "numbers in `nums` that add up to `target`. Assume exactly one solution exists. " - "Return a list of two indices [i, j] where i < j.", - "test_code": ( - "from solution import two_sum\n" - "assert two_sum([2, 7, 11, 15], 9) == [0, 1]\n" - "assert two_sum([3, 2, 4], 6) == [1, 2]\n" - "assert two_sum([3, 3], 6) == [0, 1]\n" - "print('All tests passed!')\n" - ), - "difficulty": "easy", - }, - { - "task": "Write a Python function `flatten(lst)` that takes an arbitrarily nested list and " - "returns a flat list of all elements. For example, flatten([1, [2, [3, 4], 5]]) " - "should return [1, 2, 3, 4, 5].", - "test_code": ( - "from solution import flatten\n" - "assert flatten([1, [2, [3, 4], 5]]) == [1, 2, 3, 4, 5]\n" - "assert flatten([]) == []\n" - "assert flatten([1, 2, 3]) == [1, 2, 3]\n" - "assert flatten([[[[1]]]]) == [1]\n" - "assert flatten([1, [2], [[3]], [[[4]]]]) == [1, 2, 3, 4]\n" - "print('All tests passed!')\n" - ), - "difficulty": "medium", - }, - { - "task": "Write a Python function `longest_common_prefix(strs)` that finds the longest " - "common prefix string amongst a list of strings. If there is no common prefix, " - "return an empty string.", - "test_code": ( - "from solution import longest_common_prefix\n" - "assert longest_common_prefix(['flower', 'flow', 'flight']) == 'fl'\n" - "assert longest_common_prefix(['dog', 'racecar', 'car']) == ''\n" - "assert longest_common_prefix(['interspecies', 'interstellar', 'interstate']) == 'inters'\n" - "assert longest_common_prefix(['a']) == 'a'\n" - "assert longest_common_prefix([]) == ''\n" - "print('All tests passed!')\n" - ), - "difficulty": "easy", - }, - { - "task": "Write a Python function `group_anagrams(strs)` that groups anagrams together. " - "Return a list of lists, where each inner list contains strings that are anagrams of " - "each other. The order of groups and strings within groups does not matter.", - "test_code": ( - "from solution import group_anagrams\n" - "result = group_anagrams(['eat', 'tea', 'tan', 'ate', 'nat', 'bat'])\n" - "result_sorted = sorted([sorted(g) for g in result])\n" - "assert result_sorted == [['ate', 'eat', 'tea'], ['bat'], ['nat', 'tan']]\n" - "assert group_anagrams([]) == []\n" - "assert group_anagrams(['a']) == [['a']]\n" - "print('All tests passed!')\n" - ), - "difficulty": "medium", - }, - { - "task": "Write a Python function `valid_parentheses(s)` that determines if a string " - "containing just '(', ')', '{', '}', '[' and ']' is valid. A string is valid if " - "open brackets are closed by the same type and in the correct order.", - "test_code": ( - "from solution import valid_parentheses\n" - "assert valid_parentheses('()') == True\n" - "assert valid_parentheses('()[]{}') == True\n" - "assert valid_parentheses('(]') == False\n" - "assert valid_parentheses('([)]') == False\n" - "assert valid_parentheses('{[]}') == True\n" - "assert valid_parentheses('') == True\n" - "print('All tests passed!')\n" - ), - "difficulty": "easy", - }, - { - "task": "Write a Python function `merge_intervals(intervals)` that merges overlapping " - "intervals. Each interval is a list [start, end]. Return the merged intervals sorted " - "by start time.", - "test_code": ( - "from solution import merge_intervals\n" - "assert merge_intervals([[1,3],[2,6],[8,10],[15,18]]) == [[1,6],[8,10],[15,18]]\n" - "assert merge_intervals([[1,4],[4,5]]) == [[1,5]]\n" - "assert merge_intervals([[1,4],[0,4]]) == [[0,4]]\n" - "assert merge_intervals([]) == []\n" - "assert merge_intervals([[1,2]]) == [[1,2]]\n" - "print('All tests passed!')\n" - ), - "difficulty": "medium", - }, -] - - -# ═══════════════════════════════════════════════════════════════════════ -# Hint extraction prompts (adapted from OpenClaw-RL) -# ═══════════════════════════════════════════════════════════════════════ - -_HINT_JUDGE_SYSTEM = ( - "You are a process reward model used for hindsight hint extraction.\n" - "You are given:\n" - "1) The assistant response at turn t.\n" - "2) The next state at turn t+1, along with its **role**.\n\n" - "## Understanding the next state's role\n" - "- role='user': A reply from the user (follow-up, correction, new request, etc.).\n" - "- role='tool': The return value of a tool the assistant invoked. " - "This content was NOT available before the assistant's action — " - "it exists BECAUSE the assistant called the tool. " - "A successful, non-error tool output generally means the assistant's " - "action was appropriate; do NOT treat it as information the assistant " - "should have already known.\n\n" - "Your goal is to decide whether the next state reveals useful hindsight information\n" - "that could have helped improve the assistant response at turn t.\n\n" - "Output format rules (strict):\n" - "- You MUST include exactly one final decision token: \\boxed{1} or \\boxed{-1}.\n" - "- If and only if decision is \\boxed{1}, provide a concise, information-dense hint in 1-3 sentences,\n" - " wrapped between [HINT_START] and [HINT_END].\n" - "- If decision is \\boxed{-1}, do not provide a hint block.\n" - "- Hint must be concrete and actionable for improving the previous response." -) - -_BOXED_RE = re.compile(r"\\boxed\{(-?\d+)\}") -_HINT_RE = re.compile(r"\[HINT_START\](.*?)\[HINT_END\]", re.DOTALL) - - -def _build_hint_judge_messages( - response_text: str, next_state_text: str, next_state_role: str = "tool" -) -> list[dict]: - """Build messages for the hint extraction judge.""" - user = ( - f"## Assistant response (turn t)\n{response_text}\n\n" - f"## Next state (turn t+1) [role: {next_state_role}]\n{next_state_text}\n\n" - "Now output your decision and (if positive) the hint in the required format." - ) - return [ - {"role": "system", "content": _HINT_JUDGE_SYSTEM}, - {"role": "user", "content": user}, - ] - - -def _parse_hint_result(text: str) -> tuple[int | None, str]: - """Parse the judge's boxed decision and hint text.""" - boxed = _BOXED_RE.findall(text) - score = int(boxed[-1]) if boxed else None - if score not in {1, -1}: - score = None - hint_matches = _HINT_RE.findall(text) - hint = hint_matches[-1].strip() if hint_matches else "" - return score, hint - - -def _select_best_hint(votes: list[dict]) -> dict | None: - """Select the best hint from majority-voted judge results.""" - good = [ - v - for v in votes - if v.get("score") == 1 - and isinstance(v.get("hint"), str) - and len(v["hint"].strip()) > 10 - ] - if not good: - return None - return max(good, key=lambda v: len(v["hint"].strip())) - - -def _append_hint_to_messages(messages: list[dict], hint: str) -> list[dict]: - """Clone messages and append hint to the last user message.""" - cloned = copy.deepcopy(messages) - if not cloned: - return [{"role": "user", "content": f"[user's hint / instruction]\n{hint}"}] - - # Find last user message - target_idx = None - for i in range(len(cloned) - 1, -1, -1): - if cloned[i].get("role") == "user": - target_idx = i - break - if target_idx is None: - target_idx = len(cloned) - 1 - - content = cloned[target_idx].get("content", "") - if isinstance(content, list): - content = " ".join( - c.get("text", "") if isinstance(c, dict) else str(c) for c in content - ) - suffix = f"\n\n[user's hint / instruction]\n{hint.strip()}" - cloned[target_idx]["content"] = (content + suffix).strip() - return cloned - - -# ═══════════════════════════════════════════════════════════════════════ -# Configuration -# ═══════════════════════════════════════════════════════════════════════ - - -class AgenticOPDConfig(HermesAgentEnvConfig): - """Configuration for the agentic OPD environment.""" - - # --- OPD settings --- - opd_enabled: bool = Field( - default=True, - description="Enable on-policy distillation pipeline. When disabled, " - "the environment behaves like a standard agentic env (no distill fields).", - ) - distill_topk: int = Field( - default=50, - description="Number of top-K teacher logprobs per position for distillation.", - ) - prm_votes: int = Field( - default=3, - description="Number of independent judge queries for majority-voted hint extraction.", - ) - hint_max_next_state_chars: int = Field( - default=4000, - description="Maximum characters of next-state text to include in the hint judge prompt. " - "Tool results can be very long — truncating prevents judge context overflow.", - ) - - # --- Reward settings --- - correctness_weight: float = Field( - default=0.7, - description="Weight for test pass/fail in reward.", - ) - efficiency_weight: float = Field( - default=0.15, - description="Weight for efficiency (fewer turns = better).", - ) - tool_usage_weight: float = Field( - default=0.15, - description="Weight for appropriate tool usage signal.", - ) - - # --- Dataset --- - dataset_name: Optional[str] = Field( - default=None, - description="HuggingFace dataset with coding tasks. " - "Expected fields: 'task' (problem description) and 'test_code' (pytest/assert tests). " - "Falls back to built-in tasks if not set or unavailable.", - ) - - # --- Eval --- - eval_size: int = Field( - default=10, - description="Number of held-out items for evaluation.", - ) - eval_split_ratio: float = Field( - default=0.15, - description="Fraction of dataset to hold out for evaluation.", - ) - - -# ═══════════════════════════════════════════════════════════════════════ -# Environment -# ═══════════════════════════════════════════════════════════════════════ - - -class AgenticOPDEnv(HermesAgentBaseEnv): - """ - RL environment with on-policy distillation from next-state signals. - - Runs coding tasks where the agent writes code and runs tests. - Tool results (test pass/fail, error traces) serve as next-state signals - for hint extraction and teacher logprob scoring. - - This is the first Atropos environment to populate distill_token_ids - and distill_logprobs on ScoredDataGroup for OPD training. - """ - - name = "agentic-opd" - env_config_cls = AgenticOPDConfig - - # Default toolsets: terminal for running code, file for writing it - default_toolsets = ["terminal", "file"] - - @classmethod - def config_init(cls) -> Tuple[AgenticOPDConfig, List[APIServerConfig]]: - """Default configuration.""" - env_config = AgenticOPDConfig( - # Toolsets - enabled_toolsets=["terminal", "file"], - # Agent loop - max_agent_turns=15, - agent_temperature=1.0, - system_prompt=( - "You are a skilled Python programmer. When given a coding task:\n" - "1. Write the solution to a file called 'solution.py'\n" - "2. Write the test code to a file called 'test_solution.py'\n" - "3. Run the tests with: python test_solution.py\n" - "4. If tests fail, read the error output carefully, fix your code, and re-run\n" - "5. Once all tests pass, report success\n\n" - "Be efficient — write clean code and fix errors methodically." - ), - # OPD - opd_enabled=True, - distill_topk=50, - prm_votes=3, - # Training - group_size=4, - total_steps=500, - steps_per_eval=50, - use_wandb=True, - wandb_name="agentic-opd", - ) - - server_configs = [ - APIServerConfig( - base_url="http://localhost:8000/v1", - model_name="Qwen/Qwen3-4B", - server_type="vllm", - ) - ] - - return env_config, server_configs - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self._items: list[dict] = [] - self._eval_items: list[dict] = [] - self._index: int = 0 - - # Metric buffers - self._reward_buffer: list[float] = [] - self._correctness_buffer: list[float] = [] - self._efficiency_buffer: list[float] = [] - self._tool_usage_buffer: list[float] = [] - self._hints_extracted_buffer: list[int] = [] - self._opd_turns_scored_buffer: list[int] = [] - - # ═══════════════════════════════════════════════════════════════════ - # 1. setup — load dataset - # ═══════════════════════════════════════════════════════════════════ - - async def setup(self) -> None: - """Load coding tasks from HuggingFace or use built-in set.""" - if self.config.dataset_name: - try: - from datasets import load_dataset - - logger.info( - "Loading dataset '%s'...", self.config.dataset_name - ) - ds = load_dataset( - self.config.dataset_name, split=self.config.dataset_split - ) - task_field = self.config.prompt_field - self._items = [ - { - "task": row.get(task_field, row.get("task", "")), - "test_code": row.get("test_code", row.get("tests", "")), - "difficulty": row.get("difficulty", "unknown"), - } - for row in ds - if row.get(task_field, row.get("task", "")) - ] - if self._items: - random.shuffle(self._items) - eval_size = max( - self.config.eval_size, - int(len(self._items) * self.config.eval_split_ratio), - ) - self._eval_items = self._items[:eval_size] - self._items = self._items[eval_size:] - logger.info( - "Loaded %d train / %d eval items from '%s'", - len(self._items), - len(self._eval_items), - self.config.dataset_name, - ) - return - except Exception as e: - logger.warning( - "Could not load dataset '%s': %s. Using built-in tasks.", - self.config.dataset_name, - e, - ) - - # Fallback to built-in tasks - items = copy.deepcopy(BUILTIN_CODING_TASKS) - random.shuffle(items) - split = max(1, len(items) * 85 // 100) - self._items = items[:split] - self._eval_items = items[split:] - logger.info( - "Using built-in coding tasks: %d train / %d eval items", - len(self._items), - len(self._eval_items), - ) - - # ═══════════════════════════════════════════════════════════════════ - # 2. get_next_item - # ═══════════════════════════════════════════════════════════════════ - - async def get_next_item(self) -> dict: - """Return the next coding task, cycling through the dataset.""" - if not self._items: - raise RuntimeError("Dataset is empty. Did you call setup()?") - item = self._items[self._index % len(self._items)] - self._index += 1 - return item - - # ═══════════════════════════════════════════════════════════════════ - # 3. format_prompt - # ═══════════════════════════════════════════════════════════════════ - - def format_prompt(self, item: dict) -> str: - """Format the coding task as a user prompt.""" - prompt = ( - f"Solve the following coding task.\n\n" - f"## Task\n{item['task']}\n\n" - ) - if item.get("test_code"): - prompt += ( - f"## Tests\nThe following test code will be used to verify your solution:\n" - f"```python\n{item['test_code']}```\n\n" - ) - prompt += ( - "## Instructions\n" - "1. Write your solution to `solution.py`\n" - "2. Write the test code to `test_solution.py`\n" - "3. Run `python test_solution.py` to verify\n" - "4. Fix any failures and re-run until all tests pass\n" - ) - return prompt - - # ═══════════════════════════════════════════════════════════════════ - # 4. compute_reward - # ═══════════════════════════════════════════════════════════════════ - - async def compute_reward( - self, - item: dict, - result: AgentResult, - ctx: ToolContext, - ) -> float: - """ - Multi-signal reward: - - correctness (0.7): Did the tests pass? - - efficiency (0.15): Fewer turns = better - - tool_usage (0.15): Did the agent actually write + run code? - """ - cfg = self.config - - # ---- Signal 1: Test correctness ---- - # Check if test_solution.py exists and passes in the agent's sandbox - correctness = 0.0 - try: - test_result = ctx.terminal("python test_solution.py 2>&1", timeout=30) - output = test_result.get("output", "") - exit_code = test_result.get("exit_code", 1) - if exit_code == 0 and "passed" in output.lower(): - correctness = 1.0 - elif exit_code == 0: - correctness = 0.8 # Ran without error but no explicit "passed" - elif "assert" in output.lower() and "error" in output.lower(): - correctness = 0.2 # Partial — code runs but assertions fail - else: - correctness = 0.1 # Code errors out entirely - except Exception as e: - logger.debug("Test execution failed in reward: %s", e) - correctness = 0.0 - - # ---- Signal 2: Efficiency ---- - max_turns = cfg.max_agent_turns - turns_used = result.turns_used - if turns_used <= 3: - efficiency = 1.0 - elif turns_used <= max_turns // 2: - efficiency = 0.8 - elif turns_used <= max_turns * 3 // 4: - efficiency = 0.5 - else: - efficiency = 0.2 - - # ---- Signal 3: Tool usage ---- - tools_used = set() - for msg in result.messages: - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - fn = tc.get("function", {}) if isinstance(tc, dict) else {} - name = fn.get("name", "") - if name: - tools_used.add(name) - - # Good: used both terminal and file tools - if "terminal" in tools_used and ("write_file" in tools_used or "patch" in tools_used): - tool_usage = 1.0 - elif "terminal" in tools_used: - tool_usage = 0.6 - elif tools_used: - tool_usage = 0.3 - else: - tool_usage = 0.0 - - # ---- Combine ---- - reward = ( - cfg.correctness_weight * correctness - + cfg.efficiency_weight * efficiency - + cfg.tool_usage_weight * tool_usage - ) - reward = min(1.0, max(0.0, reward)) - - # Track metrics - self._reward_buffer.append(reward) - self._correctness_buffer.append(correctness) - self._efficiency_buffer.append(efficiency) - self._tool_usage_buffer.append(tool_usage) - - logger.debug( - "Reward: correctness=%.2f, efficiency=%.2f, tool_usage=%.2f → %.3f", - correctness, - efficiency, - tool_usage, - reward, - ) - return reward - - # ═══════════════════════════════════════════════════════════════════ - # 5. collect_trajectories — OPD pipeline - # ═══════════════════════════════════════════════════════════════════ - - async def collect_trajectories( - self, item: Item - ) -> Tuple[ - Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]], - List[Item], - ]: - """ - Override collect_trajectories to add the OPD pipeline. - - 1. Run standard rollouts via super() → ScoredDataGroup with tokens/masks/scores - 2. For each rollout, extract hints from next-state signals - 3. Score student tokens under enhanced (hint-augmented) distribution - 4. Add distill_token_ids / distill_logprobs to the ScoredDataGroup - """ - # Step 1: Run standard rollouts - scored_group, backlog = await super().collect_trajectories(item) - - # Step 2: OPD pipeline (only if enabled and we have VLLM server) - if ( - self.config.opd_enabled - and scored_group is not None - and isinstance(scored_group, dict) - and self._use_managed_server() - ): - await self._apply_opd_pipeline(scored_group) - - return scored_group, backlog - - async def _apply_opd_pipeline(self, group: ScoredDataGroup) -> None: - """ - Apply on-policy distillation to each rollout in the group. - - For each rollout's messages: - 1. Find (assistant, next_state) turn pairs - 2. Extract hints via LLM judge with majority voting - 3. Build enhanced prompt (original + hint) - 4. Score student tokens under enhanced distribution via get_logprobs - 5. Add distill_token_ids / distill_logprobs to the group - """ - messages_list = group.get("messages", []) - tokens_list = group.get("tokens", []) - - if not messages_list or not tokens_list: - logger.debug("OPD: No messages or tokens to process") - return - - all_distill_token_ids: List[Optional[List[List[int]]]] = [] - all_distill_logprobs: List[Optional[List[List[float]]]] = [] - - for seq_idx, (messages, student_tokens) in enumerate( - zip(messages_list, tokens_list) - ): - try: - distill_ids, distill_lps = await self._opd_for_sequence( - messages, student_tokens - ) - all_distill_token_ids.append(distill_ids) - all_distill_logprobs.append(distill_lps) - except Exception as e: - logger.warning( - "OPD failed for sequence %d: %s", seq_idx, e - ) - all_distill_token_ids.append(None) - all_distill_logprobs.append(None) - - # Only set distill fields if at least one sequence succeeded - any_succeeded = any(d is not None for d in all_distill_token_ids) - if any_succeeded: - # Replace None entries with zero-padded arrays matching token length - for i in range(len(all_distill_token_ids)): - if all_distill_token_ids[i] is None and i < len(tokens_list): - seq_len = len(tokens_list[i]) - k = self.config.distill_topk - all_distill_token_ids[i] = [[0] * k] * seq_len - all_distill_logprobs[i] = [[0.0] * k] * seq_len - - group["distill_token_ids"] = all_distill_token_ids - group["distill_logprobs"] = all_distill_logprobs - logger.info( - "OPD: Set distill fields on %d/%d sequences", - sum(1 for d in all_distill_token_ids if d is not None), - len(all_distill_token_ids), - ) - - async def _opd_for_sequence( - self, messages: List[Dict], student_tokens: List[int] - ) -> Tuple[List[List[int]], List[List[float]]]: - """ - Run OPD for a single rollout sequence. - - 1. Walk conversation to find (assistant, next_state) pairs - 2. Extract hints from next-state signals - 3. For each hint-augmented turn, score student tokens via get_logprobs - 4. Merge per-turn teacher logprobs into a full-sequence distill array - - Returns: - (distill_token_ids, distill_logprobs) each of shape [seq_len][top_k] - """ - k = self.config.distill_topk - seq_len = len(student_tokens) - - # Initialize with zeros (no distill info = neutral) - distill_token_ids: List[List[int]] = [[0] * k for _ in range(seq_len)] - distill_logprobs: List[List[float]] = [[0.0] * k for _ in range(seq_len)] - - # Find (assistant, next_state) turn pairs - turn_pairs = self._extract_turn_pairs(messages) - if not turn_pairs: - return distill_token_ids, distill_logprobs - - hints_extracted = 0 - turns_scored = 0 - - for pair in turn_pairs: - try: - hint = await self._extract_hint( - pair["assistant_text"], - pair["next_state_text"], - pair["next_state_role"], - ) - if not hint: - continue - - hints_extracted += 1 - - # Build enhanced prompt with hint - enhanced_messages = _append_hint_to_messages( - pair["context_messages"], hint - ) - - # Tokenize the enhanced prompt - if not self.tokenizer: - logger.warning("OPD: No tokenizer available, skipping scoring") - continue - - enhanced_prompt = self.tokenizer.apply_chat_template( - enhanced_messages, - tokenize=False, - add_generation_prompt=True, - ) - - # Tokenize the assistant response to score - response_text = pair["assistant_text"] - enhanced_full_text = enhanced_prompt + response_text - enhanced_ids = self.tokenizer( - enhanced_full_text, add_special_tokens=False - )["input_ids"] - - response_ids = self.tokenizer( - response_text, add_special_tokens=False - )["input_ids"] - response_len = len(response_ids) - - if response_len == 0: - continue - - # Score via get_logprobs — teacher scoring the student's tokens - # under the enhanced (hint-augmented) distribution - try: - logprob_result = await self.server.get_logprobs( - input_ids=enhanced_ids, - top_k=k, - split="eval", # Use eval semaphore to not block training - ) - except Exception as e: - logger.debug("get_logprobs failed: %s", e) - continue - - teacher_topk_ids = logprob_result.get("prompt_topk_token_ids", []) - teacher_topk_lps = logprob_result.get("prompt_topk_logprobs", []) - - if not teacher_topk_ids: - continue - - # Extract only the response positions (last response_len entries) - if len(teacher_topk_ids) >= response_len: - resp_topk_ids = teacher_topk_ids[-response_len:] - resp_topk_lps = teacher_topk_lps[-response_len:] - else: - # Pad from the left if the response was shorter than expected - pad_len = response_len - len(teacher_topk_ids) - resp_topk_ids = [[0] * k] * pad_len + teacher_topk_ids - resp_topk_lps = [[0.0] * k] * pad_len + teacher_topk_lps - - # Map these back to the student's full sequence positions - # Find where this assistant turn's tokens appear in the full sequence - turn_start = self._find_token_span( - student_tokens, response_ids - ) - if turn_start is not None: - for j in range(min(response_len, seq_len - turn_start)): - pos = turn_start + j - if pos < seq_len and j < len(resp_topk_ids): - # Pad/truncate to exactly k entries - ids = resp_topk_ids[j][:k] - lps = resp_topk_lps[j][:k] - while len(ids) < k: - ids.append(0) - lps.append(0.0) - distill_token_ids[pos] = ids - distill_logprobs[pos] = lps - turns_scored += 1 - - except Exception as e: - logger.debug("OPD turn processing failed: %s", e) - continue - - # Track OPD metrics - self._hints_extracted_buffer.append(hints_extracted) - self._opd_turns_scored_buffer.append(turns_scored) - - logger.debug( - "OPD sequence: %d turn pairs, %d hints extracted, %d turns scored", - len(turn_pairs), - hints_extracted, - turns_scored, - ) - return distill_token_ids, distill_logprobs - - def _extract_turn_pairs( - self, messages: List[Dict] - ) -> List[Dict[str, Any]]: - """ - Walk conversation messages to find (assistant, next_state) pairs. - - A "turn pair" is an assistant message with content (the response) - followed by one or more tool results or a user reply (the next state). - - Returns list of dicts: - { - "context_messages": messages up to (not including) the assistant turn, - "assistant_text": the assistant's response text, - "next_state_text": the next state content (tool result or user reply), - "next_state_role": "tool" or "user", - } - """ - pairs = [] - i = 0 - while i < len(messages): - msg = messages[i] - if msg.get("role") == "assistant" and msg.get("content"): - # Found an assistant message with content - assistant_text = msg["content"] - context = messages[:i] # Everything before this turn - - # Look ahead for next state - j = i + 1 - # Skip tool_calls-only assistant messages and collect tool results - next_states = [] - while j < len(messages): - next_msg = messages[j] - if next_msg.get("role") == "tool": - next_states.append(next_msg) - j += 1 - elif next_msg.get("role") == "user": - next_states.append(next_msg) - break - else: - break - - if next_states: - # Combine all next-state content - next_text_parts = [] - next_role = next_states[0].get("role", "tool") - for ns in next_states: - content = ns.get("content", "") - if content: - # Truncate very long tool outputs - max_chars = self.config.hint_max_next_state_chars - if len(content) > max_chars: - content = content[:max_chars] + "\n...[truncated]" - next_text_parts.append(content) - - next_text = "\n---\n".join(next_text_parts) - if next_text.strip(): - pairs.append( - { - "context_messages": context, - "assistant_text": assistant_text, - "next_state_text": next_text, - "next_state_role": next_role, - } - ) - i += 1 - return pairs - - async def _extract_hint( - self, - assistant_text: str, - next_state_text: str, - next_state_role: str, - ) -> Optional[str]: - """ - Extract a hindsight hint from a next-state signal using majority-voted LLM judge. - - Returns the hint string if the judge votes positively, None otherwise. - """ - judge_messages = _build_hint_judge_messages( - response_text=assistant_text, - next_state_text=next_state_text, - next_state_role=next_state_role, - ) - - # Majority voting across multiple judge queries - votes = [] - tasks = [] - for _ in range(self.config.prm_votes): - tasks.append( - self.server.chat_completion( - messages=judge_messages, - n=1, - max_tokens=500, - temperature=0.7, - split="eval", - ) - ) - - results = await asyncio.gather(*tasks, return_exceptions=True) - - for result in results: - if isinstance(result, Exception): - logger.debug("Hint judge call failed: %s", result) - votes.append({"score": None, "hint": ""}) - continue - try: - text = result.choices[0].message.content or "" - score, hint = _parse_hint_result(text) - votes.append({"score": score, "hint": hint}) - except Exception as e: - logger.debug("Hint parse failed: %s", e) - votes.append({"score": None, "hint": ""}) - - selected = _select_best_hint(votes) - if selected is None: - return None - return selected["hint"] - - @staticmethod - def _find_token_span( - full_tokens: List[int], sub_tokens: List[int] - ) -> Optional[int]: - """ - Find where sub_tokens appears in full_tokens. - Returns the start index, or None if not found. - - Uses a sliding window search. For long sequences, searches - from the end since assistant responses are typically at the end. - """ - if not sub_tokens or not full_tokens: - return None - sub_len = len(sub_tokens) - full_len = len(full_tokens) - if sub_len > full_len: - return None - - # Search backwards (assistant responses are usually near the end) - for i in range(full_len - sub_len, -1, -1): - if full_tokens[i : i + sub_len] == sub_tokens: - return i - return None - - # ═══════════════════════════════════════════════════════════════════ - # 6. evaluate - # ═══════════════════════════════════════════════════════════════════ - - async def evaluate(self, *args, **kwargs) -> None: - """ - Evaluate on held-out coding tasks using the full agent loop. - No OPD during eval — just standard agentic evaluation. - """ - if not self._eval_items: - logger.warning("No eval items available.") - return - - eval_size = min(self.config.eval_size, len(self._eval_items)) - eval_items = self._eval_items[:eval_size] - - logger.info("Running eval on %d coding tasks...", len(eval_items)) - start_time = time.time() - samples = [] - - tools, valid_names = self._resolve_tools_for_group() - - for i, item in enumerate(eval_items): - task_id = str(uuid.uuid4()) - logger.info( - "Eval [%d/%d]: %s...", i + 1, len(eval_items), item["task"][:60] - ) - - try: - messages: List[Dict[str, Any]] = [] - if self.config.system_prompt: - messages.append( - {"role": "system", "content": self.config.system_prompt} - ) - messages.append( - {"role": "user", "content": self.format_prompt(item)} - ) - - agent = HermesAgentLoop( - server=self.server, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=task_id, - temperature=0.0, - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - budget_config=self.config.build_budget_config(), - ) - result = await agent.run(messages) - - # Compute reward (track buffer lengths to rollback eval pollution) - buf_len = len(self._correctness_buffer) - ctx = ToolContext(task_id) - try: - reward = await self.compute_reward(item, result, ctx) - finally: - ctx.cleanup() - - # Extract correctness and rollback training buffers - correctness = ( - self._correctness_buffer[buf_len] - if len(self._correctness_buffer) > buf_len - else 0.0 - ) - for buf in ( - self._reward_buffer, - self._correctness_buffer, - self._efficiency_buffer, - self._tool_usage_buffer, - ): - if len(buf) > buf_len: - buf.pop() - - # Also rollback OPD buffers if they were touched - for buf in ( - self._hints_extracted_buffer, - self._opd_turns_scored_buffer, - ): - if len(buf) > buf_len: - buf.pop() - - # Extract final response - final_response = "" - for msg in reversed(result.messages): - if ( - msg.get("role") == "assistant" - and msg.get("content") - and not final_response - ): - final_response = msg["content"] - break - - samples.append( - { - "prompt": item["task"][:200], - "response": final_response[:500], - "correctness": correctness, - "reward": reward, - "turns": result.turns_used, - } - ) - - logger.info( - " → correctness=%.2f, reward=%.3f, turns=%d", - correctness, - reward, - result.turns_used, - ) - - except Exception as e: - logger.error("Eval error: %s", e) - samples.append( - { - "prompt": item["task"][:200], - "response": f"ERROR: {e}", - "correctness": 0.0, - "reward": 0.0, - "turns": 0, - } - ) - - end_time = time.time() - - correctness_scores = [s["correctness"] for s in samples] - rewards = [s["reward"] for s in samples] - n = len(samples) - - eval_metrics = { - "eval/mean_correctness": sum(correctness_scores) / n if n else 0.0, - "eval/mean_reward": sum(rewards) / n if n else 0.0, - "eval/pass_rate": ( - sum(1 for c in correctness_scores if c >= 0.8) / n if n else 0.0 - ), - "eval/n_items": n, - } - - logger.info( - "Eval complete — correctness=%.3f, reward=%.3f, pass_rate=%.0f%%", - eval_metrics["eval/mean_correctness"], - eval_metrics["eval/mean_reward"], - eval_metrics["eval/pass_rate"] * 100, - ) - - await self.evaluate_log( - metrics=eval_metrics, - samples=samples, - start_time=start_time, - end_time=end_time, - ) - - # ═══════════════════════════════════════════════════════════════════ - # 7. wandb_log — custom OPD metrics - # ═══════════════════════════════════════════════════════════════════ - - async def wandb_log(self, wandb_metrics: Optional[Dict] = None) -> None: - """Log reward breakdown and OPD-specific metrics to wandb.""" - if wandb_metrics is None: - wandb_metrics = {} - - if self._reward_buffer: - n = len(self._reward_buffer) - wandb_metrics["train/mean_reward"] = sum(self._reward_buffer) / n - wandb_metrics["train/mean_correctness"] = ( - sum(self._correctness_buffer) / n - ) - wandb_metrics["train/mean_efficiency"] = ( - sum(self._efficiency_buffer) / n - ) - wandb_metrics["train/mean_tool_usage"] = ( - sum(self._tool_usage_buffer) / n - ) - wandb_metrics["train/pass_rate"] = ( - sum(1 for c in self._correctness_buffer if c >= 0.8) / n - ) - wandb_metrics["train/total_rollouts"] = n - - self._reward_buffer.clear() - self._correctness_buffer.clear() - self._efficiency_buffer.clear() - self._tool_usage_buffer.clear() - - # OPD-specific metrics - if self._hints_extracted_buffer: - n = len(self._hints_extracted_buffer) - wandb_metrics["opd/mean_hints_per_rollout"] = ( - sum(self._hints_extracted_buffer) / n - ) - wandb_metrics["opd/mean_turns_scored"] = ( - sum(self._opd_turns_scored_buffer) / n - ) - wandb_metrics["opd/hint_rate"] = ( - sum(1 for h in self._hints_extracted_buffer if h > 0) / n - ) - wandb_metrics["opd/total_hints"] = sum(self._hints_extracted_buffer) - wandb_metrics["opd/total_scored_turns"] = sum( - self._opd_turns_scored_buffer - ) - - self._hints_extracted_buffer.clear() - self._opd_turns_scored_buffer.clear() - - await super().wandb_log(wandb_metrics) - - -# ═══════════════════════════════════════════════════════════════════════ -# Entry point -# ═══════════════════════════════════════════════════════════════════════ - -if __name__ == "__main__": - AgenticOPDEnv.cli() diff --git a/environments/benchmarks/__init__.py b/environments/benchmarks/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/environments/benchmarks/tblite/README.md b/environments/benchmarks/tblite/README.md deleted file mode 100644 index 54b3745c383..00000000000 --- a/environments/benchmarks/tblite/README.md +++ /dev/null @@ -1,73 +0,0 @@ -# OpenThoughts-TBLite Evaluation Environment - -This environment evaluates terminal agents on the [OpenThoughts-TBLite](https://huggingface.co/datasets/open-thoughts/OpenThoughts-TBLite) benchmark, a difficulty-calibrated subset of [Terminal-Bench 2.0](https://www.tbench.ai/leaderboard/terminal-bench/2.0). - -## Source - -OpenThoughts-TBLite was created by the [OpenThoughts](https://www.openthoughts.ai/) Agent team in collaboration with [Snorkel AI](https://snorkel.ai/) and [Bespoke Labs](https://bespokelabs.ai/). The original dataset and documentation live at: - -- **Dataset (source):** [open-thoughts/OpenThoughts-TBLite](https://huggingface.co/datasets/open-thoughts/OpenThoughts-TBLite) -- **GitHub:** [open-thoughts/OpenThoughts-TBLite](https://github.com/open-thoughts/OpenThoughts-TBLite) -- **Blog post:** [openthoughts.ai/blog/openthoughts-tblite](https://www.openthoughts.ai/blog/openthoughts-tblite) - -## Our Dataset - -We converted the source into the same schema used by our Terminal-Bench 2.0 environment (pre-built Docker Hub images, base64-encoded test tarballs, etc.) and published it as: - -- **Dataset (ours):** [NousResearch/openthoughts-tblite](https://huggingface.co/datasets/NousResearch/openthoughts-tblite) -- **Docker images:** `nousresearch/tblite-:latest` on Docker Hub (100 images) - -The conversion script is at `scripts/prepare_tblite_dataset.py`. - -## Why TBLite? - -Terminal-Bench 2.0 is one of the strongest frontier evaluations for terminal agents, but when a model scores near the floor (e.g., Qwen 3 8B at <1%), many changes look identical in aggregate score. TBLite addresses this by calibrating task difficulty using Claude Haiku 4.5 as a reference: - -| Difficulty | Pass Rate Range | Tasks | -|------------|----------------|-------| -| Easy | >= 70% | 40 | -| Medium | 40-69% | 26 | -| Hard | 10-39% | 26 | -| Extreme | < 10% | 8 | - -This gives enough solvable tasks to detect small improvements quickly, while preserving enough hard tasks to avoid saturation. The correlation between TBLite and TB2 scores is **r = 0.911**. - -TBLite also runs 2.6-8x faster than the full TB2, making it practical for iteration loops. - -## Usage - -```bash -# Run the full benchmark -python environments/benchmarks/tblite/tblite_env.py evaluate - -# Filter to specific tasks -python environments/benchmarks/tblite/tblite_env.py evaluate \ - --env.task_filter "broken-python,pandas-etl" - -# Use a different model -python environments/benchmarks/tblite/tblite_env.py evaluate \ - --server.model_name "qwen/qwen3-30b" -``` - -## Architecture - -`TBLiteEvalEnv` is a thin subclass of `TerminalBench2EvalEnv`. All evaluation logic (agent loop, Docker sandbox management, test verification, metrics) is inherited. Only the defaults differ: - -| Setting | TB2 | TBLite | -|----------------|----------------------------------|-----------------------------------------| -| Dataset | `NousResearch/terminal-bench-2` | `NousResearch/openthoughts-tblite` | -| Tasks | 89 | 100 | -| Task timeout | 1800s (30 min) | 1200s (20 min) | -| Wandb name | `terminal-bench-2` | `openthoughts-tblite` | - -## Citation - -```bibtex -@software{OpenThoughts-TBLite, - author = {OpenThoughts-Agent team, Snorkel AI, Bespoke Labs}, - month = Feb, - title = {{OpenThoughts-TBLite: A High-Signal Benchmark for Iterating on Terminal Agents}}, - howpublished = {https://www.openthoughts.ai/blog/openthoughts-tblite}, - year = {2026} -} -``` diff --git a/environments/benchmarks/tblite/__init__.py b/environments/benchmarks/tblite/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/environments/benchmarks/tblite/default.yaml b/environments/benchmarks/tblite/default.yaml deleted file mode 100644 index cb521828061..00000000000 --- a/environments/benchmarks/tblite/default.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# OpenThoughts-TBLite Evaluation -- Default Configuration -# -# Eval-only environment for the TBLite benchmark (100 difficulty-calibrated -# terminal tasks, a faster proxy for Terminal-Bench 2.0). -# Uses Modal terminal backend for per-task cloud-isolated sandboxes -# and OpenRouter for inference. -# -# Usage: -# python environments/benchmarks/tblite/tblite_env.py evaluate \ -# --config environments/benchmarks/tblite/default.yaml -# -# # Override model: -# python environments/benchmarks/tblite/tblite_env.py evaluate \ -# --config environments/benchmarks/tblite/default.yaml \ -# --openai.model_name anthropic/claude-sonnet-4 - -env: - enabled_toolsets: ["terminal", "file"] - max_agent_turns: 60 - max_token_length: 32000 - agent_temperature: 0.8 - terminal_backend: "modal" - terminal_timeout: 300 # 5 min per command (builds, pip install) - tool_pool_size: 128 # thread pool for 100 parallel tasks - dataset_name: "NousResearch/openthoughts-tblite" - test_timeout: 600 - task_timeout: 1200 # 20 min wall-clock per task (TBLite tasks are faster) - tokenizer_name: "NousResearch/Hermes-3-Llama-3.1-8B" - use_wandb: true - wandb_name: "openthoughts-tblite" - ensure_scores_are_not_same: false - data_dir_to_save_evals: "environments/benchmarks/evals/openthoughts-tblite" - -openai: - base_url: "https://openrouter.ai/api/v1" - model_name: "anthropic/claude-opus-4.6" - server_type: "openai" - health_check: false - # api_key loaded from OPENROUTER_API_KEY in .env diff --git a/environments/benchmarks/tblite/local.yaml b/environments/benchmarks/tblite/local.yaml deleted file mode 100644 index 35d4b896869..00000000000 --- a/environments/benchmarks/tblite/local.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# OpenThoughts-TBLite Evaluation -- Docker Backend (Local Compute) -# -# Runs tasks in Docker containers on the local machine. -# Sandboxed like Modal but no cloud costs. Good for dev/testing. -# -# Usage: -# python environments/benchmarks/tblite/tblite_env.py evaluate \ -# --config environments/benchmarks/tblite/local.yaml -# -# # Override concurrency: -# python environments/benchmarks/tblite/tblite_env.py evaluate \ -# --config environments/benchmarks/tblite/local.yaml \ -# --env.eval_concurrency 4 - -env: - enabled_toolsets: ["terminal", "file"] - max_agent_turns: 60 - max_token_length: 32000 - agent_temperature: 0.8 - terminal_backend: "docker" - terminal_timeout: 300 - tool_pool_size: 16 - dataset_name: "NousResearch/openthoughts-tblite" - test_timeout: 600 - task_timeout: 1200 - eval_concurrency: 8 # max 8 tasks at once - tokenizer_name: "NousResearch/Hermes-3-Llama-3.1-8B" - use_wandb: false - wandb_name: "openthoughts-tblite-local" - ensure_scores_are_not_same: false - data_dir_to_save_evals: "environments/benchmarks/evals/openthoughts-tblite-local" - -openai: - base_url: "https://openrouter.ai/api/v1" - model_name: "anthropic/claude-sonnet-4" - server_type: "openai" - health_check: false - # api_key loaded from OPENROUTER_API_KEY in .env diff --git a/environments/benchmarks/tblite/local_vllm.yaml b/environments/benchmarks/tblite/local_vllm.yaml deleted file mode 100644 index 17689ba1d35..00000000000 --- a/environments/benchmarks/tblite/local_vllm.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# OpenThoughts-TBLite Evaluation -- Local vLLM Backend -# -# Runs against a local vLLM server with Docker sandboxes. -# -# Start the vLLM server from the atropos directory: -# python -m example_trainer.vllm_api_server \ -# --model Qwen/Qwen3-4B-Instruct-2507 \ -# --port 9001 \ -# --gpu-memory-utilization 0.8 \ -# --max-model-len=32000 -# -# Then run: -# python environments/benchmarks/tblite/tblite_env.py evaluate \ -# --config environments/benchmarks/tblite/local_vllm.yaml - -env: - enabled_toolsets: ["terminal", "file"] - max_agent_turns: 60 - max_token_length: 16000 - agent_temperature: 0.6 - terminal_backend: "docker" - terminal_timeout: 300 - tool_pool_size: 16 - dataset_name: "NousResearch/openthoughts-tblite" - test_timeout: 600 - task_timeout: 1200 - eval_concurrency: 8 - tool_call_parser: "hermes" - system_prompt: "You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands." - tokenizer_name: "Qwen/Qwen3-4B-Instruct-2507" - use_wandb: false - wandb_name: "tblite-qwen3-4b-instruct" - ensure_scores_are_not_same: false - data_dir_to_save_evals: "environments/benchmarks/evals/tblite-qwen3-4b-local" - -openai: - base_url: "http://localhost:9001" - model_name: "Qwen/Qwen3-4B-Instruct-2507" - server_type: "vllm" - health_check: false diff --git a/environments/benchmarks/tblite/run_eval.sh b/environments/benchmarks/tblite/run_eval.sh deleted file mode 100755 index 9d860bf5ef7..00000000000 --- a/environments/benchmarks/tblite/run_eval.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash - -# OpenThoughts-TBLite Evaluation -# -# Run from repo root: -# bash environments/benchmarks/tblite/run_eval.sh -# -# Override model: -# bash environments/benchmarks/tblite/run_eval.sh \ -# --openai.model_name anthropic/claude-sonnet-4 -# -# Run a subset: -# bash environments/benchmarks/tblite/run_eval.sh \ -# --env.task_filter broken-python,pandas-etl -# -# All terminal settings (backend, timeout, lifetime, pool size) are -# configured via env config fields -- no env vars needed. - -set -euo pipefail - -mkdir -p logs evals/openthoughts-tblite -LOG_FILE="logs/tblite_$(date +%Y%m%d_%H%M%S).log" - -echo "OpenThoughts-TBLite Evaluation" -echo "Log file: $LOG_FILE" -echo "" - -# Unbuffered python output so logs are written in real-time -export PYTHONUNBUFFERED=1 - -# Show INFO-level agent loop timing (api/tool durations per turn) -# These go to the log file; tqdm + [START]/[PASS]/[FAIL] go to terminal -export LOGLEVEL=INFO - -python tblite_env.py evaluate \ - --config default.yaml \ - "$@" \ - 2>&1 | tee "$LOG_FILE" - -echo "" -echo "Log saved to: $LOG_FILE" -echo "Eval results: evals/openthoughts-tblite/" diff --git a/environments/benchmarks/tblite/tblite_env.py b/environments/benchmarks/tblite/tblite_env.py deleted file mode 100644 index 4b23f9cc558..00000000000 --- a/environments/benchmarks/tblite/tblite_env.py +++ /dev/null @@ -1,119 +0,0 @@ -""" -OpenThoughts-TBLite Evaluation Environment - -A lighter, faster alternative to Terminal-Bench 2.0 for iterating on terminal -agents. Uses the same evaluation logic as TerminalBench2EvalEnv but defaults -to the NousResearch/openthoughts-tblite dataset (100 difficulty-calibrated -tasks vs TB2's 89 harder tasks). - -TBLite tasks are a curated subset of TB2 with a difficulty distribution -designed to give meaningful signal even for smaller models: - - Easy (40 tasks): >= 70% pass rate with Claude Haiku 4.5 - - Medium (26 tasks): 40-69% pass rate - - Hard (26 tasks): 10-39% pass rate - - Extreme (8 tasks): < 10% pass rate - -Usage: - python environments/benchmarks/tblite/tblite_env.py evaluate - - # Filter to specific tasks: - python environments/benchmarks/tblite/tblite_env.py evaluate \\ - --env.task_filter "broken-python,pandas-etl" -""" - -import os -import sys -from pathlib import Path -from typing import List, Tuple - -_repo_root = Path(__file__).resolve().parent.parent.parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -from pydantic import Field - -from atroposlib.envs.base import EvalHandlingEnum -from atroposlib.envs.server_handling.server_manager import APIServerConfig - -from environments.benchmarks.terminalbench_2.terminalbench2_env import ( - TerminalBench2EvalConfig, - TerminalBench2EvalEnv, -) - - -class TBLiteEvalConfig(TerminalBench2EvalConfig): - """Configuration for the OpenThoughts-TBLite evaluation environment. - - Inherits all TB2 config fields. Only the dataset default and task timeout - differ -- TBLite tasks are calibrated to be faster. - """ - - dataset_name: str = Field( - default="NousResearch/openthoughts-tblite", - description="HuggingFace dataset containing TBLite tasks.", - ) - - task_timeout: int = Field( - default=1200, - description="Maximum wall-clock seconds per task. TBLite tasks are " - "generally faster than TB2, so 20 minutes is usually sufficient.", - ) - - -class TBLiteEvalEnv(TerminalBench2EvalEnv): - """OpenThoughts-TBLite evaluation environment. - - Inherits all evaluation logic from TerminalBench2EvalEnv (agent loop, - test verification, Docker image resolution, metrics, wandb logging). - Only the default configuration differs. - """ - - name = "openthoughts-tblite" - env_config_cls = TBLiteEvalConfig - - @classmethod - def config_init(cls) -> Tuple[TBLiteEvalConfig, List[APIServerConfig]]: - env_config = TBLiteEvalConfig( - enabled_toolsets=["terminal", "file"], - disabled_toolsets=None, - distribution=None, - - max_agent_turns=60, - max_token_length=16000, - agent_temperature=0.6, - system_prompt=None, - - terminal_backend="modal", - terminal_timeout=300, - - test_timeout=180, - - # 100 tasks in parallel - tool_pool_size=128, - - eval_handling=EvalHandlingEnum.STOP_TRAIN, - group_size=1, - steps_per_eval=1, - total_steps=1, - - tokenizer_name="NousResearch/Hermes-3-Llama-3.1-8B", - use_wandb=True, - wandb_name="openthoughts-tblite", - ensure_scores_are_not_same=False, - ) - - server_configs = [ - APIServerConfig( - base_url="https://openrouter.ai/api/v1", - model_name="anthropic/claude-sonnet-4", - server_type="openai", - api_key=os.getenv("OPENROUTER_API_KEY", ""), - health_check=False, - ) - ] - - return env_config, server_configs - - -if __name__ == "__main__": - TBLiteEvalEnv.cli() diff --git a/environments/benchmarks/terminalbench_2/__init__.py b/environments/benchmarks/terminalbench_2/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/environments/benchmarks/terminalbench_2/default.yaml b/environments/benchmarks/terminalbench_2/default.yaml deleted file mode 100644 index eb675b12e70..00000000000 --- a/environments/benchmarks/terminalbench_2/default.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# Terminal-Bench 2.0 Evaluation -- Default Configuration -# -# Eval-only environment for the TB2 benchmark (89 terminal tasks). -# Uses Modal terminal backend for per-task cloud-isolated sandboxes -# and OpenRouter for inference. -# -# Usage: -# python environments/benchmarks/terminalbench_2/terminalbench2_env.py evaluate \ -# --config environments/benchmarks/terminalbench_2/default.yaml -# -# # Override model: -# python environments/benchmarks/terminalbench_2/terminalbench2_env.py evaluate \ -# --config environments/benchmarks/terminalbench_2/default.yaml \ -# --openai.model_name anthropic/claude-sonnet-4 - -env: - enabled_toolsets: ["terminal", "file"] - max_agent_turns: 60 - max_token_length: 32000 - agent_temperature: 0.8 - terminal_backend: "modal" - terminal_timeout: 300 # 5 min per command (builds, pip install) - tool_pool_size: 128 # thread pool for 89 parallel tasks - dataset_name: "NousResearch/terminal-bench-2" - test_timeout: 600 - task_timeout: 1800 # 30 min wall-clock per task, auto-FAIL if exceeded - tokenizer_name: "NousResearch/Hermes-3-Llama-3.1-8B" - use_wandb: true - wandb_name: "terminal-bench-2" - ensure_scores_are_not_same: false - data_dir_to_save_evals: "environments/benchmarks/evals/terminal-bench-2" - # CRITICAL: Limit concurrent Modal sandbox creations to avoid deadlocks. - # Modal's blocking calls (App.lookup, etc.) deadlock when too many sandboxes - # are created simultaneously inside thread pool workers via asyncio.run(). - max_concurrent_tasks: 8 - -openai: - base_url: "https://openrouter.ai/api/v1" - model_name: "anthropic/claude-opus-4.6" - server_type: "openai" - health_check: false - # api_key loaded from OPENROUTER_API_KEY in .env diff --git a/environments/benchmarks/terminalbench_2/run_eval.sh b/environments/benchmarks/terminalbench_2/run_eval.sh deleted file mode 100755 index ffbe4848065..00000000000 --- a/environments/benchmarks/terminalbench_2/run_eval.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash - -# Terminal-Bench 2.0 Evaluation -# -# Run from repo root: -# bash environments/benchmarks/terminalbench_2/run_eval.sh -# -# Override model: -# bash environments/benchmarks/terminalbench_2/run_eval.sh \ -# --openai.model_name anthropic/claude-sonnet-4 -# -# Run a subset: -# bash environments/benchmarks/terminalbench_2/run_eval.sh \ -# --env.task_filter fix-git,git-multibranch -# -# All terminal settings (backend, timeout, lifetime, pool size) are -# configured via env config fields -- no env vars needed. - -set -euo pipefail - -mkdir -p logs evals/terminal-bench-2 -LOG_FILE="logs/terminalbench2_$(date +%Y%m%d_%H%M%S).log" - -echo "Terminal-Bench 2.0 Evaluation" -echo "Log file: $LOG_FILE" -echo "" - -# Unbuffered python output so logs are written in real-time -export PYTHONUNBUFFERED=1 - -# Show INFO-level agent loop timing (api/tool durations per turn) -# These go to the log file; tqdm + [START]/[PASS]/[FAIL] go to terminal -export LOGLEVEL=INFO - -python terminalbench2_env.py evaluate \ - --config default.yaml \ - "$@" \ - 2>&1 | tee "$LOG_FILE" - -echo "" -echo "Log saved to: $LOG_FILE" -echo "Eval results: evals/terminal-bench-2/" diff --git a/environments/benchmarks/terminalbench_2/terminalbench2_env.py b/environments/benchmarks/terminalbench_2/terminalbench2_env.py deleted file mode 100644 index 1a76b8da61e..00000000000 --- a/environments/benchmarks/terminalbench_2/terminalbench2_env.py +++ /dev/null @@ -1,1016 +0,0 @@ -""" -TerminalBench2Env -- Terminal-Bench 2.0 Evaluation Environment - -Evaluates agentic LLMs on challenging terminal tasks from Terminal-Bench 2.0. -Each task provides a unique Docker environment (pre-built on Docker Hub), a natural -language instruction, and a test suite for verification. The agent uses terminal + -file tools to complete the task, then the test suite runs inside the same sandbox. - -This is an eval-only environment (not a training environment). It is designed to -be run via the `evaluate` subcommand: - - python environments/terminalbench2_env.py evaluate \\ - --env.dataset_name NousResearch/terminal-bench-2 - -The evaluate flow: - 1. setup() -- Loads the TB2 dataset from HuggingFace - 2. evaluate() -- Iterates over all tasks, running each through: - a. rollout_and_score_eval() -- Per-task agent loop + test verification - - Resolves Docker image (pre-built Hub image or Dockerfile fallback) - - Registers per-task Modal sandbox via register_task_env_overrides() - - Runs the HermesAgentLoop (terminal + file tools) - - Uploads test suite and runs test.sh in the same sandbox - - Returns binary pass/fail result - b. Aggregates per-task, per-category, and overall pass rates - c. Logs results via evaluate_log() and wandb - -Key features: - - Per-task Modal sandboxes using pre-built Docker Hub images - - Binary reward: 1.0 if all tests pass, 0.0 otherwise - - Concurrency-controlled parallel evaluation via asyncio.Semaphore - - Per-task, per-category, and aggregate pass rate tracking -""" - -import asyncio -import base64 -import io -import json -import logging -import os -import shutil -import sys -import tarfile -import tempfile -import time -import uuid -from collections import defaultdict -from pathlib import Path, PurePosixPath, PureWindowsPath -from typing import Any, Dict, List, Optional, Tuple, Union - -# Ensure repo root is on sys.path for imports -_repo_root = Path(__file__).resolve().parent.parent.parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -from pydantic import Field - -from atroposlib.envs.base import EvalHandlingEnum -from atroposlib.envs.server_handling.server_manager import APIServerConfig - -from environments.agent_loop import AgentResult, HermesAgentLoop -from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig -from environments.tool_context import ToolContext -from tools.terminal_tool import ( - register_task_env_overrides, - clear_task_env_overrides, - cleanup_vm, -) - -logger = logging.getLogger(__name__) - - -# ============================================================================= -# Configuration -# ============================================================================= - -class TerminalBench2EvalConfig(HermesAgentEnvConfig): - """ - Configuration for the Terminal-Bench 2.0 evaluation environment. - - Extends HermesAgentEnvConfig with TB2-specific settings for dataset loading, - test execution, task filtering, and eval concurrency. - """ - - # --- Dataset --- - dataset_name: str = Field( - default="NousResearch/terminal-bench-2", - description="HuggingFace dataset containing TB2 tasks.", - ) - - # --- Test execution --- - test_timeout: int = Field( - default=180, - description="Timeout in seconds for running the test suite after agent completes.", - ) - - # --- Image strategy --- - force_build: bool = Field( - default=False, - description="If True, always build from Dockerfile (ignore docker_image). " - "Useful for testing custom Dockerfiles.", - ) - - # --- Task filtering (comma-separated from CLI) --- - task_filter: Optional[str] = Field( - default=None, - description="Comma-separated task names to run (e.g., 'fix-git,git-multibranch'). " - "If not set, all tasks are run.", - ) - skip_tasks: Optional[str] = Field( - default=None, - description="Comma-separated task names to skip on top of the default skip list.", - ) - - # --- Per-task wall-clock timeout --- - task_timeout: int = Field( - default=1800, - description="Maximum wall-clock seconds per task (agent loop + verification). " - "Tasks exceeding this are scored as FAIL. Default 30 minutes.", - ) - - # --- Concurrency control --- - max_concurrent_tasks: int = Field( - default=8, - description="Maximum number of tasks to run concurrently. " - "Limits concurrent Modal sandbox creations to avoid async/threading deadlocks. " - "Modal has internal limits and creating too many sandboxes simultaneously " - "causes blocking calls to deadlock inside the thread pool.", - ) - - # --- Eval concurrency --- - eval_concurrency: int = Field( - default=0, - description="Maximum number of tasks to evaluate in parallel. " - "0 means unlimited (all tasks run concurrently). " - "Set to 8 for local backends to avoid overwhelming the machine.", - ) - - -# Tasks that cannot run properly on Modal and are excluded from scoring. -MODAL_INCOMPATIBLE_TASKS = { - "qemu-startup", # Needs KVM/hardware virtualization - "qemu-alpine-ssh", # Needs KVM/hardware virtualization - "crack-7z-hash", # Password brute-force -- too slow for cloud sandbox timeouts -} - - -# ============================================================================= -# Tar extraction helper -# ============================================================================= - -def _normalize_tar_member_parts(member_name: str) -> list: - """Return safe path components for a tar member or raise ValueError.""" - normalized_name = member_name.replace("\\", "/") - posix_path = PurePosixPath(normalized_name) - windows_path = PureWindowsPath(member_name) - - if ( - not normalized_name - or posix_path.is_absolute() - or windows_path.is_absolute() - or windows_path.drive - ): - raise ValueError(f"Unsafe archive member path: {member_name}") - - parts = [part for part in posix_path.parts if part not in {"", "."}] - if not parts or any(part == ".." for part in parts): - raise ValueError(f"Unsafe archive member path: {member_name}") - return parts - - -def _safe_extract_tar(tar: tarfile.TarFile, target_dir: Path) -> None: - """Extract a tar archive without allowing traversal or link entries.""" - target_dir.mkdir(parents=True, exist_ok=True) - target_root = target_dir.resolve() - - for member in tar.getmembers(): - parts = _normalize_tar_member_parts(member.name) - target = target_dir.joinpath(*parts) - target_real = target.resolve(strict=False) - - try: - target_real.relative_to(target_root) - except ValueError as exc: - raise ValueError(f"Unsafe archive member path: {member.name}") from exc - - if member.isdir(): - target_real.mkdir(parents=True, exist_ok=True) - continue - - if not member.isfile(): - raise ValueError(f"Unsupported archive member type: {member.name}") - - target_real.parent.mkdir(parents=True, exist_ok=True) - extracted = tar.extractfile(member) - if extracted is None: - raise ValueError(f"Cannot read archive member: {member.name}") - - with extracted, open(target_real, "wb") as dst: - shutil.copyfileobj(extracted, dst) - - try: - os.chmod(target_real, member.mode & 0o777) - except OSError: - pass - - -def _extract_base64_tar(b64_data: str, target_dir: Path): - """Extract a base64-encoded tar.gz archive into target_dir.""" - if not b64_data: - return - raw = base64.b64decode(b64_data) - buf = io.BytesIO(raw) - with tarfile.open(fileobj=buf, mode="r:gz") as tar: - _safe_extract_tar(tar, target_dir) - - -# ============================================================================= -# Main Environment -# ============================================================================= - -class TerminalBench2EvalEnv(HermesAgentBaseEnv): - """ - Terminal-Bench 2.0 evaluation environment (eval-only, no training). - - Inherits from HermesAgentBaseEnv for: - - Terminal backend setup (os.environ["TERMINAL_ENV"]) - - Tool resolution via _resolve_tools_for_group() - - Monkey patches for async-safe tool operation - - Wandb trajectory formatting - - The evaluate flow (triggered by `environment.py evaluate`): - 1. setup() -- Load dataset from HuggingFace - 2. evaluate() -- Run all tasks through rollout_and_score_eval() - - Each task in rollout_and_score_eval(): - 1. Resolve Docker image (pre-built Hub image or Dockerfile fallback) - 2. Register per-task Modal sandbox override - 3. Run HermesAgentLoop with terminal + file tools - 4. Upload test suite and execute test.sh in the same sandbox - 5. Check /logs/verifier/reward.txt for pass/fail - 6. Clean up sandbox, overrides, and temp files - """ - - name = "terminal-bench-2" - env_config_cls = TerminalBench2EvalConfig - - @classmethod - def config_init(cls) -> Tuple[TerminalBench2EvalConfig, List[APIServerConfig]]: - """ - Default configuration for Terminal-Bench 2.0 evaluation. - - Uses eval-only settings: - - eval_handling=STOP_TRAIN so the eval flow runs cleanly - - steps_per_eval=1, total_steps=1 so eval triggers immediately - - group_size=1 (one rollout per group, each task is expensive) - - Uses Modal terminal backend (cloud-isolated sandbox per task) and - OpenRouter with Claude for inference. - """ - env_config = TerminalBench2EvalConfig( - # Terminal + file tools only (the agent interacts via shell commands) - enabled_toolsets=["terminal", "file"], - disabled_toolsets=None, - distribution=None, - - # Agent settings -- TB2 tasks are complex, need many turns - max_agent_turns=60, - max_token_length=16000, - agent_temperature=0.6, - system_prompt=None, - - # Modal backend for per-task cloud-isolated sandboxes - terminal_backend="modal", - terminal_timeout=300, # 5 min per command (builds, pip install, etc.) - - # Test execution timeout (TB2 test scripts can install deps like pytest) - test_timeout=180, - - # 89 tasks run in parallel, each needs a thread for tool calls - tool_pool_size=128, - - # --- Eval-only Atropos settings --- - # These settings make the env work as an eval-only environment: - # - STOP_TRAIN: pauses training during eval (standard for eval envs) - # - steps_per_eval=1, total_steps=1: eval triggers immediately - # - group_size=1: one rollout per group (each task is expensive) - eval_handling=EvalHandlingEnum.STOP_TRAIN, - group_size=1, - steps_per_eval=1, - total_steps=1, - - tokenizer_name="NousResearch/Hermes-3-Llama-3.1-8B", - use_wandb=True, - wandb_name="terminal-bench-2", - ensure_scores_are_not_same=False, # Binary rewards may all be 0 or 1 - ) - - # OpenRouter with Claude -- API key loaded from .env - server_configs = [ - APIServerConfig( - base_url="https://openrouter.ai/api/v1", - model_name="anthropic/claude-sonnet-4", - server_type="openai", - api_key=os.getenv("OPENROUTER_API_KEY", ""), - health_check=False, - ) - ] - - return env_config, server_configs - - # ========================================================================= - # Setup -- load dataset - # ========================================================================= - - async def setup(self): - """Load the Terminal-Bench 2.0 dataset from HuggingFace.""" - from datasets import load_dataset - - # Auto-set terminal_lifetime to task_timeout + 120s so sandboxes - # never get killed during an active task, but still get cleaned up - # promptly after the task times out. - lifetime = self.config.task_timeout + 120 - self.config.terminal_lifetime = lifetime - os.environ["TERMINAL_LIFETIME_SECONDS"] = str(lifetime) - print(f" Terminal lifetime auto-set to {lifetime}s (task_timeout + 120s)") - - print(f"Loading TB2 dataset from: {self.config.dataset_name}") - ds = load_dataset(self.config.dataset_name, split="train") - - # Apply task filters (comma-separated strings from CLI) - tasks = list(ds) - if self.config.task_filter: - allowed = {name.strip() for name in self.config.task_filter.split(",")} - tasks = [t for t in tasks if t["task_name"] in allowed] - print(f" Filtered to {len(tasks)} tasks: {sorted(allowed)}") - - # Skip tasks incompatible with the current backend (e.g., QEMU on Modal) - # plus any user-specified skip_tasks - skip = set(MODAL_INCOMPATIBLE_TASKS) if self.config.terminal_backend == "modal" else set() - if self.config.skip_tasks: - skip |= {name.strip() for name in self.config.skip_tasks.split(",")} - if skip: - before = len(tasks) - tasks = [t for t in tasks if t["task_name"] not in skip] - skipped = before - len(tasks) - if skipped > 0: - print(f" Skipped {skipped} incompatible tasks: {sorted(skip & {t['task_name'] for t in ds})}") - - self.all_eval_items = tasks - self.iter = 0 - - # Build category index for per-category metrics - self.category_index: Dict[str, List[int]] = defaultdict(list) - for i, task in enumerate(self.all_eval_items): - self.category_index[task.get("category", "unknown")].append(i) - - # Reward tracking for wandb logging - self.eval_metrics: List[Tuple[str, float]] = [] - - # Streaming JSONL writer -- saves each task's full conversation - # immediately on completion so data is preserved even on Ctrl+C. - # Timestamped filename so each run produces a unique file. - import datetime - log_dir = os.path.join(os.path.dirname(__file__), "logs") - os.makedirs(log_dir, exist_ok=True) - run_ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - self._streaming_path = os.path.join(log_dir, f"samples_{run_ts}.jsonl") - self._streaming_file = open(self._streaming_path, "w", encoding="utf-8") - self._streaming_lock = __import__("threading").Lock() - print(f" Streaming results to: {self._streaming_path}") - - print(f"TB2 ready: {len(self.all_eval_items)} tasks across {len(self.category_index)} categories") - for cat, indices in sorted(self.category_index.items()): - print(f" {cat}: {len(indices)} tasks") - - def _save_result(self, result: Dict[str, Any]): - """Write a single task result to the streaming JSONL file immediately.""" - if not hasattr(self, "_streaming_file") or self._streaming_file.closed: - return - with self._streaming_lock: - self._streaming_file.write(json.dumps(result, ensure_ascii=False, default=str) + "\n") - self._streaming_file.flush() - - # ========================================================================= - # Training pipeline stubs -- NOT used in eval-only mode - # ========================================================================= - # These satisfy the abstract method requirements from HermesAgentBaseEnv. - # The evaluate subcommand calls setup() -> evaluate() directly, bypassing - # the training pipeline entirely. - - async def get_next_item(self): - """Return next item (stub -- not used in eval-only mode).""" - item = self.all_eval_items[self.iter % len(self.all_eval_items)] - self.iter += 1 - return item - - def format_prompt(self, item: Dict[str, Any]) -> str: - """Return the task's instruction as the user prompt.""" - return item["instruction"] - - async def compute_reward(self, item, result, ctx) -> float: - """Compute reward (stub -- actual verification is in rollout_and_score_eval).""" - return 0.0 - - async def collect_trajectories(self, item): - """Collect trajectories (stub -- not used in eval-only mode).""" - return None, [] - - async def score(self, rollout_group_data): - """Score rollouts (stub -- not used in eval-only mode).""" - return None - - # ========================================================================= - # Docker image resolution - # ========================================================================= - - def _resolve_task_image( - self, item: Dict[str, Any], task_name: str - ) -> Tuple[str, Optional[Path]]: - """ - Resolve the Docker image for a task, with fallback to Dockerfile. - - Strategy (mirrors Harbor's approach): - 1. If force_build=True, always build from Dockerfile in environment_tar - 2. If docker_image is available, use the pre-built Docker Hub image (fast) - 3. Otherwise, extract Dockerfile from environment_tar and build (slow) - - Returns: - (modal_image, temp_dir) -- modal_image is a Docker Hub name or a - Dockerfile path. temp_dir is set if we extracted files that need - cleanup later. - """ - docker_image = item.get("docker_image", "") - environment_tar = item.get("environment_tar", "") - - # Fast path: use pre-built Docker Hub image - if docker_image and not self.config.force_build: - logger.info("Task %s: using pre-built image %s", task_name, docker_image) - return docker_image, None - - # Slow path: extract Dockerfile from environment_tar and build - if environment_tar: - task_dir = Path(tempfile.mkdtemp(prefix=f"tb2-{task_name}-")) - _extract_base64_tar(environment_tar, task_dir) - dockerfile_path = task_dir / "Dockerfile" - if dockerfile_path.exists(): - logger.info( - "Task %s: building from Dockerfile (force_build=%s, docker_image=%s)", - task_name, self.config.force_build, bool(docker_image), - ) - return str(dockerfile_path), task_dir - - # Neither available -- fall back to Hub image if force_build was True - if docker_image: - logger.warning( - "Task %s: force_build=True but no environment_tar, " - "falling back to docker_image %s", task_name, docker_image, - ) - return docker_image, None - - return "", None - - # ========================================================================= - # Per-task evaluation -- agent loop + test verification - # ========================================================================= - - async def rollout_and_score_eval(self, eval_item: Dict[str, Any]) -> Dict: - """ - Evaluate a single TB2 task: run the agent loop, then verify with tests. - - This is the core evaluation method. For each task it: - 1. Resolves the Docker image and registers the Modal sandbox override - 2. Runs HermesAgentLoop with terminal + file tools - 3. Uploads the test suite into the sandbox - 4. Executes test.sh and checks the result - 5. Cleans up the sandbox and temp files - - Args: - eval_item: A single TB2 task dict from the dataset - - Returns: - Dict with 'passed' (bool), 'reward' (float), 'task_name' (str), - 'category' (str), and optional debug info - """ - task_name = eval_item.get("task_name", "unknown") - category = eval_item.get("category", "unknown") - task_id = str(uuid.uuid4()) - task_dir = None # Set if we extract a Dockerfile (needs cleanup) - - from tqdm import tqdm - tqdm.write(f" [START] {task_name} (task_id={task_id[:8]})") - task_start = time.time() - - try: - # --- 1. Resolve Docker image --- - modal_image, task_dir = self._resolve_task_image(eval_item, task_name) - if not modal_image: - logger.error("Task %s: no docker_image or environment_tar, skipping", task_name) - return { - "passed": False, "reward": 0.0, - "task_name": task_name, "category": category, - "error": "no_image", - } - - # --- 2. Register per-task image override --- - # Set both modal_image and docker_image so the task image is used - # regardless of which backend is configured. - register_task_env_overrides(task_id, { - "modal_image": modal_image, - "docker_image": modal_image, - "cwd": "/app", - }) - logger.info( - "Task %s: registered image override for task_id %s", - task_name, task_id[:8], - ) - - # --- 3. Resolve tools and build messages --- - tools, valid_names = self._resolve_tools_for_group() - - messages: List[Dict[str, Any]] = [] - if self.config.system_prompt: - messages.append({"role": "system", "content": self.config.system_prompt}) - messages.append({"role": "user", "content": self.format_prompt(eval_item)}) - - # --- 4. Run agent loop --- - # Use ManagedServer (Phase 2) for vLLM/SGLang backends to get - # token-level tracking via /generate. Falls back to direct - # ServerManager (Phase 1) for OpenAI endpoints. - if self._use_managed_server(): - async with self.server.managed_server( - tokenizer=self.tokenizer, - preserve_think_blocks=bool(self.config.thinking_mode), - ) as managed: - agent = HermesAgentLoop( - server=managed, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=task_id, - temperature=self.config.agent_temperature, - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - budget_config=self.config.build_budget_config(), - ) - result = await agent.run(messages) - else: - agent = HermesAgentLoop( - server=self.server, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=task_id, - temperature=self.config.agent_temperature, - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - budget_config=self.config.build_budget_config(), - ) - result = await agent.run(messages) - - # --- 5. Verify -- run test suite in the agent's sandbox --- - # Skip verification if the agent produced no meaningful output - only_system_and_user = all( - msg.get("role") in {"system", "user"} for msg in result.messages - ) - if result.turns_used == 0 or only_system_and_user: - logger.warning( - "Task %s: agent produced no output (turns=%d). Reward=0.", - task_name, result.turns_used, - ) - reward = 0.0 - else: - # Run tests in a thread so the blocking ctx.terminal() calls - # don't freeze the entire event loop (which would stall all - # other tasks, tqdm updates, and timeout timers). - ctx = ToolContext(task_id) - try: - loop = asyncio.get_running_loop() - reward = await loop.run_in_executor( - None, # default thread pool - self._run_tests, eval_item, ctx, task_name, - ) - except Exception as e: - logger.error("Task %s: test verification failed: %s", task_name, e) - reward = 0.0 - finally: - ctx.cleanup() - - passed = reward == 1.0 - status = "PASS" if passed else "FAIL" - elapsed = time.time() - task_start - tqdm.write(f" [{status}] {task_name} (turns={result.turns_used}, {elapsed:.0f}s)") - logger.info( - "Task %s: reward=%.1f, turns=%d, finished=%s", - task_name, reward, result.turns_used, result.finished_naturally, - ) - - out = { - "passed": passed, - "reward": reward, - "task_name": task_name, - "category": category, - "turns_used": result.turns_used, - "finished_naturally": result.finished_naturally, - "messages": result.messages, - } - self._save_result(out) - return out - - except Exception as e: - elapsed = time.time() - task_start - logger.error("Task %s: rollout failed: %s", task_name, e, exc_info=True) - tqdm.write(f" [ERROR] {task_name}: {e} ({elapsed:.0f}s)") - out = { - "passed": False, "reward": 0.0, - "task_name": task_name, "category": category, - "error": str(e), - } - self._save_result(out) - return out - - finally: - # --- Cleanup: clear overrides, sandbox, and temp files --- - clear_task_env_overrides(task_id) - try: - cleanup_vm(task_id) - except Exception as e: - logger.debug("VM cleanup for %s: %s", task_id[:8], e) - if task_dir and task_dir.exists(): - shutil.rmtree(task_dir, ignore_errors=True) - - def _run_tests( - self, item: Dict[str, Any], ctx: ToolContext, task_name: str - ) -> float: - """ - Upload and execute the test suite in the agent's sandbox, then - download the verifier output locally to read the reward. - - Follows Harbor's verification pattern: - 1. Upload tests/ directory into the sandbox - 2. Execute test.sh inside the sandbox - 3. Download /logs/verifier/ directory to a local temp dir - 4. Read reward.txt locally with native Python I/O - - Downloading locally avoids issues with the file_read tool on - the Modal VM and matches how Harbor handles verification. - - TB2 test scripts (test.sh) typically: - 1. Install pytest via uv/pip - 2. Run pytest against the test files in /tests/ - 3. Write results to /logs/verifier/reward.txt - - Args: - item: The TB2 task dict (contains tests_tar, test_sh) - ctx: ToolContext scoped to this task's sandbox - task_name: For logging - - Returns: - 1.0 if tests pass, 0.0 otherwise - """ - tests_tar = item.get("tests_tar", "") - test_sh = item.get("test_sh", "") - - if not test_sh: - logger.warning("Task %s: no test_sh content, reward=0", task_name) - return 0.0 - - # Create required directories in the sandbox - ctx.terminal("mkdir -p /tests /logs/verifier") - - # Upload test files into the sandbox (binary-safe via base64) - if tests_tar: - tests_temp = Path(tempfile.mkdtemp(prefix=f"tb2-tests-{task_name}-")) - try: - _extract_base64_tar(tests_tar, tests_temp) - ctx.upload_dir(str(tests_temp), "/tests") - except Exception as e: - logger.warning("Task %s: failed to upload test files: %s", task_name, e) - finally: - shutil.rmtree(tests_temp, ignore_errors=True) - - # Write the test runner script (test.sh) - ctx.write_file("/tests/test.sh", test_sh) - ctx.terminal("chmod +x /tests/test.sh") - - # Execute the test suite - logger.info( - "Task %s: running test suite (timeout=%ds)", - task_name, self.config.test_timeout, - ) - test_result = ctx.terminal( - "bash /tests/test.sh", - timeout=self.config.test_timeout, - ) - - exit_code = test_result.get("exit_code", -1) - output = test_result.get("output", "") - - # Download the verifier output directory locally, then read reward.txt - # with native Python I/O. This avoids issues with file_read on the - # Modal VM and matches Harbor's verification pattern. - reward = 0.0 - local_verifier_dir = Path(tempfile.mkdtemp(prefix=f"tb2-verifier-{task_name}-")) - try: - ctx.download_dir("/logs/verifier", str(local_verifier_dir)) - - reward_file = local_verifier_dir / "reward.txt" - if reward_file.exists() and reward_file.stat().st_size > 0: - content = reward_file.read_text().strip() - if content == "1": - reward = 1.0 - elif content == "0": - reward = 0.0 - else: - # Unexpected content -- try parsing as float - try: - reward = float(content) - except (ValueError, TypeError): - logger.warning( - "Task %s: reward.txt content unexpected (%r), " - "falling back to exit_code=%d", - task_name, content, exit_code, - ) - reward = 1.0 if exit_code == 0 else 0.0 - else: - # reward.txt not written -- fall back to exit code - logger.warning( - "Task %s: reward.txt not found after download, " - "falling back to exit_code=%d", - task_name, exit_code, - ) - reward = 1.0 if exit_code == 0 else 0.0 - except Exception as e: - logger.warning( - "Task %s: failed to download verifier dir: %s, " - "falling back to exit_code=%d", - task_name, e, exit_code, - ) - reward = 1.0 if exit_code == 0 else 0.0 - finally: - shutil.rmtree(local_verifier_dir, ignore_errors=True) - - # Log test output for debugging failures - if reward == 0.0: - output_preview = output[-500:] if output else "(no output)" - logger.info( - "Task %s: FAIL (exit_code=%d)\n%s", - task_name, exit_code, output_preview, - ) - - return reward - - # ========================================================================= - # Evaluate -- main entry point for the eval subcommand - # ========================================================================= - - async def _eval_with_timeout(self, item: Dict[str, Any]) -> Dict: - """ - Wrap rollout_and_score_eval with a per-task wall-clock timeout. - - If the task exceeds task_timeout seconds, it's automatically scored - as FAIL. This prevents any single task from hanging indefinitely. - """ - task_name = item.get("task_name", "unknown") - category = item.get("category", "unknown") - try: - return await asyncio.wait_for( - self.rollout_and_score_eval(item), - timeout=self.config.task_timeout, - ) - except asyncio.TimeoutError: - from tqdm import tqdm - elapsed = self.config.task_timeout - tqdm.write(f" [TIMEOUT] {task_name} (exceeded {elapsed}s wall-clock limit)") - logger.error("Task %s: wall-clock timeout after %ds", task_name, elapsed) - out = { - "passed": False, "reward": 0.0, - "task_name": task_name, "category": category, - "error": f"timeout ({elapsed}s)", - } - self._save_result(out) - return out - - async def evaluate(self, *args, **kwargs) -> None: - """ - Run Terminal-Bench 2.0 evaluation over all tasks. - - This is the main entry point when invoked via: - python environments/terminalbench2_env.py evaluate - - Runs all tasks through rollout_and_score_eval() via asyncio.gather() - (same pattern as GPQA and other Atropos eval envs). Each task is - wrapped with a wall-clock timeout so hung tasks auto-fail. - - Suppresses noisy Modal/terminal output (HERMES_QUIET) so the tqdm - bar stays visible. - """ - start_time = time.time() - - # Route all logging through tqdm.write() so the progress bar stays - # pinned at the bottom while log lines scroll above it. - from tqdm import tqdm - - class _TqdmHandler(logging.Handler): - def emit(self, record): - try: - tqdm.write(self.format(record)) - except Exception: - self.handleError(record) - - handler = _TqdmHandler() - handler.setFormatter(logging.Formatter( - "%(asctime)s [%(name)s] %(levelname)s: %(message)s", - datefmt="%H:%M:%S", - )) - root = logging.getLogger() - root.handlers = [handler] # Replace any existing handlers - root.setLevel(logging.INFO) - - # Silence noisy third-party loggers that flood the output - logging.getLogger("httpx").setLevel(logging.WARNING) # Every HTTP request - logging.getLogger("openai").setLevel(logging.WARNING) # OpenAI client retries - logging.getLogger("rex-deploy").setLevel(logging.WARNING) # Swerex deployment - logging.getLogger("rex_image_builder").setLevel(logging.WARNING) # Image builds - - print(f"\n{'='*60}") - print("Starting Terminal-Bench 2.0 Evaluation") - print(f"{'='*60}") - print(f" Dataset: {self.config.dataset_name}") - print(f" Total tasks: {len(self.all_eval_items)}") - print(f" Max agent turns: {self.config.max_agent_turns}") - print(f" Task timeout: {self.config.task_timeout}s") - print(f" Terminal backend: {self.config.terminal_backend}") - print(f" Tool thread pool: {self.config.tool_pool_size}") - print(f" Terminal timeout: {self.config.terminal_timeout}s/cmd") - print(f" Terminal lifetime: {self.config.terminal_lifetime}s (auto: task_timeout + 120)") - print(f" Max concurrent tasks: {self.config.max_concurrent_tasks}") - print(f"{'='*60}\n") - - # Semaphore to limit concurrent Modal sandbox creations. - # Without this, all 86 tasks fire simultaneously, each creating a Modal - # sandbox via asyncio.run() inside a thread pool worker. Modal's blocking - # calls (App.lookup, etc.) deadlock when too many are created at once. - semaphore = asyncio.Semaphore(self.config.max_concurrent_tasks) - - async def _eval_with_semaphore(item): - async with semaphore: - return await self._eval_with_timeout(item) - - # Fire all tasks with wall-clock timeout, track live accuracy on the bar - total_tasks = len(self.all_eval_items) - eval_tasks = [ - asyncio.ensure_future(_eval_with_semaphore(item)) - for item in self.all_eval_items - ] - - results = [] - passed_count = 0 - pbar = tqdm(total=total_tasks, desc="Evaluating TB2", dynamic_ncols=True) - try: - for coro in asyncio.as_completed(eval_tasks): - result = await coro - results.append(result) - if result and result.get("passed"): - passed_count += 1 - done = len(results) - pct = (passed_count / done * 100) if done else 0 - pbar.set_postfix_str(f"pass={passed_count}/{done} ({pct:.1f}%)") - pbar.update(1) - except (KeyboardInterrupt, asyncio.CancelledError): - pbar.close() - print(f"\n\nInterrupted! Cleaning up {len(eval_tasks)} tasks...") - # Cancel all pending tasks - for task in eval_tasks: - task.cancel() - # Let cancellations propagate (finally blocks run cleanup_vm) - await asyncio.gather(*eval_tasks, return_exceptions=True) - # Belt-and-suspenders: clean up any remaining sandboxes - from tools.terminal_tool import cleanup_all_environments - cleanup_all_environments() - print("All sandboxes cleaned up.") - return - finally: - pbar.close() - - end_time = time.time() - - # Filter out None results (shouldn't happen, but be safe) - valid_results = [r for r in results if r is not None] - - if not valid_results: - print("Warning: No valid evaluation results obtained") - return - - # ---- Compute metrics ---- - total = len(valid_results) - passed = sum(1 for r in valid_results if r.get("passed")) - overall_pass_rate = passed / total if total > 0 else 0.0 - - # Per-category breakdown - cat_results: Dict[str, List[Dict]] = defaultdict(list) - for r in valid_results: - cat_results[r.get("category", "unknown")].append(r) - - # Build metrics dict - eval_metrics = { - "eval/pass_rate": overall_pass_rate, - "eval/total_tasks": total, - "eval/passed_tasks": passed, - "eval/evaluation_time_seconds": end_time - start_time, - } - - # Per-category metrics - for category, cat_items in sorted(cat_results.items()): - cat_passed = sum(1 for r in cat_items if r.get("passed")) - cat_total = len(cat_items) - cat_pass_rate = cat_passed / cat_total if cat_total > 0 else 0.0 - cat_key = category.replace(" ", "_").replace("-", "_").lower() - eval_metrics[f"eval/pass_rate_{cat_key}"] = cat_pass_rate - - # Store metrics for wandb_log - self.eval_metrics = list(eval_metrics.items()) - - # ---- Print summary ---- - print(f"\n{'='*60}") - print("Terminal-Bench 2.0 Evaluation Results") - print(f"{'='*60}") - print(f"Overall Pass Rate: {overall_pass_rate:.4f} ({passed}/{total})") - print(f"Evaluation Time: {end_time - start_time:.1f} seconds") - - print("\nCategory Breakdown:") - for category, cat_items in sorted(cat_results.items()): - cat_passed = sum(1 for r in cat_items if r.get("passed")) - cat_total = len(cat_items) - cat_rate = cat_passed / cat_total if cat_total > 0 else 0.0 - print(f" {category}: {cat_rate:.1%} ({cat_passed}/{cat_total})") - - # Print individual task results - print("\nTask Results:") - for r in sorted(valid_results, key=lambda x: x.get("task_name", "")): - status = "PASS" if r.get("passed") else "FAIL" - turns = r.get("turns_used", "?") - error = r.get("error", "") - extra = f" (error: {error})" if error else "" - print(f" [{status}] {r['task_name']} (turns={turns}){extra}") - - print(f"{'='*60}\n") - - # Build sample records for evaluate_log (includes full conversations) - samples = [ - { - "task_name": r.get("task_name"), - "category": r.get("category"), - "passed": r.get("passed"), - "reward": r.get("reward"), - "turns_used": r.get("turns_used"), - "error": r.get("error"), - "messages": r.get("messages"), - } - for r in valid_results - ] - - # Log evaluation results - try: - await self.evaluate_log( - metrics=eval_metrics, - samples=samples, - start_time=start_time, - end_time=end_time, - generation_parameters={ - "temperature": self.config.agent_temperature, - "max_tokens": self.config.max_token_length, - "max_agent_turns": self.config.max_agent_turns, - "terminal_backend": self.config.terminal_backend, - }, - ) - except Exception as e: - print(f"Error logging evaluation results: {e}") - - # Close streaming file - if hasattr(self, "_streaming_file") and not self._streaming_file.closed: - self._streaming_file.close() - print(f" Live results saved to: {self._streaming_path}") - - # Kill all remaining sandboxes. Timed-out tasks leave orphaned thread - # pool workers still executing commands -- cleanup_all stops them. - from tools.terminal_tool import cleanup_all_environments - print("\nCleaning up all sandboxes...") - cleanup_all_environments() - - # Shut down the tool thread pool so orphaned workers from timed-out - # tasks are killed immediately instead of retrying against dead - # sandboxes and spamming the console with TimeoutError warnings. - from environments.agent_loop import _tool_executor - _tool_executor.shutdown(wait=False, cancel_futures=True) - print("Done.") - - # ========================================================================= - # Wandb logging - # ========================================================================= - - async def wandb_log(self, wandb_metrics: Optional[Dict] = None): - """Log TB2-specific metrics to wandb.""" - if wandb_metrics is None: - wandb_metrics = {} - - # Add stored eval metrics - for metric_name, metric_value in self.eval_metrics: - wandb_metrics[metric_name] = metric_value - self.eval_metrics = [] - - await super().wandb_log(wandb_metrics) - - -if __name__ == "__main__": - TerminalBench2EvalEnv.cli() diff --git a/environments/benchmarks/yc_bench/README.md b/environments/benchmarks/yc_bench/README.md deleted file mode 100644 index 7a8aba7874d..00000000000 --- a/environments/benchmarks/yc_bench/README.md +++ /dev/null @@ -1,115 +0,0 @@ -# YC-Bench: Long-Horizon Agent Benchmark - -[YC-Bench](https://github.com/collinear-ai/yc-bench) by [Collinear AI](https://collinear.ai/) is a deterministic, long-horizon benchmark that tests LLM agents' ability to act as a tech startup CEO. The agent manages a simulated company over 1-3 years, making compounding decisions about resource allocation, cash flow, task management, and prestige specialisation across 4 skill domains. - -Unlike TerminalBench2 (which evaluates per-task coding ability with binary pass/fail), YC-Bench measures **long-term strategic coherence** — whether an agent can maintain consistent strategy, manage compounding consequences, and adapt plans over hundreds of turns. - -## Setup - -```bash -# Install yc-bench (optional dependency) -pip install "hermes-agent[yc-bench]" - -# Or install from source -git clone https://github.com/collinear-ai/yc-bench -cd yc-bench && pip install -e . - -# Verify -yc-bench --help -``` - -## Running - -```bash -# From the repo root: -bash environments/benchmarks/yc_bench/run_eval.sh - -# Or directly: -python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \ - --config environments/benchmarks/yc_bench/default.yaml - -# Override model: -bash environments/benchmarks/yc_bench/run_eval.sh \ - --openai.model_name anthropic/claude-opus-4-20250514 - -# Quick single-preset test: -bash environments/benchmarks/yc_bench/run_eval.sh \ - --env.presets '["fast_test"]' --env.seeds '[1]' -``` - -## How It Works - -### Architecture - -``` -HermesAgentLoop (our agent) - -> terminal tool -> subprocess("yc-bench company status") -> JSON output - -> terminal tool -> subprocess("yc-bench task accept --task-id X") -> JSON - -> terminal tool -> subprocess("yc-bench sim resume") -> JSON (advance time) - -> ... (100-500 turns per run) -``` - -The environment initialises the simulation via `yc-bench sim init` (NOT `yc-bench run`, which would start yc-bench's own built-in agent loop). Our `HermesAgentLoop` then drives all interaction through CLI commands. - -### Simulation Mechanics - -- **4 skill domains**: research, inference, data_environment, training -- **Prestige system** (1.0-10.0): Gates access to higher-paying tasks -- **Employee management**: Junior/Mid/Senior with domain-specific skill rates -- **Throughput splitting**: `effective_rate = base_rate / N` active tasks per employee -- **Financial pressure**: Monthly payroll, bankruptcy = game over -- **Deterministic**: SHA256-based RNG — same seed + preset = same world - -### Difficulty Presets - -| Preset | Employees | Tasks | Focus | -|-----------|-----------|-------|-------| -| tutorial | 3 | 50 | Basic loop mechanics | -| easy | 5 | 100 | Throughput awareness | -| **medium**| 5 | 150 | Prestige climbing + domain specialisation | -| **hard** | 7 | 200 | Precise ETA reasoning | -| nightmare | 8 | 300 | Sustained perfection under payroll pressure | -| fast_test | (varies) | (varies) | Quick validation (~50 turns) | - -Default eval runs **fast_test + medium + hard** × 3 seeds = 9 runs. - -### Scoring - -``` -composite = 0.5 × survival + 0.5 × normalised_funds -``` - -- **Survival** (binary): Did the company avoid bankruptcy? -- **Normalised funds** (0.0-1.0): Log-scale relative to initial $250K capital - -## Configuration - -Key fields in `default.yaml`: - -| Field | Default | Description | -|-------|---------|-------------| -| `presets` | `["fast_test", "medium", "hard"]` | Which presets to evaluate | -| `seeds` | `[1, 2, 3]` | RNG seeds per preset | -| `max_agent_turns` | 200 | Max LLM calls per run | -| `run_timeout` | 3600 | Wall-clock timeout per run (seconds) | -| `survival_weight` | 0.5 | Weight of survival in composite score | -| `funds_weight` | 0.5 | Weight of normalised funds in composite | -| `horizon_years` | null | Override horizon (null = auto from preset) | - -## Cost & Time Estimates - -Each run is 100-500 LLM turns. Approximate costs per run at typical API rates: - -| Preset | Turns | Time | Est. Cost | -|--------|-------|------|-----------| -| fast_test | ~50 | 5-10 min | $1-5 | -| medium | ~200 | 20-40 min | $5-15 | -| hard | ~300 | 30-60 min | $10-25 | - -Full default eval (9 runs): ~3-6 hours, $50-200 depending on model. - -## References - -- [collinear-ai/yc-bench](https://github.com/collinear-ai/yc-bench) — Official repository -- [Collinear AI](https://collinear.ai/) — Company behind yc-bench -- [TerminalBench2](../terminalbench_2/) — Per-task coding benchmark (complementary) diff --git a/environments/benchmarks/yc_bench/__init__.py b/environments/benchmarks/yc_bench/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/environments/benchmarks/yc_bench/default.yaml b/environments/benchmarks/yc_bench/default.yaml deleted file mode 100644 index 4396c00ab94..00000000000 --- a/environments/benchmarks/yc_bench/default.yaml +++ /dev/null @@ -1,43 +0,0 @@ -# YC-Bench Evaluation -- Default Configuration -# -# Long-horizon agent benchmark: agent plays CEO of an AI startup over -# a simulated 1-3 year run, interacting via yc-bench CLI subcommands. -# -# Requires: pip install "hermes-agent[yc-bench]" -# -# Usage: -# python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \ -# --config environments/benchmarks/yc_bench/default.yaml -# -# # Override model: -# python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \ -# --config environments/benchmarks/yc_bench/default.yaml \ -# --openai.model_name anthropic/claude-opus-4-20250514 - -env: - enabled_toolsets: ["terminal"] - max_agent_turns: 200 - max_token_length: 32000 - agent_temperature: 0.0 - terminal_backend: "local" - terminal_timeout: 60 - presets: ["fast_test", "medium", "hard"] - seeds: [1, 2, 3] - run_timeout: 3600 # 60 min wall-clock per run, auto-FAIL if exceeded - survival_weight: 0.5 # weight of binary survival in composite score - funds_weight: 0.5 # weight of normalised final funds in composite score - db_dir: "/tmp/yc_bench_dbs" - company_name: "BenchCo" - start_date: "01/01/2025" # MM/DD/YYYY (yc-bench convention) - tokenizer_name: "NousResearch/Hermes-3-Llama-3.1-8B" - use_wandb: true - wandb_name: "yc-bench" - ensure_scores_are_not_same: false - data_dir_to_save_evals: "environments/benchmarks/evals/yc-bench" - -openai: - base_url: "https://openrouter.ai/api/v1" - model_name: "anthropic/claude-sonnet-4.6" - server_type: "openai" - health_check: false - # api_key loaded from OPENROUTER_API_KEY in .env diff --git a/environments/benchmarks/yc_bench/run_eval.sh b/environments/benchmarks/yc_bench/run_eval.sh deleted file mode 100755 index 0d793f53d54..00000000000 --- a/environments/benchmarks/yc_bench/run_eval.sh +++ /dev/null @@ -1,34 +0,0 @@ -#!/bin/bash - -# YC-Bench Evaluation -# -# Requires: pip install "hermes-agent[yc-bench]" -# -# Run from repo root: -# bash environments/benchmarks/yc_bench/run_eval.sh -# -# Override model: -# bash environments/benchmarks/yc_bench/run_eval.sh \ -# --openai.model_name anthropic/claude-opus-4-20250514 -# -# Run a single preset: -# bash environments/benchmarks/yc_bench/run_eval.sh \ -# --env.presets '["fast_test"]' --env.seeds '[1]' - -set -euo pipefail - -mkdir -p logs evals/yc-bench -LOG_FILE="logs/yc_bench_$(date +%Y%m%d_%H%M%S).log" - -echo "YC-Bench Evaluation" -echo "Log: $LOG_FILE" -echo "" - -PYTHONUNBUFFERED=1 LOGLEVEL="${LOGLEVEL:-INFO}" \ - python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \ - --config environments/benchmarks/yc_bench/default.yaml \ - "$@" \ - 2>&1 | tee "$LOG_FILE" - -echo "" -echo "Log saved to: $LOG_FILE" diff --git a/environments/benchmarks/yc_bench/yc_bench_env.py b/environments/benchmarks/yc_bench/yc_bench_env.py deleted file mode 100644 index 6e7be2c899b..00000000000 --- a/environments/benchmarks/yc_bench/yc_bench_env.py +++ /dev/null @@ -1,848 +0,0 @@ -""" -YCBenchEvalEnv -- YC-Bench Long-Horizon Agent Benchmark Environment - -Evaluates agentic LLMs on YC-Bench: a deterministic, long-horizon benchmark -where the agent acts as CEO of an AI startup over a simulated 1-3 year run. -The agent manages cash flow, employees, tasks, and prestige across 4 domains, -interacting exclusively via CLI subprocess calls against a SQLite-backed -discrete-event simulation. - -Unlike TerminalBench2 (per-task binary pass/fail), YC-Bench measures sustained -multi-turn strategic coherence -- whether an agent can manage compounding -decisions over hundreds of turns without going bankrupt. - -This is an eval-only environment. Run via: - - python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \ - --config environments/benchmarks/yc_bench/default.yaml - -The evaluate flow: - 1. setup() -- Verifies yc-bench installed, builds eval matrix (preset x seed) - 2. evaluate() -- Iterates over all runs sequentially through: - a. rollout_and_score_eval() -- Per-run agent loop - - Initialises a fresh yc-bench simulation via `sim init` (NOT `run`) - - Runs HermesAgentLoop with terminal tool only - - Reads final SQLite DB to extract score - - Returns survival (0/1) + normalised funds score - b. Aggregates per-preset and overall metrics - c. Logs results via evaluate_log() and wandb - -Key features: - - CLI-only interface: agent calls yc-bench subcommands via terminal tool - - Deterministic: same seed + preset = same world (SHA256-based RNG) - - Multi-dimensional scoring: survival + normalised final funds - - Per-preset difficulty breakdown in results - - Isolated SQLite DB per run (no cross-run state leakage) - -Requires: pip install hermes-agent[yc-bench] -""" - -import asyncio -import datetime -import json -import logging -import math -import os -import sqlite3 -import subprocess -import sys -import threading -import time -import uuid -from collections import defaultdict -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - -_repo_root = Path(__file__).resolve().parent.parent.parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -from pydantic import Field - -from atroposlib.envs.base import EvalHandlingEnum -from atroposlib.envs.server_handling.server_manager import APIServerConfig - -from environments.agent_loop import HermesAgentLoop -from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig - -logger = logging.getLogger(__name__) - -# ============================================================================= -# System prompt -# ============================================================================= - -YC_BENCH_SYSTEM_PROMPT = """\ -You are the autonomous CEO of an early-stage AI startup in a deterministic -business simulation. You manage the company exclusively through the `yc-bench` -CLI tool. Your primary goal is to **survive** until the simulation horizon ends -without going bankrupt, while **maximising final funds**. - -## Simulation Mechanics - -- **Funds**: You start with $250,000 seed capital. Revenue comes from completing - tasks. Rewards scale with your prestige: `base × (1 + scale × (prestige − 1))`. -- **Domains**: There are 4 skill domains: **research**, **inference**, - **data_environment**, and **training**. Each has its own prestige level - (1.0-10.0). Higher prestige unlocks better-paying tasks. -- **Employees**: You have employees (Junior/Mid/Senior) with domain-specific - skill rates. **Throughput splits**: `effective_rate = base_rate / N` where N - is the number of active tasks assigned to that employee. Focus beats breadth. -- **Payroll**: Deducted automatically on the first business day of each month. - Running out of funds = bankruptcy = game over. -- **Time**: The simulation runs on business days (Mon-Fri), 09:00-18:00. - Time only advances when you call `yc-bench sim resume`. - -## Task Lifecycle - -1. Browse market tasks with `market browse` -2. Accept a task with `task accept` (this sets its deadline) -3. Assign employees with `task assign` -4. Dispatch with `task dispatch` to start work -5. Call `sim resume` to advance time and let employees make progress -6. Tasks complete when all domain requirements are fulfilled - -**Penalties for failure vary by difficulty preset.** Completing a task on time -earns full reward + prestige gain. Missing a deadline or cancelling a task -incurs prestige penalties -- cancelling is always more costly than letting a -task fail, so cancel only as a last resort. - -## CLI Commands - -### Observe -- `yc-bench company status` -- funds, prestige, runway -- `yc-bench employee list` -- skills, salary, active tasks -- `yc-bench market browse [--domain D] [--required-prestige-lte N]` -- available tasks -- `yc-bench task list [--status active|planned]` -- your tasks -- `yc-bench task inspect --task-id UUID` -- progress, deadline, assignments -- `yc-bench finance ledger [--category monthly_payroll|task_reward]` -- transaction history -- `yc-bench report monthly` -- monthly P&L - -### Act -- `yc-bench task accept --task-id UUID` -- accept from market -- `yc-bench task assign --task-id UUID --employee-id UUID` -- assign employee -- `yc-bench task dispatch --task-id UUID` -- start work (needs >=1 assignment) -- `yc-bench task cancel --task-id UUID --reason "text"` -- cancel (prestige penalty) -- `yc-bench sim resume` -- advance simulation clock - -### Memory (persists across context truncation) -- `yc-bench scratchpad read` -- read your persistent notes -- `yc-bench scratchpad write --content "text"` -- overwrite notes -- `yc-bench scratchpad append --content "text"` -- append to notes -- `yc-bench scratchpad clear` -- clear notes - -## Strategy Guidelines - -1. **Specialise in 2-3 domains** to climb the prestige ladder faster and unlock - high-reward tasks. Don't spread thin across all 4 domains early on. -2. **Focus employees** -- assigning one employee to many tasks halves their - throughput per additional task. Keep assignments concentrated. -3. **Use the scratchpad** to track your strategy, upcoming deadlines, and - employee assignments. This persists even if conversation context is truncated. -4. **Monitor runway** -- always know how many months of payroll you can cover. - Accept high-reward tasks before payroll dates. -5. **Don't over-accept** -- taking too many tasks and missing deadlines cascades - into prestige loss, locking you out of profitable contracts. -6. Use `finance ledger` and `report monthly` to track revenue trends. - -## Your Turn - -Each turn: -1. Call `yc-bench company status` and `yc-bench task list` to orient yourself. -2. Check for completed tasks and pending deadlines. -3. Browse market for profitable tasks within your prestige level. -4. Accept, assign, and dispatch tasks strategically. -5. Call `yc-bench sim resume` to advance time. -6. Repeat until the simulation ends. - -Think step by step before acting.""" - -# Starting funds in cents ($250,000) -INITIAL_FUNDS_CENTS = 25_000_000 - -# Default horizon per preset (years) -_PRESET_HORIZONS = { - "tutorial": 1, - "easy": 1, - "medium": 1, - "hard": 1, - "nightmare": 1, - "fast_test": 1, - "default": 3, - "high_reward": 1, -} - - -# ============================================================================= -# Configuration -# ============================================================================= - -class YCBenchEvalConfig(HermesAgentEnvConfig): - """ - Configuration for the YC-Bench evaluation environment. - - Extends HermesAgentEnvConfig with YC-Bench-specific settings for - preset selection, seed control, scoring, and simulation parameters. - """ - - presets: List[str] = Field( - default=["fast_test", "medium", "hard"], - description="YC-Bench preset names to evaluate.", - ) - seeds: List[int] = Field( - default=[1, 2, 3], - description="Random seeds -- each preset x seed = one run.", - ) - run_timeout: int = Field( - default=3600, - description="Maximum wall-clock seconds per run. Default 60 minutes.", - ) - survival_weight: float = Field( - default=0.5, - description="Weight of survival (0/1) in composite score.", - ) - funds_weight: float = Field( - default=0.5, - description="Weight of normalised final funds in composite score.", - ) - db_dir: str = Field( - default="/tmp/yc_bench_dbs", - description="Directory for per-run SQLite databases.", - ) - horizon_years: Optional[int] = Field( - default=None, - description=( - "Simulation horizon in years. If None (default), inferred from " - "preset name (1 year for most, 3 for 'default')." - ), - ) - company_name: str = Field( - default="BenchCo", - description="Name of the simulated company.", - ) - start_date: str = Field( - default="01/01/2025", - description="Simulation start date in MM/DD/YYYY format (yc-bench convention).", - ) - - -# ============================================================================= -# Scoring helpers -# ============================================================================= - -def _read_final_score(db_path: str) -> Dict[str, Any]: - """ - Read final game state from a YC-Bench SQLite database. - - Returns dict with final_funds_cents (int), survived (bool), - terminal_reason (str). - - Note: yc-bench table names are plural -- 'companies' not 'company', - 'sim_events' not 'simulation_log'. - """ - if not os.path.exists(db_path): - logger.warning("DB not found at %s", db_path) - return { - "final_funds_cents": 0, - "survived": False, - "terminal_reason": "db_missing", - } - - conn = None - try: - conn = sqlite3.connect(db_path) - cur = conn.cursor() - - # Read final funds from the 'companies' table - cur.execute("SELECT funds_cents FROM companies LIMIT 1") - row = cur.fetchone() - funds = row[0] if row else 0 - - # Determine terminal reason from 'sim_events' table - terminal_reason = "unknown" - try: - cur.execute( - "SELECT event_type FROM sim_events " - "WHERE event_type IN ('bankruptcy', 'horizon_end') " - "ORDER BY scheduled_at DESC LIMIT 1" - ) - event_row = cur.fetchone() - if event_row: - terminal_reason = event_row[0] - except sqlite3.OperationalError: - # Table may not exist if simulation didn't progress - pass - - survived = funds >= 0 and terminal_reason != "bankruptcy" - return { - "final_funds_cents": funds, - "survived": survived, - "terminal_reason": terminal_reason, - } - - except Exception as e: - logger.error("Failed to read DB %s: %s", db_path, e) - return { - "final_funds_cents": 0, - "survived": False, - "terminal_reason": f"db_error: {e}", - } - finally: - if conn: - conn.close() - - -def _compute_composite_score( - final_funds_cents: int, - survived: bool, - survival_weight: float = 0.5, - funds_weight: float = 0.5, - initial_funds_cents: int = INITIAL_FUNDS_CENTS, -) -> float: - """ - Compute composite score from survival and final funds. - - Score = survival_weight * survival_score - + funds_weight * normalised_funds_score - - Normalised funds uses log-scale relative to initial capital: - - funds <= 0: 0.0 - - funds == initial: ~0.15 - - funds == 10x: ~0.52 - - funds == 100x: 1.0 - """ - survival_score = 1.0 if survived else 0.0 - - if final_funds_cents <= 0: - funds_score = 0.0 - else: - max_ratio = 100.0 - ratio = final_funds_cents / max(initial_funds_cents, 1) - funds_score = min(math.log1p(ratio) / math.log1p(max_ratio), 1.0) - - return survival_weight * survival_score + funds_weight * funds_score - - -# ============================================================================= -# Main Environment -# ============================================================================= - -class YCBenchEvalEnv(HermesAgentBaseEnv): - """ - YC-Bench long-horizon agent benchmark environment (eval-only). - - Each eval item is a (preset, seed) pair. The environment initialises the - simulation via ``yc-bench sim init`` (NOT ``yc-bench run`` which would start - a competing built-in agent loop). The HermesAgentLoop then drives the - interaction by calling individual yc-bench CLI commands via the terminal tool. - - After the agent loop ends, the SQLite DB is read to extract the final score. - - Scoring: - composite = 0.5 * survival + 0.5 * normalised_funds - """ - - name = "yc-bench" - env_config_cls = YCBenchEvalConfig - - @classmethod - def config_init(cls) -> Tuple[YCBenchEvalConfig, List[APIServerConfig]]: - env_config = YCBenchEvalConfig( - enabled_toolsets=["terminal"], - disabled_toolsets=None, - distribution=None, - max_agent_turns=200, - max_token_length=32000, - agent_temperature=0.0, - system_prompt=YC_BENCH_SYSTEM_PROMPT, - terminal_backend="local", - terminal_timeout=60, - presets=["fast_test", "medium", "hard"], - seeds=[1, 2, 3], - run_timeout=3600, - survival_weight=0.5, - funds_weight=0.5, - db_dir="/tmp/yc_bench_dbs", - eval_handling=EvalHandlingEnum.STOP_TRAIN, - group_size=1, - steps_per_eval=1, - total_steps=1, - tokenizer_name="NousResearch/Hermes-3-Llama-3.1-8B", - use_wandb=True, - wandb_name="yc-bench", - ensure_scores_are_not_same=False, - ) - - server_configs = [ - APIServerConfig( - base_url="https://openrouter.ai/api/v1", - model_name="anthropic/claude-sonnet-4.6", - server_type="openai", - api_key=os.getenv("OPENROUTER_API_KEY", ""), - health_check=False, - ) - ] - - return env_config, server_configs - - # ========================================================================= - # Setup - # ========================================================================= - - async def setup(self): - """Verify yc-bench is installed and build the eval matrix.""" - # Verify yc-bench CLI is available - try: - result = subprocess.run( - ["yc-bench", "--help"], capture_output=True, text=True, timeout=10 - ) - if result.returncode != 0: - raise FileNotFoundError - except (FileNotFoundError, subprocess.TimeoutExpired): - raise RuntimeError( - "yc-bench CLI not found. Install with:\n" - ' pip install "hermes-agent[yc-bench]"\n' - "Or: git clone https://github.com/collinear-ai/yc-bench " - "&& cd yc-bench && pip install -e ." - ) - print("yc-bench CLI verified.") - - # Build eval matrix: preset x seed - self.all_eval_items = [ - {"preset": preset, "seed": seed} - for preset in self.config.presets - for seed in self.config.seeds - ] - self.iter = 0 - - os.makedirs(self.config.db_dir, exist_ok=True) - self.eval_metrics: List[Tuple[str, float]] = [] - - # Streaming JSONL log for crash-safe result persistence - log_dir = os.path.join(os.path.dirname(__file__), "logs") - os.makedirs(log_dir, exist_ok=True) - run_ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - self._streaming_path = os.path.join(log_dir, f"samples_{run_ts}.jsonl") - self._streaming_file = open(self._streaming_path, "w", encoding="utf-8") - self._streaming_lock = threading.Lock() - - print(f"\nYC-Bench eval matrix: {len(self.all_eval_items)} runs") - for item in self.all_eval_items: - print(f" preset={item['preset']!r} seed={item['seed']}") - print(f"Streaming results to: {self._streaming_path}\n") - - def _save_result(self, result: Dict[str, Any]): - """Write a single run result to the streaming JSONL file immediately.""" - if not hasattr(self, "_streaming_file") or self._streaming_file.closed: - return - with self._streaming_lock: - self._streaming_file.write( - json.dumps(result, ensure_ascii=False, default=str) + "\n" - ) - self._streaming_file.flush() - - # ========================================================================= - # Training pipeline stubs (eval-only -- not used) - # ========================================================================= - - async def get_next_item(self): - item = self.all_eval_items[self.iter % len(self.all_eval_items)] - self.iter += 1 - return item - - def format_prompt(self, item: Dict[str, Any]) -> str: - preset = item["preset"] - seed = item["seed"] - return ( - f"A new YC-Bench simulation has been initialized " - f"(preset='{preset}', seed={seed}).\n" - f"Your company '{self.config.company_name}' is ready.\n\n" - "Begin by calling:\n" - "1. `yc-bench company status` -- see your starting funds and prestige\n" - "2. `yc-bench employee list` -- see your team and their skills\n" - "3. `yc-bench market browse --required-prestige-lte 1` -- find tasks " - "you can take\n\n" - "Then accept 2-3 tasks, assign employees, dispatch them, and call " - "`yc-bench sim resume` to advance time. Repeat this loop until the " - "simulation ends (horizon reached or bankruptcy)." - ) - - async def compute_reward(self, item, result, ctx) -> float: - return 0.0 - - async def collect_trajectories(self, item): - return None, [] - - async def score(self, rollout_group_data): - return None - - # ========================================================================= - # Per-run evaluation - # ========================================================================= - - async def rollout_and_score_eval(self, eval_item: Dict[str, Any]) -> Dict: - """ - Evaluate a single (preset, seed) run. - - 1. Sets DATABASE_URL and YC_BENCH_EXPERIMENT env vars - 2. Initialises the simulation via ``yc-bench sim init`` (NOT ``run``) - 3. Runs HermesAgentLoop with terminal tool - 4. Reads SQLite DB to compute final score - 5. Returns result dict with survival, funds, and composite score - """ - preset = eval_item["preset"] - seed = eval_item["seed"] - run_id = str(uuid.uuid4())[:8] - run_key = f"{preset}_seed{seed}_{run_id}" - - from tqdm import tqdm - tqdm.write(f" [START] preset={preset!r} seed={seed} (run_id={run_id})") - run_start = time.time() - - # Isolated DB per run -- prevents cross-run state leakage - db_path = os.path.join(self.config.db_dir, f"yc_bench_{run_key}.db") - os.environ["DATABASE_URL"] = f"sqlite:///{db_path}" - os.environ["YC_BENCH_EXPERIMENT"] = preset - - # Determine horizon: explicit config override > preset lookup > default 1 - horizon = self.config.horizon_years or _PRESET_HORIZONS.get(preset, 1) - - try: - # ---------------------------------------------------------- - # Step 1: Initialise the simulation via CLI - # IMPORTANT: We use `sim init`, NOT `yc-bench run`. - # `yc-bench run` starts yc-bench's own LLM agent loop (via - # LiteLLM), which would compete with our HermesAgentLoop. - # `sim init` just sets up the world and returns. - # ---------------------------------------------------------- - init_cmd = [ - "yc-bench", "sim", "init", - "--seed", str(seed), - "--start-date", self.config.start_date, - "--company-name", self.config.company_name, - "--horizon-years", str(horizon), - ] - init_result = subprocess.run( - init_cmd, capture_output=True, text=True, timeout=30, - ) - if init_result.returncode != 0: - error_msg = (init_result.stderr or init_result.stdout).strip() - raise RuntimeError(f"yc-bench sim init failed: {error_msg}") - - tqdm.write(f" Simulation initialized (horizon={horizon}yr)") - - # ---------------------------------------------------------- - # Step 2: Run the HermesAgentLoop - # ---------------------------------------------------------- - tools, valid_names = self._resolve_tools_for_group() - - messages: List[Dict[str, Any]] = [ - {"role": "system", "content": YC_BENCH_SYSTEM_PROMPT}, - {"role": "user", "content": self.format_prompt(eval_item)}, - ] - - agent = HermesAgentLoop( - server=self.server, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=run_id, - temperature=self.config.agent_temperature, - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - budget_config=self.config.build_budget_config(), - ) - result = await agent.run(messages) - - # ---------------------------------------------------------- - # Step 3: Read final score from the simulation DB - # ---------------------------------------------------------- - score_data = _read_final_score(db_path) - final_funds = score_data["final_funds_cents"] - survived = score_data["survived"] - terminal_reason = score_data["terminal_reason"] - - composite = _compute_composite_score( - final_funds_cents=final_funds, - survived=survived, - survival_weight=self.config.survival_weight, - funds_weight=self.config.funds_weight, - ) - - elapsed = time.time() - run_start - status = "SURVIVED" if survived else "BANKRUPT" - if final_funds >= 0: - funds_str = f"${final_funds / 100:,.0f}" - else: - funds_str = f"-${abs(final_funds) / 100:,.0f}" - - tqdm.write( - f" [{status}] preset={preset!r} seed={seed} " - f"funds={funds_str} score={composite:.3f} " - f"turns={result.turns_used} ({elapsed:.0f}s)" - ) - - out = { - "preset": preset, - "seed": seed, - "survived": survived, - "final_funds_cents": final_funds, - "final_funds_usd": final_funds / 100, - "terminal_reason": terminal_reason, - "composite_score": composite, - "turns_used": result.turns_used, - "finished_naturally": result.finished_naturally, - "elapsed_seconds": elapsed, - "db_path": db_path, - "messages": result.messages, - } - self._save_result(out) - return out - - except Exception as e: - elapsed = time.time() - run_start - logger.error("Run %s failed: %s", run_key, e, exc_info=True) - tqdm.write( - f" [ERROR] preset={preset!r} seed={seed}: {e} ({elapsed:.0f}s)" - ) - out = { - "preset": preset, - "seed": seed, - "survived": False, - "final_funds_cents": 0, - "final_funds_usd": 0.0, - "terminal_reason": f"error: {e}", - "composite_score": 0.0, - "turns_used": 0, - "error": str(e), - "elapsed_seconds": elapsed, - } - self._save_result(out) - return out - - # ========================================================================= - # Evaluate - # ========================================================================= - - async def _run_with_timeout(self, item: Dict[str, Any]) -> Dict: - """Wrap a single rollout with a wall-clock timeout.""" - preset = item["preset"] - seed = item["seed"] - try: - return await asyncio.wait_for( - self.rollout_and_score_eval(item), - timeout=self.config.run_timeout, - ) - except asyncio.TimeoutError: - from tqdm import tqdm - tqdm.write( - f" [TIMEOUT] preset={preset!r} seed={seed} " - f"(exceeded {self.config.run_timeout}s)" - ) - out = { - "preset": preset, - "seed": seed, - "survived": False, - "final_funds_cents": 0, - "final_funds_usd": 0.0, - "terminal_reason": f"timeout ({self.config.run_timeout}s)", - "composite_score": 0.0, - "turns_used": 0, - "error": "timeout", - } - self._save_result(out) - return out - - async def evaluate(self, *args, **kwargs) -> None: - """ - Run YC-Bench evaluation over all (preset, seed) combinations. - - Runs sequentially -- each run is 100-500 turns, parallelising would - be prohibitively expensive and cause env var conflicts. - """ - start_time = time.time() - from tqdm import tqdm - - # --- tqdm-compatible logging handler (TB2 pattern) --- - class _TqdmHandler(logging.Handler): - def emit(self, record): - try: - tqdm.write(self.format(record)) - except Exception: - self.handleError(record) - - root = logging.getLogger() - handler = _TqdmHandler() - handler.setFormatter( - logging.Formatter("%(levelname)s %(name)s: %(message)s") - ) - root.handlers = [handler] - for noisy in ("httpx", "openai"): - logging.getLogger(noisy).setLevel(logging.WARNING) - - # --- Print config summary --- - print(f"\n{'='*60}") - print("Starting YC-Bench Evaluation") - print(f"{'='*60}") - print(f" Presets: {self.config.presets}") - print(f" Seeds: {self.config.seeds}") - print(f" Total runs: {len(self.all_eval_items)}") - print(f" Max turns/run: {self.config.max_agent_turns}") - print(f" Run timeout: {self.config.run_timeout}s") - print(f"{'='*60}\n") - - results = [] - pbar = tqdm( - total=len(self.all_eval_items), desc="YC-Bench", dynamic_ncols=True - ) - - try: - for item in self.all_eval_items: - result = await self._run_with_timeout(item) - results.append(result) - survived_count = sum(1 for r in results if r.get("survived")) - pbar.set_postfix_str( - f"survived={survived_count}/{len(results)}" - ) - pbar.update(1) - - except (KeyboardInterrupt, asyncio.CancelledError): - tqdm.write("\n[INTERRUPTED] Stopping evaluation...") - pbar.close() - try: - from tools.terminal_tool import cleanup_all_environments - cleanup_all_environments() - except Exception: - pass - if hasattr(self, "_streaming_file") and not self._streaming_file.closed: - self._streaming_file.close() - return - - pbar.close() - end_time = time.time() - - # --- Compute metrics --- - valid = [r for r in results if r is not None] - if not valid: - print("Warning: No valid results.") - return - - total = len(valid) - survived_total = sum(1 for r in valid if r.get("survived")) - survival_rate = survived_total / total if total else 0.0 - avg_score = ( - sum(r.get("composite_score", 0) for r in valid) / total - if total - else 0.0 - ) - - preset_results: Dict[str, List[Dict]] = defaultdict(list) - for r in valid: - preset_results[r["preset"]].append(r) - - eval_metrics = { - "eval/survival_rate": survival_rate, - "eval/avg_composite_score": avg_score, - "eval/total_runs": total, - "eval/survived_runs": survived_total, - "eval/evaluation_time_seconds": end_time - start_time, - } - - for preset, items in sorted(preset_results.items()): - ps = sum(1 for r in items if r.get("survived")) - pt = len(items) - pa = ( - sum(r.get("composite_score", 0) for r in items) / pt - if pt - else 0 - ) - key = preset.replace("-", "_") - eval_metrics[f"eval/survival_rate_{key}"] = ps / pt if pt else 0 - eval_metrics[f"eval/avg_score_{key}"] = pa - - self.eval_metrics = list(eval_metrics.items()) - - # --- Print summary --- - print(f"\n{'='*60}") - print("YC-Bench Evaluation Results") - print(f"{'='*60}") - print( - f"Overall survival rate: {survival_rate:.1%} " - f"({survived_total}/{total})" - ) - print(f"Average composite score: {avg_score:.4f}") - print(f"Evaluation time: {end_time - start_time:.1f}s") - - print("\nPer-preset breakdown:") - for preset, items in sorted(preset_results.items()): - ps = sum(1 for r in items if r.get("survived")) - pt = len(items) - pa = ( - sum(r.get("composite_score", 0) for r in items) / pt - if pt - else 0 - ) - print(f" {preset}: {ps}/{pt} survived avg_score={pa:.4f}") - for r in items: - status = "SURVIVED" if r.get("survived") else "BANKRUPT" - funds = r.get("final_funds_usd", 0) - print( - f" seed={r['seed']} [{status}] " - f"${funds:,.0f} " - f"score={r.get('composite_score', 0):.3f}" - ) - - print(f"{'='*60}\n") - - # --- Log results --- - samples = [ - {k: v for k, v in r.items() if k != "messages"} for r in valid - ] - - try: - await self.evaluate_log( - metrics=eval_metrics, - samples=samples, - start_time=start_time, - end_time=end_time, - generation_parameters={ - "temperature": self.config.agent_temperature, - "max_tokens": self.config.max_token_length, - "max_agent_turns": self.config.max_agent_turns, - }, - ) - except Exception as e: - print(f"Error logging results: {e}") - - # --- Cleanup (TB2 pattern) --- - if hasattr(self, "_streaming_file") and not self._streaming_file.closed: - self._streaming_file.close() - print(f"Results saved to: {self._streaming_path}") - - try: - from tools.terminal_tool import cleanup_all_environments - cleanup_all_environments() - except Exception: - pass - - try: - from environments.agent_loop import _tool_executor - _tool_executor.shutdown(wait=False, cancel_futures=True) - except Exception: - pass - - # ========================================================================= - # Wandb logging - # ========================================================================= - - async def wandb_log(self, wandb_metrics: Optional[Dict] = None): - """Log YC-Bench-specific metrics to wandb.""" - if wandb_metrics is None: - wandb_metrics = {} - for k, v in self.eval_metrics: - wandb_metrics[k] = v - self.eval_metrics = [] - await super().wandb_log(wandb_metrics) - - -if __name__ == "__main__": - YCBenchEvalEnv.cli() diff --git a/environments/hermes_base_env.py b/environments/hermes_base_env.py deleted file mode 100644 index adefa9b7c3c..00000000000 --- a/environments/hermes_base_env.py +++ /dev/null @@ -1,714 +0,0 @@ -""" -HermesAgentBaseEnv -- Abstract Base Environment for Hermes-Agent + Atropos - -Provides the Atropos integration plumbing that all hermes-agent environments share: -- Two-mode operation (OpenAI server for Phase 1, VLLM ManagedServer for Phase 2) -- Per-group toolset/distribution resolution -- Agent loop orchestration via HermesAgentLoop -- ToolContext creation for reward functions -- ScoredDataGroup construction from ManagedServer state - -Subclasses only need to implement: - setup() -- Load dataset, initialize state - get_next_item() -- Return the next item from the dataset - format_prompt() -- Convert a dataset item into the user message - compute_reward() -- Score the rollout (has full ToolContext access) - evaluate() -- Periodic evaluation -""" - -import asyncio -import json -import logging -import os -import sys -import uuid -from abc import abstractmethod -from pathlib import Path -from typing import Any, Dict, List, Optional, Set, Tuple, Union - -# Ensure the hermes-agent repo root is on sys.path so that imports like -# `from model_tools import ...` and `from environments.X import ...` work -# regardless of where the script is invoked from. -_repo_root = Path(__file__).resolve().parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -from dotenv import load_dotenv -from pydantic import Field - -# Load API keys from hermes-agent/.env so all environments can access them -_env_path = _repo_root / ".env" -if _env_path.exists(): - load_dotenv(dotenv_path=_env_path) - -# Apply monkey patches for async-safe tool operation inside Atropos's event loop. -# This patches SwerexModalEnvironment to use a background thread instead of -# asyncio.run(), which would deadlock inside Atropos. Safe for normal CLI too. -from environments.patches import apply_patches -apply_patches() - -from atroposlib.envs.base import ( - BaseEnv, - BaseEnvConfig, - ScoredDataGroup, - ScoredDataItem, -) -from atroposlib.envs.server_handling.server_manager import ( - APIServerConfig, - ServerBaseline, - ServerManager, -) -from atroposlib.type_definitions import Item - -from environments.agent_loop import AgentResult, HermesAgentLoop -from environments.tool_context import ToolContext -from tools.budget_config import ( - DEFAULT_RESULT_SIZE_CHARS, - DEFAULT_TURN_BUDGET_CHARS, - DEFAULT_PREVIEW_SIZE_CHARS, -) - -# Import hermes-agent toolset infrastructure -from model_tools import get_tool_definitions -from toolset_distributions import sample_toolsets_from_distribution - -logger = logging.getLogger(__name__) - - -class HermesAgentEnvConfig(BaseEnvConfig): - """ - Configuration for hermes-agent Atropos environments. - - Extends BaseEnvConfig with agent-specific settings for toolsets, - terminal backend, dataset loading, and tool call parsing. - """ - - # --- Toolset configuration --- - # Mutually exclusive: use either enabled_toolsets OR distribution - enabled_toolsets: Optional[List[str]] = Field( - default=None, - description="Explicit list of hermes toolsets to enable (e.g., ['terminal', 'file', 'web']). " - "If None and distribution is also None, all available toolsets are enabled.", - ) - disabled_toolsets: Optional[List[str]] = Field( - default=None, - description="Toolsets to disable. Applied as a filter on top of enabled_toolsets or distribution.", - ) - distribution: Optional[str] = Field( - default=None, - description="Name of a toolset distribution from toolset_distributions.py " - "(e.g., 'development', 'terminal_tasks'). Sampled once per group. " - "Mutually exclusive with enabled_toolsets.", - ) - - # --- Agent loop configuration --- - max_agent_turns: int = Field( - default=30, - description="Maximum number of LLM calls (tool-calling iterations) per rollout.", - ) - system_prompt: Optional[str] = Field( - default=None, - description="System prompt for the agent. Tools are handled via the tools= parameter, " - "not embedded in the prompt text.", - ) - agent_temperature: float = Field( - default=1.0, - description="Sampling temperature for agent generation during rollouts.", - ) - - # --- Terminal backend --- - terminal_backend: str = Field( - default="local", - description="Terminal backend: 'local', 'docker', 'modal', 'daytona', 'ssh', 'singularity'. " - "Modal or Daytona recommended for production RL (cloud isolation per rollout).", - ) - terminal_timeout: int = Field( - default=120, - description="Per-command timeout in seconds for terminal tool calls. " - "Commands exceeding this are killed. Increase for tasks with long-running " - "commands (compilation, pip install, etc.).", - ) - terminal_lifetime: int = Field( - default=3600, - description="Sandbox inactivity lifetime in seconds. The cleanup thread kills " - "sandboxes that have been idle longer than this. Must be longer than " - "the longest gap between tool calls (e.g., waiting for LLM response).", - ) - - # --- Dataset --- - dataset_name: Optional[str] = Field( - default=None, - description="HuggingFace dataset name. Optional if tasks are defined inline.", - ) - dataset_split: str = Field( - default="train", - description="Dataset split to use.", - ) - prompt_field: str = Field( - default="prompt", - description="Which field in the dataset contains the prompt.", - ) - - # --- Thread pool --- - tool_pool_size: int = Field( - default=128, - description="Thread pool size for tool execution. Each concurrent task needs a " - "thread for tool calls. Must be large enough for parallel evaluation. " - "Too small = thread pool starvation.", - ) - - # --- Phase 2: Tool call parsing --- - tool_call_parser: str = Field( - default="hermes", - description="Tool call parser name for Phase 2 (VLLM server type). " - "Ignored in Phase 1 (OpenAI server type where VLLM parses natively). " - "Options: hermes, mistral, llama3_json, qwen, deepseek_v3, etc.", - ) - - # --- Tool result budget --- - # Defaults imported from tools.budget_config (single source of truth). - default_result_size_chars: int = Field( - default=DEFAULT_RESULT_SIZE_CHARS, - description="Default per-tool threshold (chars) for persisting large results " - "to sandbox. Results exceeding this are written to /tmp/hermes-results/ " - "and replaced with a preview. Per-tool registry values take precedence " - "unless overridden via tool_result_overrides.", - ) - turn_budget_chars: int = Field( - default=DEFAULT_TURN_BUDGET_CHARS, - description="Aggregate char budget per assistant turn. If all tool results " - "in a single turn exceed this, the largest are persisted to disk first.", - ) - preview_size_chars: int = Field( - default=DEFAULT_PREVIEW_SIZE_CHARS, - description="Size of the inline preview shown after a tool result is persisted.", - ) - tool_result_overrides: Optional[Dict[str, int]] = Field( - default=None, - description="Per-tool threshold overrides (chars). Keys are tool names, " - "values are char thresholds. Overrides both the default and registry " - "per-tool values. Example: {'terminal': 10000, 'search_files': 5000}. " - "Note: read_file is pinned to infinity and cannot be overridden.", - ) - - # --- Provider-specific parameters --- - # Passed as extra_body to the OpenAI client's chat.completions.create() call. - # Useful for OpenRouter provider preferences, transforms, route settings, etc. - # Example YAML: - # extra_body: - # provider: - # ignore: ["DeepInfra", "Fireworks"] - # order: ["Together"] - # transforms: ["middle-out"] - extra_body: Optional[Dict[str, Any]] = Field( - default=None, - description="Extra body parameters passed to the OpenAI client's " - "chat.completions.create(). Used for OpenRouter provider preferences, " - "transforms, and other provider-specific settings.", - ) - - def build_budget_config(self): - """Build a BudgetConfig from env config fields.""" - from tools.budget_config import BudgetConfig - return BudgetConfig( - default_result_size=self.default_result_size_chars, - turn_budget=self.turn_budget_chars, - preview_size=self.preview_size_chars, - tool_overrides=dict(self.tool_result_overrides) if self.tool_result_overrides else {}, - ) - - -class HermesAgentBaseEnv(BaseEnv): - """ - Abstract base environment for hermes-agent Atropos integration. - - Handles two modes of operation: - - Phase 1 (OpenAI server type): Uses server.chat_completion() directly. - The server (VLLM, SGLang, OpenRouter, OpenAI) handles tool call parsing - and reasoning extraction natively. DummyManagedServer provides placeholder - tokens. Good for SFT data gen, verifier testing, evaluation. - - - Phase 2 (VLLM server type): Uses ManagedServer for exact token IDs + logprobs - via /generate. Client-side tool call parser reconstructs structured tool_calls - from raw output. Full RL training capability. - - Subclasses must implement: - setup() -- Load dataset, initialize state - get_next_item() -- Return the next item to roll out - format_prompt() -- Convert a dataset item into the user message string - compute_reward() -- Score the rollout using ToolContext - evaluate() -- Periodic evaluation - """ - - name: Optional[str] = "hermes-agent" - env_config_cls = HermesAgentEnvConfig - - def __init__( - self, - config: HermesAgentEnvConfig, - server_configs: Union[ServerBaseline, List[APIServerConfig]], - slurm=False, - testing=False, - ): - super().__init__(config, server_configs, slurm, testing) - - # Set terminal environment variables so hermes tools pick them up. - # These can all be overridden per-environment via config fields instead - # of requiring users to set shell env vars. - if config.terminal_backend: - os.environ["TERMINAL_ENV"] = config.terminal_backend - os.environ["TERMINAL_TIMEOUT"] = str(config.terminal_timeout) - os.environ["TERMINAL_LIFETIME_SECONDS"] = str(config.terminal_lifetime) - print( - f"🖥️ Terminal: backend={config.terminal_backend}, " - f"timeout={config.terminal_timeout}s, lifetime={config.terminal_lifetime}s" - ) - - # Resize the agent loop's thread pool for tool execution. - # This must be large enough for the number of concurrent tasks - # (e.g., 89 parallel TB2 eval tasks each need a thread for tool calls). - from environments.agent_loop import resize_tool_pool - resize_tool_pool(config.tool_pool_size) - - # Set tool_parser on the ServerManager so ManagedServer uses it - # for bidirectional tool call translation (raw text ↔ OpenAI tool_calls). - if hasattr(self.server, 'tool_parser'): - self.server.tool_parser = config.tool_call_parser - print(f"🔧 Tool parser: {config.tool_call_parser}") - - # Current group's resolved tools (set in collect_trajectories) - self._current_group_tools: Optional[Tuple[List[Dict], Set[str]]] = None - - # Tool error tracking for wandb logging - self._tool_error_buffer: List[Dict[str, Any]] = [] - - # ========================================================================= - # Toolset resolution (per-group) - # ========================================================================= - - def _resolve_tools_for_group(self) -> Tuple[List[Dict[str, Any]], Set[str]]: - """ - Resolve toolsets for a group. Called once in collect_trajectories(), - then shared by all collect_trajectory() calls in the group. - - If distribution is set, samples probabilistically. - If enabled_toolsets is set, uses that explicit list. - disabled_toolsets is applied as a filter on top. - - Returns: - (tool_schemas, valid_tool_names) tuple - """ - config = self.config - - if config.distribution: - group_toolsets = sample_toolsets_from_distribution(config.distribution) - logger.info("Sampled toolsets from '%s': %s", config.distribution, group_toolsets) - else: - group_toolsets = config.enabled_toolsets # None means "all available" - if group_toolsets is None: - logger.warning( - "enabled_toolsets is None -- loading ALL tools including messaging. " - "Set explicit enabled_toolsets for RL training." - ) - - tools = get_tool_definitions( - enabled_toolsets=group_toolsets, - disabled_toolsets=config.disabled_toolsets, - quiet_mode=True, - ) - - valid_names = {t["function"]["name"] for t in tools} if tools else set() - logger.info("Resolved %d tools for group: %s", len(valid_names), sorted(valid_names)) - return tools, valid_names - - # ========================================================================= - # Server mode detection - # ========================================================================= - - def _use_managed_server(self) -> bool: - """ - Determine if we should use ManagedServer (Phase 2) or direct server (Phase 1). - - Phase 2 (ManagedServer) is used when the server type is 'vllm' or 'sglang', - which go through the /generate endpoint for exact token tracking. - - Phase 1 (direct server) is used for 'openai' server type, which uses - /v1/chat/completions with native tool call parsing. - """ - if not self.server.servers: - return False - - server = self.server.servers[0] - # If the server is an OpenAI server (not VLLM/SGLang), use direct mode - from atroposlib.envs.server_handling.openai_server import OpenAIServer - return not isinstance(server, OpenAIServer) - - # ========================================================================= - # Core Atropos integration - # ========================================================================= - - async def collect_trajectories( - self, item: Item - ) -> Tuple[ - Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]], - List[Item], - ]: - """ - Override collect_trajectories to resolve toolsets once per group, - then delegate to the standard group-level collection. - - The default BaseEnv.collect_trajectories() calls collect_trajectory() - group_size times in parallel. We resolve tools once here and store - them for all those calls to use. - """ - # Resolve toolsets for this group (shared by all rollouts in the group) - self._current_group_tools = self._resolve_tools_for_group() - - # Delegate to the default implementation which calls collect_trajectory() - # group_size times via asyncio.gather - return await super().collect_trajectories(item) - - # ========================================================================= - # Wandb rollout display -- format trajectories nicely - # ========================================================================= - - @staticmethod - def _format_trajectory_for_display(messages: List[Dict[str, Any]]) -> str: - """ - Format a conversation's messages into a readable trajectory string - for wandb rollout tables. Shows tool calls, tool results, and reasoning - in a structured way instead of raw token decoding. - """ - parts = [] - for msg in messages: - role = msg.get("role", "unknown") - content = msg.get("content", "") - - if role == "system": - parts.append(f"[SYSTEM]\n{content}") - - elif role == "user": - parts.append(f"[USER]\n{content}") - - elif role == "assistant": - # Show reasoning if present - reasoning = msg.get("reasoning_content", "") - if reasoning: - # Truncate long reasoning for display - if len(reasoning) > 300: - reasoning = reasoning[:300] + "..." - parts.append(f"[ASSISTANT thinking]\n{reasoning}") - - # Show content - if content: - parts.append(f"[ASSISTANT]\n{content}") - - # Show tool calls - tool_calls = msg.get("tool_calls", []) - for tc in tool_calls: - func = tc.get("function", {}) - name = func.get("name", "?") - args = func.get("arguments", "{}") - # Truncate long arguments for display - if len(args) > 200: - args = args[:200] + "..." - parts.append(f"[TOOL CALL] {name}({args})") - - elif role == "tool": - tool_id = msg.get("tool_call_id", "") - result = content - # Truncate long tool results for display - if len(result) > 500: - result = result[:500] + "..." - parts.append(f"[TOOL RESULT] {result}") - - return "\n\n".join(parts) - - async def add_rollouts_for_wandb( - self, - scored_data, - item=None, - ): - """ - Override to show formatted trajectories with tool calls visible, - instead of raw token decoding which loses all structure. - """ - num_keep = self.config.num_rollouts_per_group_for_logging - if num_keep == -1: - num_keep = self.config.group_size - - group = [] - for i in range(min(num_keep, len(scored_data.get("scores", [])))): - score = scored_data["scores"][i] - - # Use messages if available for rich display - messages = None - if scored_data.get("messages") and i < len(scored_data["messages"]): - messages = scored_data["messages"][i] - - if messages: - text = self._format_trajectory_for_display(messages) - elif scored_data.get("tokens") and i < len(scored_data["tokens"]): - text = self.tokenizer.decode(scored_data["tokens"][i]) - else: - text = "(no data)" - - group.append((text, score)) - - self.rollouts_for_wandb.append(group) - if len(self.rollouts_for_wandb) > self.config.num_rollouts_to_keep: - self.rollouts_for_wandb.pop(0) - - async def wandb_log(self, wandb_metrics: Optional[Dict] = None): - """Log base metrics including tool errors to wandb.""" - if wandb_metrics is None: - wandb_metrics = {} - - # Log tool error stats - if self._tool_error_buffer: - wandb_metrics["train/tool_errors_count"] = len(self._tool_error_buffer) - - # Log error details as a summary string (tables can crash wandb on tmp cleanup) - error_summaries = [] - for err in self._tool_error_buffer: - error_summaries.append( - f"[turn {err['turn']}] {err['tool']}({err['args'][:80]}) -> {err['error'][:150]}" - ) - wandb_metrics["train/tool_error_details"] = "\n".join(error_summaries) - - # Also print to stdout for immediate visibility - for summary in error_summaries: - print(f" Tool Error: {summary}") - - self._tool_error_buffer = [] - else: - wandb_metrics["train/tool_errors_count"] = 0 - - await super().wandb_log(wandb_metrics) - - async def collect_trajectory( - self, item: Item - ) -> Tuple[Optional[Union[ScoredDataItem, Any]], List[Item]]: - """ - Run a single rollout: agent loop + reward computation. - - This is called group_size times in parallel by collect_trajectories(). - Each call gets its own task_id for terminal/browser session isolation. - """ - task_id = str(uuid.uuid4()) - - # Get group-level tools (resolved once in collect_trajectories) - if self._current_group_tools is None: - # Fallback: resolve per-trajectory if called outside collect_trajectories - tools, valid_names = self._resolve_tools_for_group() - else: - tools, valid_names = self._current_group_tools - - # Build initial messages - messages: List[Dict[str, Any]] = [] - if self.config.system_prompt: - messages.append({"role": "system", "content": self.config.system_prompt}) - messages.append({"role": "user", "content": self.format_prompt(item)}) - - # Run the agent loop - result: AgentResult - if self._use_managed_server(): - # Phase 2: ManagedServer with ToolCallTranslator -- exact tokens + logprobs - # tool_parser is set on ServerManager in __init__ and passed through - # to ManagedServer, which uses ToolCallTranslator for bidirectional - # translation between raw text and OpenAI tool_calls. - try: - async with self.server.managed_server( - tokenizer=self.tokenizer, - preserve_think_blocks=bool(self.config.thinking_mode), - ) as managed: - agent = HermesAgentLoop( - server=managed, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=task_id, - temperature=self.config.agent_temperature, - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - budget_config=self.config.build_budget_config(), - ) - result = await agent.run(messages) - except NotImplementedError: - # DummyManagedServer not allowed -- fall back to Phase 1 - logger.warning( - "ManagedServer not available (OpenAI server?). " - "Falling back to direct server mode." - ) - agent = HermesAgentLoop( - server=self.server, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=task_id, - temperature=self.config.agent_temperature, - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - budget_config=self.config.build_budget_config(), - ) - result = await agent.run(messages) - else: - # Phase 1: OpenAI server -- native tool_calls, placeholder tokens - agent = HermesAgentLoop( - server=self.server, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=task_id, - temperature=self.config.agent_temperature, - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - budget_config=self.config.build_budget_config(), - ) - result = await agent.run(messages) - - # Skip reward computation if the agent loop produced no meaningful work - # (e.g., API call failed on turn 1). No point spinning up a Modal sandbox - # just to verify files that were never created. - only_system_and_user = all( - msg.get("role") in {"system", "user"} for msg in result.messages - ) - if result.turns_used == 0 or only_system_and_user: - logger.warning( - "Agent loop produced no output (turns=%d, msgs=%d). Skipping reward.", - result.turns_used, len(result.messages), - ) - reward = 0.0 - else: - # Compute reward using ToolContext (gives verifier full tool access) - ctx = ToolContext(task_id) - try: - reward = await self.compute_reward(item, result, ctx) - except Exception as e: - logger.error("compute_reward failed: %s", e) - reward = 0.0 - finally: - ctx.cleanup() - - # Track tool errors for wandb logging - if result.tool_errors: - for err in result.tool_errors: - self._tool_error_buffer.append({ - "turn": err.turn, - "tool": err.tool_name, - "args": err.arguments[:150], - "error": err.error[:300], - "result": err.tool_result[:300], - }) - - # Build ScoredDataItem from ManagedServer state - # Phase 2: real tokens/masks/logprobs from SequenceNodes - # Phase 1: placeholder tokens (still need a valid ScoredDataItem for the pipeline) - nodes = (result.managed_state or {}).get("nodes", []) - - if nodes: - # Phase 2 (or DummyManagedServer): use actual node data - node = nodes[-1] # Final sequence node = full trajectory - scored_item: Dict[str, Any] = { - "tokens": node.tokens, - "masks": node.masked_tokens, - "scores": reward, - } - - # Include logprobs if available (Phase 2) - if hasattr(node, "logprobs") and node.logprobs: - scored_item["advantages"] = None # Computed by trainer - scored_item["ref_logprobs"] = None - else: - # Phase 1 with no managed state: create placeholder tokens - # so the data pipeline doesn't break. These are NOT suitable - # for training but allow process mode (SFT data gen) to work. - # Tokenize the full conversation to get approximate tokens. - full_text = "\n".join( - msg.get("content", "") for msg in result.messages if msg.get("content") - ) - if self.tokenizer: - tokens = self.tokenizer.encode(full_text, add_special_tokens=True) - else: - tokens = list(range(min(len(full_text) // 4, 128))) - - scored_item = { - "tokens": tokens, - "masks": [-100] + tokens[1:], # Mask first token as prompt - "scores": reward, - } - - # Always include messages for wandb rollout display and data logging - scored_item["messages"] = result.messages - - return scored_item, [] - - # ========================================================================= - # Abstract methods -- subclasses must implement - # ========================================================================= - - @abstractmethod - async def setup(self): - """ - Load dataset, initialize state. - - Called once when the environment starts. Typical implementation: - self.dataset = load_dataset(self.config.dataset_name, split=self.config.dataset_split) - self.iter = 0 - """ - raise NotImplementedError - - @abstractmethod - async def get_next_item(self) -> Item: - """ - Return the next item from the dataset for rollout. - - Called by the base env's main loop to get items for workers. - Should cycle through the dataset. - """ - raise NotImplementedError - - @abstractmethod - def format_prompt(self, item: Item) -> str: - """ - Convert a dataset item into the user message for the agent. - - Args: - item: Dataset item (dict, tuple, etc.) - - Returns: - The prompt string to send to the agent - """ - raise NotImplementedError - - @abstractmethod - async def compute_reward( - self, item: Item, result: AgentResult, ctx: ToolContext - ) -> float: - """ - Score the rollout. Has full access to: - - item: the original dataset item (ground truth, test commands, etc.) - - result: AgentResult with full messages, turn count, reasoning, etc. - - ctx: ToolContext -- call ANY hermes-agent tool (terminal, file, web, - browser, vision...) scoped to this rollout's sandbox. Nothing - is off-limits. - - Args: - item: The dataset item that was rolled out - result: The agent's rollout result - ctx: ToolContext with full tool access for verification - - Returns: - Reward float (typically 0.0 to 1.0, but any float is valid) - """ - raise NotImplementedError - - @abstractmethod - async def evaluate(self, *args, **kwargs): - """ - Periodic evaluation. Called every steps_per_eval steps. - - Typical implementation runs the agent on a held-out eval set - and logs metrics via wandb/evaluate_log. - """ - raise NotImplementedError diff --git a/environments/hermes_swe_env/__init__.py b/environments/hermes_swe_env/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/environments/hermes_swe_env/default.yaml b/environments/hermes_swe_env/default.yaml deleted file mode 100644 index 2d0113345f8..00000000000 --- a/environments/hermes_swe_env/default.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# SWE Environment -- Default Configuration -# -# SWE-bench style tasks with Modal sandboxes for cloud isolation. -# Uses terminal + file + web toolsets. -# -# Usage: -# python environments/hermes_swe_env/hermes_swe_env.py serve \ -# --config environments/hermes_swe_env/default.yaml - -env: - enabled_toolsets: ["terminal", "file", "web"] - max_agent_turns: 30 - max_token_length: 4096 - group_size: 4 - terminal_backend: "modal" - tool_call_parser: "hermes" - tokenizer_name: "NousResearch/DeepHermes-3-Llama-3-3B-Preview" - dataset_name: "bigcode/humanevalpack" - dataset_split: "test" - prompt_field: "prompt" - steps_per_eval: 50 - total_steps: 500 - use_wandb: true - wandb_name: "hermes-swe" - system_prompt: > - You are a skilled software engineer. You have access to a terminal, - file tools, and web search. Use these tools to complete the coding task. - Write clean, working code and verify it runs correctly before finishing. - -openai: - base_url: "http://localhost:8000/v1" - model_name: "NousResearch/DeepHermes-3-Llama-3-3B-Preview" - server_type: "openai" - api_key: "" diff --git a/environments/hermes_swe_env/hermes_swe_env.py b/environments/hermes_swe_env/hermes_swe_env.py deleted file mode 100644 index 49c521e5f76..00000000000 --- a/environments/hermes_swe_env/hermes_swe_env.py +++ /dev/null @@ -1,229 +0,0 @@ -""" -HermesSweEnv -- SWE-Bench Style Environment with Modal Sandboxes - -A concrete environment for software engineering tasks where the model writes code -and the reward function runs tests to verify correctness. Uses Modal terminal -backend for cloud-isolated sandboxes per rollout. - -The reward function uses ToolContext.terminal() to run test commands in the same -Modal sandbox the model used during its agentic loop. All filesystem state from -the model's tool calls is preserved for verification. - -Usage: - # Phase 1: OpenAI server type - vllm serve YourModel --tool-parser hermes - run-api - python environments/hermes_swe_env.py serve \\ - --openai.base_url http://localhost:8000/v1 \\ - --openai.model_name YourModel \\ - --openai.server_type openai \\ - --env.dataset_name bigcode/humanevalpack \\ - --env.terminal_backend modal - - # Phase 2: VLLM server type (full RL training) - python environments/hermes_swe_env.py serve \\ - --openai.base_url http://localhost:8000/v1 \\ - --openai.model_name YourModel \\ - --openai.server_type vllm \\ - --env.tool_call_parser hermes \\ - --env.terminal_backend modal -""" - -import logging -import sys -import time -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple, Union - -# Ensure repo root is on sys.path for imports -_repo_root = Path(__file__).resolve().parent.parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -from datasets import load_dataset - -from atroposlib.envs.base import ScoredDataGroup -from atroposlib.envs.server_handling.server_manager import APIServerConfig -from atroposlib.type_definitions import Item - -from environments.agent_loop import AgentResult -from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig -from environments.tool_context import ToolContext - -logger = logging.getLogger(__name__) - - -class HermesSweEnvConfig(HermesAgentEnvConfig): - """Config with defaults for SWE-bench style tasks.""" - - pass # Inherits all fields, overrides defaults in config_init - - -class HermesSweEnv(HermesAgentBaseEnv): - """ - SWE-bench style environment using Modal terminal backend. - - The model gets a coding task, uses terminal + file + web tools to solve it, - and the reward function runs tests in the same Modal sandbox to verify. - - Subclass this for specific SWE datasets (HumanEval, SWE-bench, etc.) - and customize format_prompt() and compute_reward() as needed. - """ - - name = "hermes-swe" - env_config_cls = HermesSweEnvConfig - - @classmethod - def config_init(cls) -> Tuple[HermesSweEnvConfig, List[APIServerConfig]]: - """ - Default configuration for the SWE environment. - - Uses Modal terminal backend for cloud isolation and terminal + file + web toolsets. - """ - env_config = HermesSweEnvConfig( - # Toolsets: terminal for running code, file for reading/writing, web for docs - enabled_toolsets=["terminal", "file", "web"], - disabled_toolsets=None, - distribution=None, - # Agent settings -- SWE tasks need more turns - max_agent_turns=30, - max_token_length=4096, - agent_temperature=1.0, - system_prompt=( - "You are a skilled software engineer. You have access to a terminal, " - "file tools, and web search. Use these tools to complete the coding task. " - "Write clean, working code and verify it runs correctly before finishing." - ), - # Modal backend for cloud-isolated sandboxes - terminal_backend="modal", - # Dataset -- override via CLI for your specific SWE dataset - dataset_name="bigcode/humanevalpack", - dataset_split="test", - prompt_field="prompt", - # Atropos settings - group_size=4, - tokenizer_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview", - tool_call_parser="hermes", - steps_per_eval=50, - total_steps=500, - use_wandb=True, - wandb_name="hermes-swe", - ) - - server_configs = [ - APIServerConfig( - base_url="http://localhost:8000/v1", - model_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview", - server_type="openai", # Phase 1; switch to "vllm" for Phase 2 - api_key="", - ) - ] - - return env_config, server_configs - - async def setup(self): - """Load the SWE dataset.""" - if self.config.dataset_name: - self.dataset = load_dataset( - self.config.dataset_name, split=self.config.dataset_split - ) - else: - # Placeholder if no dataset specified - self.dataset = [] - self.iter = 0 - self.reward_buffer: List[float] = [] - - async def get_next_item(self) -> Dict[str, Any]: - """Cycle through the SWE dataset.""" - if not self.dataset: - raise ValueError("No dataset loaded. Set dataset_name in config.") - item = self.dataset[self.iter % len(self.dataset)] - self.iter += 1 - return item - - def format_prompt(self, item: Dict[str, Any]) -> str: - """ - Format the SWE task prompt. - - Override this in subclasses for different dataset formats. - Default assumes the dataset has a 'prompt' field and optionally a 'test' field. - """ - prompt = item.get(self.config.prompt_field, "") - - # If the dataset has test information, include it in the prompt - test_info = item.get("test", item.get("test_code", item.get("tests", ""))) - if test_info: - prompt += f"\n\nTests to pass:\n{test_info}" - - return prompt - - async def compute_reward( - self, item: Dict[str, Any], result: AgentResult, ctx: ToolContext - ) -> float: - """ - Score by running tests in the model's Modal sandbox. - - Default implementation: - - If the dataset item has a 'test' or 'test_code' field, run it - - Check exit code: 0 = pass, non-zero = fail - - Partial credit for file creation - - Override this in subclasses for more sophisticated reward logic. - """ - # Find the test command from the dataset item - test_code = item.get("test", item.get("test_code", item.get("tests", ""))) - - if test_code: - # Run the test in the model's sandbox - test_result = ctx.terminal( - f'cd /workspace && python3 -c "{test_code}"', timeout=60 - ) - - if test_result["exit_code"] == 0: - self.reward_buffer.append(1.0) - return 1.0 - - # Partial credit: check if the model created any Python files - file_check = ctx.terminal("find /workspace -name '*.py' -newer /tmp/.start_marker 2>/dev/null | head -5") - if file_check["exit_code"] == 0 and file_check.get("output", "").strip(): - self.reward_buffer.append(0.1) - return 0.1 - - self.reward_buffer.append(0.0) - return 0.0 - - async def evaluate(self, *args, **kwargs): - """ - Run evaluation on a held-out set. - - Override for dataset-specific evaluation logic. - """ - start_time = time.time() - end_time = time.time() - - eval_metrics = {"eval/placeholder": 0.0} - await self.evaluate_log( - metrics=eval_metrics, - start_time=start_time, - end_time=end_time, - ) - - async def wandb_log(self, wandb_metrics: Optional[Dict] = None): - """Log SWE-specific metrics.""" - if wandb_metrics is None: - wandb_metrics = {} - - if self.reward_buffer: - wandb_metrics["train/avg_reward"] = sum(self.reward_buffer) / len( - self.reward_buffer - ) - wandb_metrics["train/pass_rate"] = sum( - 1 for r in self.reward_buffer if r == 1.0 - ) / len(self.reward_buffer) - self.reward_buffer = [] - - await super().wandb_log(wandb_metrics) - - -if __name__ == "__main__": - HermesSweEnv.cli() diff --git a/environments/patches.py b/environments/patches.py deleted file mode 100644 index a5afe751ece..00000000000 --- a/environments/patches.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -Monkey patches for making hermes-agent tools work inside async frameworks (Atropos). - -Problem: - Some tools use asyncio.run() internally (e.g., Modal backend via SWE-ReX, - web_extract). This crashes when called from inside Atropos's event loop because - asyncio.run() can't be nested. - -Solution: - The Modal environment (tools/environments/modal.py) now uses a dedicated - _AsyncWorker thread internally, making it safe for both CLI and Atropos use. - No monkey-patching is required. - - This module is kept for backward compatibility. apply_patches() is a no-op. - -Usage: - Call apply_patches() once at import time (done automatically by hermes_base_env.py). - This is idempotent and safe to call multiple times. -""" - -import logging - -logger = logging.getLogger(__name__) - -_patches_applied = False - - -def apply_patches(): - """Apply all monkey patches needed for Atropos compatibility.""" - global _patches_applied - if _patches_applied: - return - - logger.debug("apply_patches() called; no patches needed (async safety is built-in)") - _patches_applied = True diff --git a/environments/terminal_test_env/__init__.py b/environments/terminal_test_env/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/environments/terminal_test_env/default.yaml b/environments/terminal_test_env/default.yaml deleted file mode 100644 index dc971071c3a..00000000000 --- a/environments/terminal_test_env/default.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# Terminal Test Environment -- Default Configuration -# -# Simple file-creation tasks for validating the full Atropos + hermes-agent stack. -# Uses Modal terminal backend and OpenRouter (Claude) for inference. -# API keys loaded from ~/hermes-agent/.env -# -# Usage: -# run-api -# python environments/terminal_test_env/terminal_test_env.py serve \ -# --config environments/terminal_test_env/default.yaml - -env: - enabled_toolsets: ["terminal", "file"] - max_agent_turns: 10 - max_token_length: 2048 - group_size: 3 - total_steps: 3 - steps_per_eval: 3 - terminal_backend: "modal" - tool_call_parser: "hermes" - tokenizer_name: "NousResearch/DeepHermes-3-Llama-3-3B-Preview" - ensure_scores_are_not_same: false - use_wandb: false - system_prompt: > - You are a helpful assistant with access to a terminal and file tools. - Complete the user's request by using the available tools. - Be precise and follow instructions exactly. - -openai: - base_url: "https://openrouter.ai/api/v1" - model_name: "anthropic/claude-opus-4.6" - server_type: "openai" - health_check: false - # api_key loaded from OPENROUTER_API_KEY in .env diff --git a/environments/terminal_test_env/terminal_test_env.py b/environments/terminal_test_env/terminal_test_env.py deleted file mode 100644 index 4d151ee7b76..00000000000 --- a/environments/terminal_test_env/terminal_test_env.py +++ /dev/null @@ -1,292 +0,0 @@ -""" -TerminalTestEnv -- Simple Test Environment for Validating the Stack - -A self-contained environment with inline tasks (no external dataset needed). -Each task asks the model to create a file at a known path with specific content. -The reward verifier cats the file and checks if the content matches. - -Enables only terminal + file toolsets. Uses Modal terminal backend with -OpenRouter (Claude) by default. - -Training tasks (3): - 1. Create ~/greeting.txt with "Hello from Hermes Agent" - 2. Create ~/count.txt with numbers 1-5, one per line - 3. Create ~/answer.txt with the result of 123 + 456 - -Eval task (1): - 1. Create ~/result.txt with the result of 6 * 7 - -Usage: - # Start Atropos API server - run-api - - # Run environment (uses OpenRouter + Modal by default) - python environments/terminal_test_env.py serve - - # Process mode (no run-api needed, saves to JSONL) - python environments/terminal_test_env.py process \\ - --env.data_path_to_save_groups terminal_test_output.jsonl -""" - -import logging -import os -import sys -import time -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple, Union - -# Ensure repo root is on sys.path for imports -_repo_root = Path(__file__).resolve().parent.parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -from atroposlib.envs.base import ScoredDataGroup -from atroposlib.envs.server_handling.server_manager import APIServerConfig -from atroposlib.type_definitions import Item - -from environments.agent_loop import AgentResult -from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig -from environments.tool_context import ToolContext - -logger = logging.getLogger(__name__) - - -# ============================================================================= -# Inline task definitions -- no external dataset needed -# ============================================================================= - -TRAIN_TASKS = [ - { - "prompt": "Create a file at ~/greeting.txt containing exactly the text: Hello from Hermes Agent", - "verify_path": "~/greeting.txt", - "expected_content": "Hello from Hermes Agent", - }, - { - "prompt": "Create a file at ~/count.txt containing the numbers 1 through 5, one per line", - "verify_path": "~/count.txt", - "expected_content": "1\n2\n3\n4\n5", - }, - { - "prompt": "Create a file at ~/answer.txt containing the result of 123 + 456", - "verify_path": "~/answer.txt", - "expected_content": "579", - }, -] - -EVAL_TASKS = [ - { - "prompt": "Create a file at ~/result.txt containing the result of 6 * 7", - "verify_path": "~/result.txt", - "expected_content": "42", - }, -] - - -class TerminalTestEnvConfig(HermesAgentEnvConfig): - """Config with defaults suitable for terminal testing.""" - - pass # Inherits all fields, overrides defaults in config_init - - -class TerminalTestEnv(HermesAgentBaseEnv): - """ - Simple test environment with inline file-creation tasks. - - All tasks follow the same pattern: "create a file at ~/X.txt with content Y". - The verifier runs `cat ~/X.txt` in the rollout's terminal and checks the output - against the expected string. Same verifier logic for all tasks. - - This environment is designed to validate the full stack end-to-end: - - Agent loop executes tool calls (terminal/file) - - ToolContext provides terminal access to the reward function - - Reward function verifies file content via cat - - Scored data flows through the Atropos pipeline - """ - - name = "terminal-test" - env_config_cls = TerminalTestEnvConfig - - @classmethod - def config_init(cls) -> Tuple[TerminalTestEnvConfig, List[APIServerConfig]]: - """ - Default configuration for the terminal test environment. - - Uses Modal terminal backend for cloud isolation and OpenRouter with - Claude for inference. API keys loaded from ~/hermes-agent/.env. - """ - env_config = TerminalTestEnvConfig( - # Terminal + file tools only - enabled_toolsets=["terminal", "file"], - disabled_toolsets=None, - distribution=None, - # Agent settings - max_agent_turns=10, # Simple tasks, don't need many turns - max_token_length=16000, - agent_temperature=1.0, - system_prompt=( - "You are a helpful assistant with access to a terminal and file tools. " - "Complete the user's request by using the available tools. " - "Be precise and follow instructions exactly." - ), - # Modal terminal backend for cloud-isolated sandboxes per rollout - terminal_backend="modal", - # Atropos settings - group_size=3, # 3 rollouts per group - tokenizer_name="NousResearch/q-30b-t-h45-e1", - tool_call_parser="hermes", - steps_per_eval=3, # Eval after all 3 steps - total_steps=3, # 3 groups total (1 group per step) - use_wandb=True, - wandb_name="terminal-test", - ensure_scores_are_not_same=False, # Allow all-same scores for simple tasks - # No external dataset - dataset_name=None, - ) - - # OpenRouter with Claude -- API key loaded from .env (OPENROUTER_API_KEY) - server_configs = [ - APIServerConfig( - base_url="https://openrouter.ai/api/v1", - model_name="anthropic/claude-opus-4.6", - server_type="openai", - api_key=os.getenv("OPENROUTER_API_KEY", ""), - health_check=False, # OpenRouter doesn't have a /health endpoint - ) - ] - - return env_config, server_configs - - async def setup(self): - """Initialize inline task lists.""" - self.train_tasks = list(TRAIN_TASKS) - self.eval_tasks = list(EVAL_TASKS) - self.iter = 0 - # Track reward stats for wandb logging - self.reward_buffer: List[float] = [] - - async def get_next_item(self) -> Dict[str, str]: - """Cycle through training tasks.""" - item = self.train_tasks[self.iter % len(self.train_tasks)] - self.iter += 1 - return item - - def format_prompt(self, item: Dict[str, str]) -> str: - """The prompt is directly in the task item.""" - return item["prompt"] - - async def compute_reward( - self, item: Dict[str, str], result: AgentResult, ctx: ToolContext - ) -> float: - """ - Verify by cat-ing the expected file path and checking content matches. - Same verifier for all tasks -- they all write a file at a known path. - - Scoring: - 1.0 = exact match - 0.5 = expected content is present but has extra stuff - 0.0 = file doesn't exist or content doesn't match - """ - verify_result = ctx.terminal(f"cat {item['verify_path']}") - - # File doesn't exist or can't be read - if verify_result["exit_code"] != 0: - self.reward_buffer.append(0.0) - return 0.0 - - actual = verify_result.get("output", "").strip() - expected = item["expected_content"].strip() - - # Exact match - if actual == expected: - self.reward_buffer.append(1.0) - return 1.0 - - # Partial credit: expected content is present but has extra stuff - if expected in actual: - self.reward_buffer.append(0.5) - return 0.5 - - self.reward_buffer.append(0.0) - return 0.0 - - async def evaluate(self, *args, **kwargs): - """ - Run eval tasks using the agent loop and verify results. - Logs accuracy metrics. - """ - start_time = time.time() - correct = 0 - total = len(self.eval_tasks) - samples = [] - - for eval_item in self.eval_tasks: - try: - # For eval, we do a simple single-turn completion (not full agent loop) - # to keep eval fast. The agent loop is tested via training. - completion = await self.server.chat_completion( - messages=[ - {"role": "system", "content": self.config.system_prompt or ""}, - {"role": "user", "content": eval_item["prompt"]}, - ], - n=1, - max_tokens=self.config.max_token_length, - temperature=0.0, - split="eval", - ) - - response_content = ( - completion.choices[0].message.content if completion.choices else "" - ) - - samples.append( - { - "prompt": eval_item["prompt"], - "response": response_content, - "expected": eval_item["expected_content"], - } - ) - - except Exception as e: - logger.error("Eval failed for item: %s", e) - samples.append( - { - "prompt": eval_item["prompt"], - "response": f"ERROR: {e}", - "expected": eval_item["expected_content"], - } - ) - - end_time = time.time() - - eval_metrics = { - "eval/num_samples": total, - } - - await self.evaluate_log( - metrics=eval_metrics, - samples=samples, - start_time=start_time, - end_time=end_time, - ) - - async def wandb_log(self, wandb_metrics: Optional[Dict] = None): - """Log training metrics including reward stats and accuracy.""" - if wandb_metrics is None: - wandb_metrics = {} - - if self.reward_buffer: - total = len(self.reward_buffer) - correct = sum(1 for r in self.reward_buffer if r == 1.0) - partial = sum(1 for r in self.reward_buffer if r == 0.5) - - wandb_metrics["train/avg_reward"] = sum(self.reward_buffer) / total - wandb_metrics["train/accuracy"] = correct / total - wandb_metrics["train/partial_match_rate"] = partial / total - wandb_metrics["train/total_rollouts"] = total - self.reward_buffer = [] - - await super().wandb_log(wandb_metrics) - - -if __name__ == "__main__": - TerminalTestEnv.cli() diff --git a/environments/tool_call_parsers/__init__.py b/environments/tool_call_parsers/__init__.py deleted file mode 100644 index 8bff3f9d1f0..00000000000 --- a/environments/tool_call_parsers/__init__.py +++ /dev/null @@ -1,120 +0,0 @@ -""" -Tool Call Parser Registry - -Client-side parsers that extract structured tool_calls from raw model output text. -Used in Phase 2 (VLLM server type) where ManagedServer's /generate endpoint returns -raw text without tool call parsing. - -Each parser is a standalone reimplementation of the corresponding VLLM parser's -non-streaming extract_tool_calls() logic. No VLLM dependency -- only standard library -(re, json, uuid) and openai types. - -Usage: - from environments.tool_call_parsers import get_parser - - parser = get_parser("hermes") - content, tool_calls = parser.parse(raw_model_output) - # content = text with tool call markup stripped - # tool_calls = list of ChatCompletionMessageToolCall objects, or None -""" - -import logging -from abc import ABC, abstractmethod -from typing import Dict, List, Optional, Tuple, Type - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, -) - -logger = logging.getLogger(__name__) - -# Type alias for parser return value -ParseResult = Tuple[Optional[str], Optional[List[ChatCompletionMessageToolCall]]] - - -class ToolCallParser(ABC): - """ - Base class for tool call parsers. - - Each parser knows how to extract structured tool_calls from a specific - model family's raw output text format. - """ - - @abstractmethod - def parse(self, text: str) -> ParseResult: - """ - Parse raw model output text for tool calls. - - Args: - text: Raw decoded text from the model's completion - - Returns: - Tuple of (content, tool_calls) where: - - content: text with tool call markup stripped (the message 'content' field), - or None if the entire output was tool calls - - tool_calls: list of ChatCompletionMessageToolCall objects, - or None if no tool calls were found - """ - raise NotImplementedError - - -# Global parser registry: name -> parser class -PARSER_REGISTRY: Dict[str, Type[ToolCallParser]] = {} - - -def register_parser(name: str): - """ - Decorator to register a parser class under a given name. - - Usage: - @register_parser("hermes") - class HermesToolCallParser(ToolCallParser): - ... - """ - - def decorator(cls: Type[ToolCallParser]) -> Type[ToolCallParser]: - PARSER_REGISTRY[name] = cls - return cls - - return decorator - - -def get_parser(name: str) -> ToolCallParser: - """ - Get a parser instance by name. - - Args: - name: Parser name (e.g., "hermes", "mistral", "llama3_json") - - Returns: - Instantiated parser - - Raises: - KeyError: If parser name is not found in registry - """ - if name not in PARSER_REGISTRY: - available = sorted(PARSER_REGISTRY.keys()) - raise KeyError( - f"Tool call parser '{name}' not found. Available parsers: {available}" - ) - return PARSER_REGISTRY[name]() - - -def list_parsers() -> List[str]: - """Return sorted list of registered parser names.""" - return sorted(PARSER_REGISTRY.keys()) - - -# Import all parser modules to trigger registration via @register_parser decorators -# Each module registers itself when imported -from environments.tool_call_parsers.hermes_parser import HermesToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.longcat_parser import LongcatToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.mistral_parser import MistralToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.llama_parser import LlamaToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.qwen_parser import QwenToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.deepseek_v3_parser import DeepSeekV3ToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.deepseek_v3_1_parser import DeepSeekV31ToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.kimi_k2_parser import KimiK2ToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.glm45_parser import Glm45ToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.glm47_parser import Glm47ToolCallParser # noqa: E402, F401 -from environments.tool_call_parsers.qwen3_coder_parser import Qwen3CoderToolCallParser # noqa: E402, F401 diff --git a/environments/tool_call_parsers/deepseek_v3_1_parser.py b/environments/tool_call_parsers/deepseek_v3_1_parser.py deleted file mode 100644 index 8456990c6ad..00000000000 --- a/environments/tool_call_parsers/deepseek_v3_1_parser.py +++ /dev/null @@ -1,72 +0,0 @@ -""" -DeepSeek V3.1 tool call parser. - -Similar to V3 but with a slightly different format: - <｜tool▁call▁begin｜>function_name<｜tool▁sep｜>arguments<｜tool▁call▁end｜> - -Note: V3 has type+name before the separator, V3.1 has name before and args after. - -Based on VLLM's DeepSeekV31ToolParser.extract_tool_calls() -""" - -import re -import uuid -from typing import List, Optional - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) - -from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser - - -@register_parser("deepseek_v3_1") -@register_parser("deepseek_v31") -class DeepSeekV31ToolCallParser(ToolCallParser): - """ - Parser for DeepSeek V3.1 tool calls. - - Slightly different regex than V3: function_name comes before the separator, - arguments come after (no type field, no json code block wrapper). - """ - - START_TOKEN = "<｜tool▁calls▁begin｜>" - - # Regex captures: function_name, function_arguments - PATTERN = re.compile( - r"<｜tool▁call▁begin｜>(?P.*?)<｜tool▁sep｜>(?P.*?)<｜tool▁call▁end｜>", - re.DOTALL, - ) - - def parse(self, text: str) -> ParseResult: - if self.START_TOKEN not in text: - return text, None - - try: - matches = self.PATTERN.findall(text) - if not matches: - return text, None - - tool_calls: List[ChatCompletionMessageToolCall] = [] - for match in matches: - func_name, func_args = match - tool_calls.append( - ChatCompletionMessageToolCall( - id=f"call_{uuid.uuid4().hex[:8]}", - type="function", - function=Function( - name=func_name.strip(), - arguments=func_args.strip(), - ), - ) - ) - - if not tool_calls: - return text, None - - content = text[: text.find(self.START_TOKEN)].strip() - return content if content else None, tool_calls - - except Exception: - return text, None diff --git a/environments/tool_call_parsers/deepseek_v3_parser.py b/environments/tool_call_parsers/deepseek_v3_parser.py deleted file mode 100644 index 61d23d5fecc..00000000000 --- a/environments/tool_call_parsers/deepseek_v3_parser.py +++ /dev/null @@ -1,89 +0,0 @@ -""" -DeepSeek V3 tool call parser. - -Format uses special unicode tokens: - <｜tool▁calls▁begin｜> - <｜tool▁call▁begin｜>type<｜tool▁sep｜>function_name - ```json - {"arg": "value"} - ``` - <｜tool▁call▁end｜> - <｜tool▁calls▁end｜> - -Fixes Issue #989: Support for multiple simultaneous tool calls. -""" - -import re -import uuid -import logging -from typing import List, Optional, Tuple - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) - -from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser - -logger = logging.getLogger(__name__) - -@register_parser("deepseek_v3") -class DeepSeekV3ToolCallParser(ToolCallParser): - """ - Parser for DeepSeek V3 tool calls. - - Uses special unicode tokens with fullwidth angle brackets and block elements. - Extracts type, function name, and JSON arguments from the structured format. - Ensures all tool calls are captured when the model executes multiple actions. - """ - - START_TOKEN = "<｜tool▁calls▁begin｜>" - - # Updated PATTERN: Using \s* instead of literal \n for increased robustness - # against variations in model formatting (Issue #989). - PATTERN = re.compile( - r"<｜tool▁call▁begin｜>(?P.*?)<｜tool▁sep｜>(?P.*?)\s*```json\s*(?P.*?)\s*```\s*<｜tool▁call▁end｜>", - re.DOTALL, - ) - - def parse(self, text: str) -> ParseResult: - """ - Parses the input text and extracts all available tool calls. - """ - if self.START_TOKEN not in text: - return text, None - - try: - # Using finditer to capture ALL tool calls in the sequence - matches = list(self.PATTERN.finditer(text)) - if not matches: - return text, None - - tool_calls: List[ChatCompletionMessageToolCall] = [] - - for match in matches: - func_name = match.group("function_name").strip() - func_args = match.group("function_arguments").strip() - - tool_calls.append( - ChatCompletionMessageToolCall( - id=f"call_{uuid.uuid4().hex[:8]}", - type="function", - function=Function( - name=func_name, - arguments=func_args, - ), - ) - ) - - if tool_calls: - # Content is text before the first tool call block - content_index = text.find(self.START_TOKEN) - content = text[:content_index].strip() - return content if content else None, tool_calls - - return text, None - - except Exception as e: - logger.error(f"Error parsing DeepSeek V3 tool calls: {e}") - return text, None diff --git a/environments/tool_call_parsers/glm45_parser.py b/environments/tool_call_parsers/glm45_parser.py deleted file mode 100644 index e92e29881f1..00000000000 --- a/environments/tool_call_parsers/glm45_parser.py +++ /dev/null @@ -1,109 +0,0 @@ -""" -GLM 4.5 (GLM-4-MoE) tool call parser. - -Format uses custom arg_key/arg_value tags rather than standard JSON: - function_name - param1value1 - param2value2 - - -Values are deserialized using json.loads -> ast.literal_eval -> raw string fallback. - -Based on VLLM's Glm4MoeModelToolParser.extract_tool_calls() -""" - -import ast -import json -import re -import uuid -from typing import Any, Dict, List, Optional - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) - -from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser - - -def _deserialize_value(value: str) -> Any: - """ - Try to deserialize a string value to its native Python type. - Attempts json.loads, then ast.literal_eval, then returns raw string. - """ - try: - return json.loads(value) - except (json.JSONDecodeError, TypeError): - pass - - try: - return ast.literal_eval(value) - except (ValueError, SyntaxError, TypeError): - pass - - return value - - -@register_parser("glm45") -class Glm45ToolCallParser(ToolCallParser): - """ - Parser for GLM 4.5 (GLM-4-MoE) tool calls. - - Uses ... tags with / pairs - instead of standard JSON arguments. - """ - - FUNC_CALL_REGEX = re.compile(r".*?", re.DOTALL) - FUNC_DETAIL_REGEX = re.compile(r"([^\n]*)\n(.*)", re.DOTALL) - FUNC_ARG_REGEX = re.compile( - r"(.*?)\s*(.*?)", re.DOTALL - ) - - START_TOKEN = "" - - def parse(self, text: str) -> ParseResult: - if self.START_TOKEN not in text: - return text, None - - try: - matched_calls = self.FUNC_CALL_REGEX.findall(text) - if not matched_calls: - return text, None - - tool_calls: List[ChatCompletionMessageToolCall] = [] - - for match in matched_calls: - detail = self.FUNC_DETAIL_REGEX.search(match) - if not detail: - continue - - func_name = detail.group(1).strip() - func_args_raw = detail.group(2) - - # Parse arg_key/arg_value pairs - pairs = self.FUNC_ARG_REGEX.findall(func_args_raw) if func_args_raw else [] - arg_dict: Dict[str, Any] = {} - for key, value in pairs: - arg_key = key.strip() - arg_val = _deserialize_value(value.strip()) - arg_dict[arg_key] = arg_val - - tool_calls.append( - ChatCompletionMessageToolCall( - id=f"call_{uuid.uuid4().hex[:8]}", - type="function", - function=Function( - name=func_name, - arguments=json.dumps(arg_dict, ensure_ascii=False), - ), - ) - ) - - if not tool_calls: - return text, None - - content = text[: text.find(self.START_TOKEN)].strip() - return content if content else None, tool_calls - - except Exception: - return text, None diff --git a/environments/tool_call_parsers/glm47_parser.py b/environments/tool_call_parsers/glm47_parser.py deleted file mode 100644 index 6631cf842ce..00000000000 --- a/environments/tool_call_parsers/glm47_parser.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -GLM 4.7 tool call parser. - -Same as GLM 4.5 but with slightly different regex patterns. -The tool_call tags may wrap differently and arg parsing handles -newlines between key/value pairs. - -Based on VLLM's Glm47MoeModelToolParser (extends Glm4MoeModelToolParser). -""" - -import re - -from environments.tool_call_parsers import ParseResult, register_parser -from environments.tool_call_parsers.glm45_parser import Glm45ToolCallParser - - -@register_parser("glm47") -class Glm47ToolCallParser(Glm45ToolCallParser): - """ - Parser for GLM 4.7 tool calls. - Extends GLM 4.5 with updated regex patterns. - """ - - def __init__(self): - super().__init__() - # GLM 4.7 uses a slightly different detail regex that includes - # the wrapper and optional arg_key content - self.FUNC_DETAIL_REGEX = re.compile( - r"(.*?)(.*?)?", re.DOTALL - ) - # GLM 4.7 handles newlines between arg_key and arg_value tags - self.FUNC_ARG_REGEX = re.compile( - r"(.*?)(?:\\n|\s)*(.*?)", - re.DOTALL, - ) diff --git a/environments/tool_call_parsers/hermes_parser.py b/environments/tool_call_parsers/hermes_parser.py deleted file mode 100644 index c6f911db04a..00000000000 --- a/environments/tool_call_parsers/hermes_parser.py +++ /dev/null @@ -1,75 +0,0 @@ -""" -Hermes tool call parser. - -Format: {"name": "func", "arguments": {...}} -Based on VLLM's Hermes2ProToolParser.extract_tool_calls() -""" - -import json -import re -import uuid -from typing import List, Optional, Tuple - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) - -from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser - - -@register_parser("hermes") -class HermesToolCallParser(ToolCallParser): - """ - Parser for Hermes-format tool calls. - - Matches ... tags containing JSON with "name" and "arguments". - Also handles unclosed at end-of-string (truncated generation). - """ - - # Matches both closed and unclosed tool_call tags - PATTERN = re.compile( - r"\s*(.*?)\s*|\s*(.*)", re.DOTALL - ) - - def parse(self, text: str) -> ParseResult: - if "" not in text: - return text, None - - try: - matches = self.PATTERN.findall(text) - if not matches: - return text, None - - tool_calls: List[ChatCompletionMessageToolCall] = [] - for match in matches: - # match is a tuple: (closed_content, unclosed_content) - raw_json = match[0] if match[0] else match[1] - if not raw_json.strip(): - continue - - tc_data = json.loads(raw_json) - if "name" not in tc_data: - continue - tool_calls.append( - ChatCompletionMessageToolCall( - id=f"call_{uuid.uuid4().hex[:8]}", - type="function", - function=Function( - name=tc_data["name"], - arguments=json.dumps( - tc_data.get("arguments", {}), ensure_ascii=False - ), - ), - ) - ) - - if not tool_calls: - return text, None - - # Content is everything before the first tag - content = text[: text.find("")].strip() - return content if content else None, tool_calls - - except Exception: - return text, None diff --git a/environments/tool_call_parsers/kimi_k2_parser.py b/environments/tool_call_parsers/kimi_k2_parser.py deleted file mode 100644 index 29f40fc2435..00000000000 --- a/environments/tool_call_parsers/kimi_k2_parser.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -Kimi K2 tool call parser. - -Format: - <|tool_calls_section_begin|> - <|tool_call_begin|>function_id:0<|tool_call_argument_begin|>{"arg": "val"}<|tool_call_end|> - <|tool_calls_section_end|> - -The function_id format is typically "functions.func_name:index" or "func_name:index". - -Based on VLLM's KimiK2ToolParser.extract_tool_calls() -""" - -import re -import uuid -from typing import List, Optional - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) - -from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser - - -@register_parser("kimi_k2") -class KimiK2ToolCallParser(ToolCallParser): - """ - Parser for Kimi K2 tool calls. - - Uses section begin/end tokens wrapping individual tool call begin/end tokens. - The tool_call_id contains the function name (after last dot, before colon). - """ - - # Support both singular and plural variants - START_TOKENS = [ - "<|tool_calls_section_begin|>", - "<|tool_call_section_begin|>", - ] - - # Regex captures: tool_call_id (e.g., "functions.get_weather:0"), function_arguments - PATTERN = re.compile( - r"<\|tool_call_begin\|>\s*(?P[^<]+:\d+)\s*" - r"<\|tool_call_argument_begin\|>\s*" - r"(?P(?:(?!<\|tool_call_begin\|>).)*?)\s*" - r"<\|tool_call_end\|>", - re.DOTALL, - ) - - def parse(self, text: str) -> ParseResult: - # Check for any variant of the start token - has_start = any(token in text for token in self.START_TOKENS) - if not has_start: - return text, None - - try: - matches = self.PATTERN.findall(text) - if not matches: - return text, None - - tool_calls: List[ChatCompletionMessageToolCall] = [] - for match in matches: - function_id, function_args = match - - # Extract function name from ID format: "functions.get_weather:0" -> "get_weather" - function_name = function_id.split(":")[0].split(".")[-1] - - tool_calls.append( - ChatCompletionMessageToolCall( - id=function_id, # Preserve the original ID format - type="function", - function=Function( - name=function_name, - arguments=function_args.strip(), - ), - ) - ) - - if not tool_calls: - return text, None - - # Content is everything before the tool calls section - earliest_start = len(text) - for token in self.START_TOKENS: - idx = text.find(token) - if idx >= 0 and idx < earliest_start: - earliest_start = idx - - content = text[:earliest_start].strip() - return content if content else None, tool_calls - - except Exception: - return text, None diff --git a/environments/tool_call_parsers/llama_parser.py b/environments/tool_call_parsers/llama_parser.py deleted file mode 100644 index 8eb2136a11a..00000000000 --- a/environments/tool_call_parsers/llama_parser.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -Llama 3.x / 4 tool call parser. - -Format: The model outputs JSON objects with "name" and "arguments" (or "parameters") keys. -May be preceded by <|python_tag|> token. Supports multiple JSON objects separated -by content or semicolons. - -Based on VLLM's Llama3JsonToolParser.extract_tool_calls() -""" - -import json -import re -import uuid -from typing import List, Optional - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) - -from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser - - -@register_parser("llama3_json") -@register_parser("llama4_json") -class LlamaToolCallParser(ToolCallParser): - """ - Parser for Llama 3.x and 4 JSON-format tool calls. - - Finds JSON objects containing "name" + ("arguments" or "parameters") keys. - Uses Python's json.JSONDecoder.raw_decode for robust extraction of - JSON objects from mixed text. - """ - - BOT_TOKEN = "<|python_tag|>" - - # Regex to find the start of potential JSON objects - JSON_START = re.compile(r"\{") - - def parse(self, text: str) -> ParseResult: - # Quick check: need either the bot token or a JSON brace - if self.BOT_TOKEN not in text and "{" not in text: - return text, None - - try: - decoder = json.JSONDecoder() - tool_calls: List[ChatCompletionMessageToolCall] = [] - end_index = -1 # Track where the last parsed JSON ended - - for match in self.JSON_START.finditer(text): - start = match.start() - # Skip if this brace is inside a previously parsed JSON object - if start <= end_index: - continue - - try: - obj, json_end = decoder.raw_decode(text[start:]) - end_index = start + json_end - - # Must have "name" and either "arguments" or "parameters" - name = obj.get("name") - args = obj.get("arguments", obj.get("parameters")) - - if not name or args is None: - continue - - # Normalize arguments to JSON string - if isinstance(args, dict): - args = json.dumps(args, ensure_ascii=False) - elif not isinstance(args, str): - args = json.dumps(args, ensure_ascii=False) - - tool_calls.append( - ChatCompletionMessageToolCall( - id=f"call_{uuid.uuid4().hex[:8]}", - type="function", - function=Function(name=name, arguments=args), - ) - ) - except (json.JSONDecodeError, KeyError, ValueError): - continue - - if not tool_calls: - return text, None - - # Content is everything before the first tool call JSON - # Find where the first tool call starts in the text - first_tc_start = text.find("{") - if self.BOT_TOKEN in text: - first_tc_start = text.find(self.BOT_TOKEN) - content = text[:first_tc_start].strip() if first_tc_start > 0 else None - - return content, tool_calls - - except Exception: - return text, None diff --git a/environments/tool_call_parsers/longcat_parser.py b/environments/tool_call_parsers/longcat_parser.py deleted file mode 100644 index afecdb86292..00000000000 --- a/environments/tool_call_parsers/longcat_parser.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -Longcat Flash Chat tool call parser. - -Same as Hermes but uses tags instead of . -Based on VLLM's LongcatFlashToolParser (extends Hermes2ProToolParser). -""" - -import json -import re -import uuid -from typing import List, Optional - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) - -from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser - - -@register_parser("longcat") -class LongcatToolCallParser(ToolCallParser): - """ - Parser for Longcat Flash Chat tool calls. - Identical logic to Hermes, just different tag names. - """ - - PATTERN = re.compile( - r"\s*(.*?)\s*|\s*(.*)", - re.DOTALL, - ) - - def parse(self, text: str) -> ParseResult: - if "" not in text: - return text, None - - try: - matches = self.PATTERN.findall(text) - if not matches: - return text, None - - tool_calls: List[ChatCompletionMessageToolCall] = [] - for match in matches: - raw_json = match[0] if match[0] else match[1] - if not raw_json.strip(): - continue - - tc_data = json.loads(raw_json) - tool_calls.append( - ChatCompletionMessageToolCall( - id=f"call_{uuid.uuid4().hex[:8]}", - type="function", - function=Function( - name=tc_data["name"], - arguments=json.dumps( - tc_data.get("arguments", {}), ensure_ascii=False - ), - ), - ) - ) - - if not tool_calls: - return text, None - - content = text[: text.find("")].strip() - return content if content else None, tool_calls - - except Exception: - return text, None diff --git a/environments/tool_call_parsers/mistral_parser.py b/environments/tool_call_parsers/mistral_parser.py deleted file mode 100644 index a23684e8739..00000000000 --- a/environments/tool_call_parsers/mistral_parser.py +++ /dev/null @@ -1,137 +0,0 @@ -""" -Mistral tool call parser. - -Supports two formats depending on tokenizer version: -- Pre-v11: content[TOOL_CALLS] [{"name": ..., "arguments": {...}}, ...] -- v11+: content[TOOL_CALLS]tool_name1{"arg": "val"}[TOOL_CALLS]tool_name2{"arg": "val"} - -Based on VLLM's MistralToolParser.extract_tool_calls() -The [TOOL_CALLS] token is the bot_token used by Mistral models. -""" - -import json -import uuid -from typing import List, Optional - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) - -from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser - - -def _generate_mistral_id() -> str: - """Mistral tool call IDs are 9-char alphanumeric strings.""" - import random - import string - - return "".join(random.choices(string.ascii_letters + string.digits, k=9)) - - -@register_parser("mistral") -class MistralToolCallParser(ToolCallParser): - """ - Parser for Mistral-format tool calls. - - Detects format by checking if the content after [TOOL_CALLS] starts with '[' - (pre-v11 JSON array) or with a tool name (v11+ format). - """ - - # The [TOOL_CALLS] token -- may appear as different strings depending on tokenizer - BOT_TOKEN = "[TOOL_CALLS]" - - def parse(self, text: str) -> ParseResult: - if self.BOT_TOKEN not in text: - return text, None - - try: - parts = text.split(self.BOT_TOKEN) - content = parts[0].strip() - raw_tool_calls = parts[1:] - - # Detect format: if the first raw part starts with '[', it's pre-v11 - first_raw = raw_tool_calls[0].strip() if raw_tool_calls else "" - is_pre_v11 = first_raw.startswith("[") or first_raw.startswith("{") - - tool_calls: List[ChatCompletionMessageToolCall] = [] - - if not is_pre_v11: - # v11+ format: [TOOL_CALLS]tool_name{args}[TOOL_CALLS]tool_name2{args2} - for raw in raw_tool_calls: - raw = raw.strip() - if not raw or "{" not in raw: - continue - - brace_idx = raw.find("{") - tool_name = raw[:brace_idx].strip() - args_str = raw[brace_idx:] - - # Validate and clean the JSON arguments - try: - parsed_args = json.loads(args_str) - args_str = json.dumps(parsed_args, ensure_ascii=False) - except json.JSONDecodeError: - pass # Keep raw if parsing fails - - tool_calls.append( - ChatCompletionMessageToolCall( - id=_generate_mistral_id(), - type="function", - function=Function(name=tool_name, arguments=args_str), - ) - ) - else: - # Pre-v11 format: [TOOL_CALLS] [{"name": ..., "arguments": {...}}] - try: - parsed = json.loads(first_raw) - if isinstance(parsed, dict): - parsed = [parsed] - - for tc in parsed: - if "name" not in tc: - continue - args = tc.get("arguments", {}) - if isinstance(args, dict): - args = json.dumps(args, ensure_ascii=False) - - tool_calls.append( - ChatCompletionMessageToolCall( - id=_generate_mistral_id(), - type="function", - function=Function( - name=tc["name"], arguments=args - ), - ) - ) - except json.JSONDecodeError: - # Fallback: extract JSON objects using raw_decode - decoder = json.JSONDecoder() - idx = 0 - while idx < len(first_raw): - try: - obj, end_idx = decoder.raw_decode(first_raw, idx) - if isinstance(obj, dict) and "name" in obj: - args = obj.get("arguments", {}) - if isinstance(args, dict): - args = json.dumps(args, ensure_ascii=False) - tool_calls.append( - ChatCompletionMessageToolCall( - id=_generate_mistral_id(), - type="function", - function=Function( - name=obj["name"], arguments=args - ), - ) - ) - idx = end_idx - except json.JSONDecodeError: - idx += 1 - - if not tool_calls: - return text, None - - return content if content else None, tool_calls - - except Exception: - return text, None diff --git a/environments/tool_call_parsers/qwen3_coder_parser.py b/environments/tool_call_parsers/qwen3_coder_parser.py deleted file mode 100644 index 042e46f7bf9..00000000000 --- a/environments/tool_call_parsers/qwen3_coder_parser.py +++ /dev/null @@ -1,163 +0,0 @@ -""" -Qwen3-Coder tool call parser. - -Format uses XML-style nested tags: - - - value - value2 - - - -Parameters are extracted from value tags and -type-converted using the schema if available, otherwise treated as strings. - -Based on VLLM's Qwen3CoderToolParser.extract_tool_calls() -""" - -import ast -import json -import re -import uuid -from typing import Any, Dict, List, Optional - -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) - -from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser - - -def _try_convert_value(value: str) -> Any: - """ - Try to convert a parameter value string to a native Python type. - Handles null, numbers, booleans, JSON objects/arrays, and falls back to string. - """ - stripped = value.strip() - - # Handle null - if stripped.lower() == "null": - return None - - # Try JSON first (handles objects, arrays, strings, numbers, booleans) - try: - return json.loads(stripped) - except (json.JSONDecodeError, TypeError): - pass - - # Try Python literal eval (handles tuples, etc.) - try: - return ast.literal_eval(stripped) - except (ValueError, SyntaxError, TypeError): - pass - - # Return as string - return stripped - - -@register_parser("qwen3_coder") -class Qwen3CoderToolCallParser(ToolCallParser): - """ - Parser for Qwen3-Coder XML-format tool calls. - - Uses nested XML tags: val - """ - - START_TOKEN = "" - FUNCTION_PREFIX = "(.*?)|(.*?)$", re.DOTALL - ) - - # Find function blocks within a tool_call - FUNCTION_REGEX = re.compile( - r"||(?=)|$)", - re.DOTALL, - ) - - def _parse_function_call(self, function_str: str) -> Optional[ChatCompletionMessageToolCall]: - """Parse a single ... block into a ToolCall.""" - try: - # Extract function name: everything before the first '>' - gt_idx = function_str.index(">") - func_name = function_str[:gt_idx].strip() - params_str = function_str[gt_idx + 1:] - - # Extract parameters - param_dict: Dict[str, Any] = {} - for match_text in self.PARAMETER_REGEX.findall(params_str): - if ">" not in match_text: - continue - eq_idx = match_text.index(">") - param_name = match_text[:eq_idx].strip() - param_value = match_text[eq_idx + 1:] - - # Clean up whitespace - if param_value.startswith("\n"): - param_value = param_value[1:] - if param_value.endswith("\n"): - param_value = param_value[:-1] - - param_dict[param_name] = _try_convert_value(param_value) - - return ChatCompletionMessageToolCall( - id=f"call_{uuid.uuid4().hex[:24]}", - type="function", - function=Function( - name=func_name, - arguments=json.dumps(param_dict, ensure_ascii=False), - ), - ) - except (ValueError, IndexError): - return None - - def parse(self, text: str) -> ParseResult: - if self.FUNCTION_PREFIX not in text: - return text, None - - try: - # Find all tool_call blocks - tc_matches = self.TOOL_CALL_REGEX.findall(text) - raw_blocks = [m[0] if m[0] else m[1] for m in tc_matches] - - # Fallback: if no tool_call tags, try the whole text - if not raw_blocks: - raw_blocks = [text] - - # Find function blocks within each tool_call - function_strs: List[str] = [] - for block in raw_blocks: - func_matches = self.FUNCTION_REGEX.findall(block) - function_strs.extend(m[0] if m[0] else m[1] for m in func_matches) - - if not function_strs: - return text, None - - # Parse each function call - tool_calls: List[ChatCompletionMessageToolCall] = [] - for func_str in function_strs: - tc = self._parse_function_call(func_str) - if tc is not None: - tool_calls.append(tc) - - if not tool_calls: - return text, None - - # Content before tool calls - first_tc = text.find(self.START_TOKEN) - if first_tc < 0: - first_tc = text.find(self.FUNCTION_PREFIX) - content = text[:first_tc].strip() if first_tc > 0 else None - - return content, tool_calls - - except Exception: - return text, None diff --git a/environments/tool_call_parsers/qwen_parser.py b/environments/tool_call_parsers/qwen_parser.py deleted file mode 100644 index 9c8a8141997..00000000000 --- a/environments/tool_call_parsers/qwen_parser.py +++ /dev/null @@ -1,19 +0,0 @@ -""" -Qwen 2.5 tool call parser. - -Uses the same format as Hermes. -Registered as a separate parser name for clarity when using --tool-parser=qwen. -""" - -from environments.tool_call_parsers import register_parser -from environments.tool_call_parsers.hermes_parser import HermesToolCallParser - - -@register_parser("qwen") -class QwenToolCallParser(HermesToolCallParser): - """ - Parser for Qwen 2.5 tool calls. - Same {"name": ..., "arguments": ...} format as Hermes. - """ - - pass # Identical format -- inherits everything from Hermes diff --git a/environments/tool_context.py b/environments/tool_context.py deleted file mode 100644 index 9756dadaf7c..00000000000 --- a/environments/tool_context.py +++ /dev/null @@ -1,473 +0,0 @@ -""" -ToolContext -- Unrestricted Tool Access for Reward Functions - -A per-rollout handle that gives reward/verification functions direct access to -ALL hermes-agent tools, scoped to the rollout's task_id. The same task_id means -the terminal/browser session is the SAME one the model used during its rollout -- -all state (files, processes, browser tabs) is preserved. - -The verifier author decides which tools to use. Nothing is hardcoded or gated. - -Example usage in a compute_reward(): - async def compute_reward(self, item, result, ctx): - # Run tests in the model's terminal sandbox - test = ctx.terminal("pytest -v") - if test["exit_code"] == 0: - return 1.0 - - # Check if a file was created - content = ctx.read_file("/workspace/solution.py") - if content.get("content"): - return 0.5 - - return 0.0 -""" - -import json -import logging -import os -from typing import Any, Dict, List, Optional - -import asyncio -import concurrent.futures - -from model_tools import handle_function_call -from tools.terminal_tool import cleanup_vm -from tools.browser_tool import cleanup_browser - -logger = logging.getLogger(__name__) - -# Thread pool for running sync tool calls that internally use asyncio.run() -_tool_executor = concurrent.futures.ThreadPoolExecutor(max_workers=4) - - -def _run_tool_in_thread(tool_name: str, arguments: Dict[str, Any], task_id: str) -> str: - """ - Run a tool call in a thread pool executor so backends that use asyncio.run() - internally (modal, docker, daytona) get a clean event loop. - - If we're already in an async context, executes handle_function_call() in a - disposable worker thread and blocks for the result. - If not (e.g., called from sync code), runs directly. - """ - try: - loop = asyncio.get_running_loop() - # We're in an async context -- need to run in thread - with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool: - future = pool.submit( - handle_function_call, tool_name, arguments, task_id - ) - return future.result(timeout=300) - except RuntimeError: - # No running event loop -- safe to call directly - return handle_function_call(tool_name, arguments, task_id) - - -class ToolContext: - """ - Open-ended access to all hermes-agent tools for a specific rollout. - - Passed to compute_reward() so verifiers can use any tool they need: - terminal commands, file reads/writes, web searches, browser automation, etc. - All calls share the rollout's task_id for session isolation. - """ - - def __init__(self, task_id: str): - self.task_id = task_id - - # ------------------------------------------------------------------------- - # Terminal tools - # ------------------------------------------------------------------------- - - def terminal(self, command: str, timeout: int = 180) -> Dict[str, Any]: - """ - Run a command in the rollout's terminal session. - - Args: - command: Shell command to execute - timeout: Command timeout in seconds - - Returns: - Dict with 'exit_code' (int) and 'output' (str) - """ - import os - backend = os.getenv("TERMINAL_ENV", "local") - logger.debug("ToolContext.terminal [%s backend] task=%s: %s", backend, self.task_id[:8], command[:100]) - - # Run via thread helper so modal/docker/daytona backends' asyncio.run() doesn't deadlock - result = _run_tool_in_thread( - "terminal", - {"command": command, "timeout": timeout}, - self.task_id, - ) - try: - return json.loads(result) - except json.JSONDecodeError: - return {"exit_code": -1, "output": result} - - # ------------------------------------------------------------------------- - # File tools - # ------------------------------------------------------------------------- - - def read_file(self, path: str) -> Dict[str, Any]: - """ - Read a file from the rollout's filesystem. - - Args: - path: File path to read - - Returns: - Dict with file content or error - """ - result = handle_function_call( - "read_file", {"path": path}, task_id=self.task_id - ) - try: - return json.loads(result) - except json.JSONDecodeError: - return {"error": result} - - def write_file(self, path: str, content: str) -> Dict[str, Any]: - """ - Write a TEXT file in the rollout's filesystem. - - Uses a shell heredoc under the hood, so this is only safe for text content. - For binary files (images, compiled artifacts, etc.), use upload_file() instead. - - Args: - path: File path to write - content: Text content to write - - Returns: - Dict with success status or error - """ - result = handle_function_call( - "write_file", {"path": path, "content": content}, task_id=self.task_id - ) - try: - return json.loads(result) - except json.JSONDecodeError: - return {"error": result} - - def upload_file(self, local_path: str, remote_path: str) -> Dict[str, Any]: - """ - Upload a local file to the rollout's sandbox (binary-safe). - - Unlike write_file() which passes content through a shell heredoc (text-only), - this method base64-encodes the file and decodes it inside the sandbox. - Safe for any file type: binaries, images, archives, etc. - - For large files (>1MB), the content is split into chunks to avoid - hitting shell command-length limits. - - Args: - local_path: Path to a local file on the host - remote_path: Destination path inside the sandbox - - Returns: - Dict with 'exit_code' and 'output' - """ - import base64 - from pathlib import Path as _Path - - local = _Path(local_path) - if not local.exists(): - return {"exit_code": -1, "output": f"Local file not found: {local_path}"} - - raw = local.read_bytes() - b64 = base64.b64encode(raw).decode("ascii") - - # Ensure parent directory exists in the sandbox - parent = str(_Path(remote_path).parent) - if parent not in {".", "/"}: - self.terminal(f"mkdir -p {parent}", timeout=10) - - # For small files, single command is fine - chunk_size = 60_000 # ~60KB per chunk (well within shell limits) - if len(b64) <= chunk_size: - result = self.terminal( - f"printf '%s' '{b64}' | base64 -d > {remote_path}", - timeout=30, - ) - else: - # For larger files, write base64 in chunks then decode - tmp_b64 = "/tmp/_hermes_upload.b64" - self.terminal(f": > {tmp_b64}", timeout=5) # truncate - for i in range(0, len(b64), chunk_size): - chunk = b64[i : i + chunk_size] - self.terminal(f"printf '%s' '{chunk}' >> {tmp_b64}", timeout=15) - result = self.terminal( - f"base64 -d {tmp_b64} > {remote_path} && rm -f {tmp_b64}", - timeout=30, - ) - - return result - - def upload_dir(self, local_dir: str, remote_dir: str) -> List[Dict[str, Any]]: - """ - Upload an entire local directory to the rollout's sandbox (binary-safe). - - Recursively uploads all files, preserving directory structure. - - Args: - local_dir: Path to a local directory on the host - remote_dir: Destination directory inside the sandbox - - Returns: - List of results, one per file uploaded - """ - from pathlib import Path as _Path - - local = _Path(local_dir) - if not local.exists() or not local.is_dir(): - return [{"exit_code": -1, "output": f"Local directory not found: {local_dir}"}] - - results = [] - for file_path in sorted(local.rglob("*")): - if file_path.is_file(): - relative = file_path.relative_to(local) - target = f"{remote_dir}/{relative}" - results.append(self.upload_file(str(file_path), target)) - return results - - def download_file(self, remote_path: str, local_path: str) -> Dict[str, Any]: - """ - Download a file from the rollout's sandbox to the host (binary-safe). - - The inverse of upload_file(). Base64-encodes the file inside the sandbox, - reads the encoded data through the terminal, and decodes it locally. - Safe for any file type. - - Args: - remote_path: Path to the file inside the sandbox - local_path: Destination path on the host - - Returns: - Dict with 'success' (bool) and 'bytes' (int) or 'error' (str) - """ - import base64 - from pathlib import Path as _Path - - # Base64-encode the file inside the sandbox and capture output - result = self.terminal( - f"base64 {remote_path} 2>/dev/null", - timeout=30, - ) - - if result.get("exit_code", -1) != 0: - return { - "success": False, - "error": f"Failed to read remote file: {result.get('output', '')}", - } - - b64_data = result.get("output", "").strip() - if not b64_data: - return {"success": False, "error": f"Remote file is empty or missing: {remote_path}"} - - try: - raw = base64.b64decode(b64_data) - except Exception as e: - return {"success": False, "error": f"Base64 decode failed: {e}"} - - # Write to local host filesystem - local = _Path(local_path) - local.parent.mkdir(parents=True, exist_ok=True) - local.write_bytes(raw) - - return {"success": True, "bytes": len(raw)} - - def download_dir(self, remote_dir: str, local_dir: str) -> List[Dict[str, Any]]: - """ - Download a directory from the rollout's sandbox to the host (binary-safe). - - Lists all files in the remote directory, then downloads each one. - Preserves directory structure. - - Args: - remote_dir: Path to the directory inside the sandbox - local_dir: Destination directory on the host - - Returns: - List of results, one per file downloaded - """ - from pathlib import Path as _Path - - # List files in the remote directory - ls_result = self.terminal( - f"find {remote_dir} -type f 2>/dev/null", - timeout=15, - ) - - if ls_result.get("exit_code", -1) != 0: - return [{"success": False, "error": f"Failed to list remote dir: {remote_dir}"}] - - file_list = ls_result.get("output", "").strip() - if not file_list: - return [{"success": False, "error": f"Remote directory is empty or missing: {remote_dir}"}] - - results = [] - for remote_file in file_list.splitlines(): - remote_file = remote_file.strip() - if not remote_file: - continue - # Compute the relative path to preserve directory structure - if remote_file.startswith(remote_dir): - relative = remote_file[len(remote_dir):].lstrip("/") - else: - relative = _Path(remote_file).name - local_file = str(_Path(local_dir) / relative) - results.append(self.download_file(remote_file, local_file)) - - return results - - def search(self, query: str, path: str = ".") -> Dict[str, Any]: - """ - Search for text in the rollout's filesystem. - - Args: - query: Search query - path: Directory to search in - - Returns: - Dict with search results - """ - result = handle_function_call( - "search_files", {"pattern": query, "path": path}, task_id=self.task_id - ) - try: - return json.loads(result) - except json.JSONDecodeError: - return {"error": result} - - # ------------------------------------------------------------------------- - # Web tools - # ------------------------------------------------------------------------- - - def web_search(self, query: str) -> Dict[str, Any]: - """ - Search the web. - - Args: - query: Search query - - Returns: - Dict with search results - """ - result = handle_function_call("web_search", {"query": query}) - try: - return json.loads(result) - except json.JSONDecodeError: - return {"error": result} - - def web_extract(self, urls: List[str]) -> Dict[str, Any]: - """ - Extract content from URLs. - - Args: - urls: List of URLs to extract content from - - Returns: - Dict with extracted content - """ - result = handle_function_call("web_extract", {"urls": urls}) - try: - return json.loads(result) - except json.JSONDecodeError: - return {"error": result} - - # ------------------------------------------------------------------------- - # Browser tools - # ------------------------------------------------------------------------- - - def browser_navigate(self, url: str) -> Dict[str, Any]: - """ - Navigate the rollout's browser session to a URL. - - Args: - url: URL to navigate to - - Returns: - Dict with page snapshot or error - """ - result = handle_function_call( - "browser_navigate", {"url": url}, task_id=self.task_id - ) - try: - return json.loads(result) - except json.JSONDecodeError: - return {"error": result} - - def browser_snapshot(self) -> Dict[str, Any]: - """ - Take a snapshot of the current browser page. - - Returns: - Dict with page content/accessibility snapshot - """ - result = handle_function_call( - "browser_snapshot", {}, task_id=self.task_id - ) - try: - return json.loads(result) - except json.JSONDecodeError: - return {"error": result} - - # ------------------------------------------------------------------------- - # Generic tool access - # ------------------------------------------------------------------------- - - def call_tool(self, tool_name: str, arguments: Dict[str, Any]) -> str: - """ - Call any hermes-agent tool by name. - - This is the generic escape hatch -- if a tool doesn't have a convenience - wrapper above, you can call it directly here. - - Args: - tool_name: Name of the tool (e.g., "vision_analyze", "skills_list") - arguments: Dict of arguments for the tool - - Returns: - Raw JSON string result from the tool - """ - return _run_tool_in_thread(tool_name, arguments, self.task_id) - - # ------------------------------------------------------------------------- - # Cleanup - # ------------------------------------------------------------------------- - - def cleanup(self): - """ - Release all resources (terminal VMs, browser sessions, background processes) - for this rollout. - - Called automatically by the base environment via try/finally after - compute_reward() completes. You generally don't need to call this yourself. - """ - # Kill any background processes from this rollout (safety net) - try: - from tools.process_registry import process_registry - killed = process_registry.kill_all(task_id=self.task_id) - if killed: - logger.debug("Process cleanup for task %s: killed %d process(es)", self.task_id, killed) - except Exception as e: - logger.debug("Process cleanup for task %s: %s", self.task_id, e) - - try: - cleanup_vm(self.task_id) - except Exception as e: - logger.debug("VM cleanup for task %s: %s", self.task_id, e) - - # Suppress browser_tool's noisy debug prints during cleanup. - # The cleanup still runs (safe), it just doesn't spam the console. - _prev_quiet = os.environ.get("HERMES_QUIET") - os.environ["HERMES_QUIET"] = "1" - try: - cleanup_browser(self.task_id) - except Exception as e: - logger.debug("Browser cleanup for task %s: %s", self.task_id, e) - finally: - if _prev_quiet is None: - os.environ.pop("HERMES_QUIET", None) - else: - os.environ["HERMES_QUIET"] = _prev_quiet diff --git a/environments/web_research_env.py b/environments/web_research_env.py deleted file mode 100644 index c637a7cbeae..00000000000 --- a/environments/web_research_env.py +++ /dev/null @@ -1,719 +0,0 @@ -""" -WebResearchEnv — RL Environment for Multi-Step Web Research -============================================================ - -Trains models to do accurate, efficient, multi-source web research. - -Reward signals: - - Answer correctness (LLM judge, 0.0–1.0) - - Source diversity (used ≥2 distinct domains) - - Efficiency (penalizes excessive tool calls) - - Tool usage (bonus for actually using web tools) - -Dataset: FRAMES benchmark (Google, 2024) — multi-hop factual questions - HuggingFace: google/frames-benchmark - Fallback: built-in sample questions (no HF token needed) - -Usage: - # Phase 1 (OpenAI-compatible server) - python environments/web_research_env.py serve \\ - --openai.base_url http://localhost:8000/v1 \\ - --openai.model_name YourModel \\ - --openai.server_type openai - - # Process mode (offline data generation) - python environments/web_research_env.py process \\ - --env.data_path_to_save_groups data/web_research.jsonl - - # Standalone eval - python environments/web_research_env.py evaluate \\ - --openai.base_url http://localhost:8000/v1 \\ - --openai.model_name YourModel - -Built by: github.com/jackx707 -Inspired by: GroceryMind — production Hermes agent doing live web research - across German grocery stores (firecrawl + hermes-agent) -""" - -from __future__ import annotations - -import asyncio -import json -import logging -import os -import random -import re -import sys -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple -from urllib.parse import urlparse - -from pydantic import Field - -# Ensure hermes-agent root is on path -_repo_root = Path(__file__).resolve().parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -# --------------------------------------------------------------------------- -# Optional HuggingFace datasets import -# --------------------------------------------------------------------------- -try: - from datasets import load_dataset - HF_AVAILABLE = True -except ImportError: - HF_AVAILABLE = False - -from atroposlib.envs.base import ScoredDataGroup -from atroposlib.envs.server_handling.server_manager import APIServerConfig -from atroposlib.type_definitions import Item - -from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig -from environments.agent_loop import AgentResult -from environments.tool_context import ToolContext - -logger = logging.getLogger(__name__) - -# --------------------------------------------------------------------------- -# Fallback sample dataset (used when HuggingFace is unavailable) -# Multi-hop questions requiring real web search to answer. -# --------------------------------------------------------------------------- -SAMPLE_QUESTIONS = [ - { - "question": "What is the current population of the capital city of the country that won the 2022 FIFA World Cup?", - "answer": "Buenos Aires has approximately 3 million people in the city proper, or around 15 million in the greater metro area.", - "difficulty": "medium", - "hops": 2, - }, - { - "question": "Who is the CEO of the company that makes the most widely used open-source container orchestration platform?", - "answer": "The Linux Foundation oversees Kubernetes. CNCF (Cloud Native Computing Foundation) is the specific body — it does not have a traditional CEO but has an executive director.", - "difficulty": "medium", - "hops": 2, - }, - { - "question": "What programming language was used to write the original version of the web framework used by Instagram?", - "answer": "Django, which Instagram was built on, is written in Python.", - "difficulty": "easy", - "hops": 2, - }, - { - "question": "In what year was the university founded where the inventor of the World Wide Web currently holds a professorship?", - "answer": "Tim Berners-Lee holds a professorship at MIT (founded 1861) and the University of Southampton (founded 1952).", - "difficulty": "hard", - "hops": 3, - }, - { - "question": "What is the latest stable version of the programming language that ranks #1 on the TIOBE index as of this year?", - "answer": "Python is currently #1 on TIOBE. The latest stable version should be verified via the official python.org site.", - "difficulty": "medium", - "hops": 2, - }, - { - "question": "How many employees does the parent company of Instagram have?", - "answer": "Meta Platforms (parent of Instagram) employs approximately 70,000+ people as of recent reports.", - "difficulty": "medium", - "hops": 2, - }, - { - "question": "What is the current interest rate set by the central bank of the country where the Eiffel Tower is located?", - "answer": "The European Central Bank sets rates for France/eurozone. The current rate should be verified — it has changed frequently in 2023-2025.", - "difficulty": "hard", - "hops": 2, - }, - { - "question": "Which company acquired the startup founded by the creator of Oculus VR?", - "answer": "Palmer Luckey founded Oculus VR, which was acquired by Facebook (now Meta). He later founded Anduril Industries.", - "difficulty": "medium", - "hops": 2, - }, - { - "question": "What is the market cap of the company that owns the most popular search engine in Russia?", - "answer": "Yandex (now split into separate entities after 2024 restructuring). Current market cap should be verified via financial sources.", - "difficulty": "hard", - "hops": 2, - }, - { - "question": "What was the GDP growth rate of the country that hosted the most recent Summer Olympics?", - "answer": "Paris, France hosted the 2024 Summer Olympics. France's recent GDP growth should be verified via World Bank or IMF data.", - "difficulty": "hard", - "hops": 2, - }, -] - - -# --------------------------------------------------------------------------- -# Configuration -# --------------------------------------------------------------------------- - -class WebResearchEnvConfig(HermesAgentEnvConfig): - """Configuration for the web research RL environment.""" - - # Reward weights - correctness_weight: float = Field( - default=0.6, - description="Weight for answer correctness in reward (LLM judge score).", - ) - tool_usage_weight: float = Field( - default=0.2, - description="Weight for tool usage signal (did the model actually use web tools?).", - ) - efficiency_weight: float = Field( - default=0.2, - description="Weight for efficiency signal (penalizes excessive tool calls).", - ) - diversity_bonus: float = Field( - default=0.1, - description="Bonus reward for citing ≥2 distinct domains.", - ) - - # Efficiency thresholds - efficient_max_calls: int = Field( - default=5, - description="Maximum tool calls before efficiency penalty begins.", - ) - heavy_penalty_calls: int = Field( - default=10, - description="Tool call count where efficiency penalty steepens.", - ) - - # Eval - eval_size: int = Field( - default=20, - description="Number of held-out items for evaluation.", - ) - eval_split_ratio: float = Field( - default=0.1, - description="Fraction of dataset to hold out for evaluation (0.0–1.0).", - ) - - # Dataset - dataset_name: str = Field( - default="google/frames-benchmark", - description="HuggingFace dataset name for research questions.", - ) - - -# --------------------------------------------------------------------------- -# Environment -# --------------------------------------------------------------------------- - -class WebResearchEnv(HermesAgentBaseEnv): - """ - RL environment for training multi-step web research skills. - - The model is given a factual question requiring 2-3 hops of web research - and must use web_search / web_extract tools to find and synthesize the answer. - - Reward is multi-signal: - 60% — answer correctness (LLM judge) - 20% — tool usage (did the model actually search the web?) - 20% — efficiency (penalizes >5 tool calls) - - Bonus +0.1 for source diversity (≥2 distinct domains cited). - """ - - name = "web-research" - env_config_cls = WebResearchEnvConfig - - # Default toolsets for this environment — web + file for saving notes - default_toolsets = ["web", "file"] - - @classmethod - def config_init(cls) -> Tuple[WebResearchEnvConfig, List[APIServerConfig]]: - """Default configuration for the web research environment.""" - env_config = WebResearchEnvConfig( - enabled_toolsets=["web", "file"], - max_agent_turns=15, - agent_temperature=1.0, - system_prompt=( - "You are a highly capable research agent. When asked a factual question, " - "always use web_search to find current, accurate information before answering. " - "Cite at least 2 sources. Be concise and accurate." - ), - group_size=4, - total_steps=1000, - steps_per_eval=100, - use_wandb=True, - wandb_name="web-research", - ) - - server_configs = [ - APIServerConfig( - base_url="https://openrouter.ai/api/v1", - model_name="anthropic/claude-sonnet-4.5", - server_type="openai", - api_key=os.getenv("OPENROUTER_API_KEY", ""), - health_check=False, - ) - ] - - return env_config, server_configs - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self._items: list[dict] = [] - self._eval_items: list[dict] = [] - self._index: int = 0 - - # Metrics tracking for wandb - self._reward_buffer: list[float] = [] - self._correctness_buffer: list[float] = [] - self._tool_usage_buffer: list[float] = [] - self._efficiency_buffer: list[float] = [] - self._diversity_buffer: list[float] = [] - - # ------------------------------------------------------------------ - # 1. Setup — load dataset - # ------------------------------------------------------------------ - - async def setup(self) -> None: - """Load the FRAMES benchmark or fall back to built-in samples.""" - if HF_AVAILABLE: - try: - logger.info("Loading FRAMES benchmark from HuggingFace...") - ds = load_dataset(self.config.dataset_name, split="test") - self._items = [ - { - "question": row["Prompt"], - "answer": row["Answer"], - "difficulty": row.get("reasoning_types", "unknown"), - "hops": 2, - } - for row in ds - ] - # Hold out for eval - eval_size = max( - self.config.eval_size, - int(len(self._items) * self.config.eval_split_ratio), - ) - random.shuffle(self._items) - self._eval_items = self._items[:eval_size] - self._items = self._items[eval_size:] - logger.info( - f"Loaded {len(self._items)} train / {len(self._eval_items)} eval items " - f"from FRAMES benchmark." - ) - return - except Exception as e: - logger.warning(f"Could not load FRAMES from HuggingFace: {e}. Using built-in samples.") - - # Fallback - random.shuffle(SAMPLE_QUESTIONS) - split = max(1, len(SAMPLE_QUESTIONS) * 8 // 10) - self._items = SAMPLE_QUESTIONS[:split] - self._eval_items = SAMPLE_QUESTIONS[split:] - logger.info( - f"Using built-in sample dataset: {len(self._items)} train / " - f"{len(self._eval_items)} eval items." - ) - - # ------------------------------------------------------------------ - # 2. get_next_item — return the next question - # ------------------------------------------------------------------ - - async def get_next_item(self) -> dict: - """Return the next item, cycling through the dataset.""" - if not self._items: - raise RuntimeError("Dataset is empty. Did you call setup()?") - item = self._items[self._index % len(self._items)] - self._index += 1 - return item - - # ------------------------------------------------------------------ - # 3. format_prompt — build the user-facing prompt - # ------------------------------------------------------------------ - - def format_prompt(self, item: dict) -> str: - """Format the research question as a task prompt.""" - return ( - f"Research the following question thoroughly using web search. " - f"You MUST search the web to find current, accurate information — " - f"do not rely solely on your training data.\n\n" - f"Question: {item['question']}\n\n" - f"Requirements:\n" - f"- Use web_search and/or web_extract tools to find information\n" - f"- Search at least 2 different sources\n" - f"- Provide a concise, accurate answer (2-4 sentences)\n" - f"- Cite the sources you used" - ) - - # ------------------------------------------------------------------ - # 4. compute_reward — multi-signal scoring - # ------------------------------------------------------------------ - - async def compute_reward( - self, - item: dict, - result: AgentResult, - ctx: ToolContext, - ) -> float: - """ - Multi-signal reward function: - - correctness_weight * correctness — LLM judge comparing answer to ground truth - tool_usage_weight * tool_used — binary: did the model use web tools? - efficiency_weight * efficiency — penalizes wasteful tool usage - + diversity_bonus — source diversity (≥2 distinct domains) - """ - # Extract final response from messages (last assistant message with content) - final_response = "" - tools_used: list[str] = [] - for msg in reversed(result.messages): - if msg.get("role") == "assistant" and msg.get("content") and not final_response: - final_response = msg["content"] - # Collect tool names from tool call messages - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - fn = tc.get("function", {}) if isinstance(tc, dict) else {} - name = fn.get("name", "") - if name: - tools_used.append(name) - tool_call_count: int = result.turns_used or len(tools_used) - - cfg = self.config - - # ---- Signal 1: Answer correctness (LLM judge) ---------------- - correctness = await self._llm_judge( - question=item["question"], - expected=item["answer"], - model_answer=final_response, - ) - - # ---- Signal 2: Web tool usage -------------------------------- - web_tools = {"web_search", "web_extract", "search", "firecrawl"} - tool_used = 1.0 if any(t in web_tools for t in tools_used) else 0.0 - - # ---- Signal 3: Efficiency ------------------------------------ - if tool_call_count <= cfg.efficient_max_calls: - efficiency = 1.0 - elif tool_call_count <= cfg.heavy_penalty_calls: - efficiency = 1.0 - (tool_call_count - cfg.efficient_max_calls) * 0.08 - else: - efficiency = max(0.0, 1.0 - (tool_call_count - cfg.efficient_max_calls) * 0.12) - - # ---- Bonus: Source diversity --------------------------------- - domains = self._extract_domains(final_response) - diversity = cfg.diversity_bonus if len(domains) >= 2 else 0.0 - - # ---- Combine ------------------------------------------------ - reward = ( - cfg.correctness_weight * correctness - + cfg.tool_usage_weight * tool_used - + cfg.efficiency_weight * efficiency - + diversity - ) - reward = min(1.0, max(0.0, reward)) # clamp to [0, 1] - - # Track for wandb - self._reward_buffer.append(reward) - self._correctness_buffer.append(correctness) - self._tool_usage_buffer.append(tool_used) - self._efficiency_buffer.append(efficiency) - self._diversity_buffer.append(diversity) - - logger.debug( - f"Reward breakdown — correctness={correctness:.2f}, " - f"tool_used={tool_used:.1f}, efficiency={efficiency:.2f}, " - f"diversity={diversity:.1f} → total={reward:.3f}" - ) - - return reward - - # ------------------------------------------------------------------ - # 5. evaluate — run on held-out eval split - # ------------------------------------------------------------------ - - async def evaluate(self, *args, **kwargs) -> None: - """Run evaluation on the held-out split using the full agent loop with tools. - - Each eval item runs through the same agent loop as training — - the model can use web_search, web_extract, etc. to research answers. - This measures actual agentic research capability, not just knowledge. - """ - import time - import uuid - from environments.agent_loop import HermesAgentLoop - from environments.tool_context import ToolContext - - items = self._eval_items - if not items: - logger.warning("No eval items available.") - return - - eval_size = min(self.config.eval_size, len(items)) - eval_items = items[:eval_size] - - logger.info(f"Running eval on {len(eval_items)} questions (with agent loop + tools)...") - start_time = time.time() - samples = [] - - # Resolve tools once for all eval items - tools, valid_names = self._resolve_tools_for_group() - - for i, item in enumerate(eval_items): - task_id = str(uuid.uuid4()) - logger.info(f"Eval [{i+1}/{len(eval_items)}]: {item['question'][:80]}...") - - try: - # Build messages - messages: List[Dict[str, Any]] = [] - if self.config.system_prompt: - messages.append({"role": "system", "content": self.config.system_prompt}) - messages.append({"role": "user", "content": self.format_prompt(item)}) - - # Run the full agent loop with tools - agent = HermesAgentLoop( - server=self.server, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=task_id, - temperature=0.0, # Deterministic for eval - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - budget_config=self.config.build_budget_config(), - ) - result = await agent.run(messages) - - # Extract final response and tool usage from messages - final_response = "" - tool_call_count = 0 - for msg in reversed(result.messages): - if msg.get("role") == "assistant" and msg.get("content") and not final_response: - final_response = msg["content"] - if msg.get("role") == "assistant" and msg.get("tool_calls"): - tool_call_count += len(msg["tool_calls"]) - - # Compute reward (includes LLM judge for correctness) - # Temporarily save buffer lengths so we can extract the - # correctness score without calling judge twice, and avoid - # polluting training metric buffers with eval data. - buf_len = len(self._correctness_buffer) - ctx = ToolContext(task_id) - try: - reward = await self.compute_reward(item, result, ctx) - finally: - ctx.cleanup() - - # Extract correctness from the buffer (compute_reward appended it) - # then remove eval entries from training buffers - correctness = ( - self._correctness_buffer[buf_len] - if len(self._correctness_buffer) > buf_len - else 0.0 - ) - # Roll back buffers to avoid polluting training metrics - for buf in ( - self._reward_buffer, self._correctness_buffer, - self._tool_usage_buffer, self._efficiency_buffer, - self._diversity_buffer, - ): - if len(buf) > buf_len: - buf.pop() - - samples.append({ - "prompt": item["question"], - "response": final_response[:500], - "expected": item["answer"], - "correctness": correctness, - "reward": reward, - "tool_calls": tool_call_count, - "turns": result.turns_used, - }) - - logger.info( - f" → correctness={correctness:.2f}, reward={reward:.3f}, " - f"tools={tool_call_count}, turns={result.turns_used}" - ) - - except Exception as e: - logger.error(f"Eval error on item: {e}") - samples.append({ - "prompt": item["question"], - "response": f"ERROR: {e}", - "expected": item["answer"], - "correctness": 0.0, - "reward": 0.0, - "tool_calls": 0, - "turns": 0, - }) - - end_time = time.time() - - # Compute aggregate metrics - correctness_scores = [s["correctness"] for s in samples] - rewards = [s["reward"] for s in samples] - tool_counts = [s["tool_calls"] for s in samples] - n = len(samples) - - eval_metrics = { - "eval/mean_correctness": sum(correctness_scores) / n if n else 0.0, - "eval/mean_reward": sum(rewards) / n if n else 0.0, - "eval/mean_tool_calls": sum(tool_counts) / n if n else 0.0, - "eval/tool_usage_rate": sum(1 for t in tool_counts if t > 0) / n if n else 0.0, - "eval/n_items": n, - } - - logger.info( - f"Eval complete — correctness={eval_metrics['eval/mean_correctness']:.3f}, " - f"reward={eval_metrics['eval/mean_reward']:.3f}, " - f"tool_usage={eval_metrics['eval/tool_usage_rate']:.0%}" - ) - - await self.evaluate_log( - metrics=eval_metrics, - samples=samples, - start_time=start_time, - end_time=end_time, - ) - - # ------------------------------------------------------------------ - # 6. wandb_log — custom metrics - # ------------------------------------------------------------------ - - async def wandb_log(self, wandb_metrics: Optional[Dict] = None) -> None: - """Log reward breakdown metrics to wandb.""" - if wandb_metrics is None: - wandb_metrics = {} - - if self._reward_buffer: - n = len(self._reward_buffer) - wandb_metrics["train/mean_reward"] = sum(self._reward_buffer) / n - wandb_metrics["train/mean_correctness"] = sum(self._correctness_buffer) / n - wandb_metrics["train/mean_tool_usage"] = sum(self._tool_usage_buffer) / n - wandb_metrics["train/mean_efficiency"] = sum(self._efficiency_buffer) / n - wandb_metrics["train/mean_diversity"] = sum(self._diversity_buffer) / n - wandb_metrics["train/total_rollouts"] = n - - # Accuracy buckets - wandb_metrics["train/correct_rate"] = ( - sum(1 for c in self._correctness_buffer if c >= 0.7) / n - ) - wandb_metrics["train/tool_usage_rate"] = ( - sum(1 for t in self._tool_usage_buffer if t > 0) / n - ) - - # Clear buffers - self._reward_buffer.clear() - self._correctness_buffer.clear() - self._tool_usage_buffer.clear() - self._efficiency_buffer.clear() - self._diversity_buffer.clear() - - await super().wandb_log(wandb_metrics) - - # ------------------------------------------------------------------ - # Private helpers - # ------------------------------------------------------------------ - - async def _llm_judge( - self, - question: str, - expected: str, - model_answer: str, - ) -> float: - """ - Use the server's LLM to judge answer correctness. - Falls back to keyword heuristic if LLM call fails. - """ - if not model_answer or not model_answer.strip(): - return 0.0 - - judge_prompt = ( - "You are an impartial judge evaluating the quality of an AI research answer.\n\n" - f"Question: {question}\n\n" - f"Reference answer: {expected}\n\n" - f"Model answer: {model_answer}\n\n" - "Score the model answer on a scale from 0.0 to 1.0 where:\n" - " 1.0 = fully correct and complete\n" - " 0.7 = mostly correct with minor gaps\n" - " 0.4 = partially correct\n" - " 0.1 = mentions relevant topic but wrong or very incomplete\n" - " 0.0 = completely wrong or no answer\n\n" - "Consider: factual accuracy, completeness, and relevance.\n" - 'Respond with ONLY a JSON object: {"score": , "reason": ""}' - ) - - try: - response = await self.server.chat_completion( - messages=[{"role": "user", "content": judge_prompt}], - n=1, - max_tokens=150, - temperature=0.0, - split="eval", - ) - text = response.choices[0].message.content if response.choices else "" - parsed = self._parse_judge_json(text) - if parsed is not None: - return float(parsed) - except Exception as e: - logger.debug(f"LLM judge failed: {e}. Using heuristic.") - - return self._heuristic_score(expected, model_answer) - - @staticmethod - def _parse_judge_json(text: str) -> Optional[float]: - """Extract the score float from LLM judge JSON response.""" - try: - clean = re.sub(r"```(?:json)?|```", "", text).strip() - data = json.loads(clean) - score = float(data.get("score", -1)) - if 0.0 <= score <= 1.0: - return score - except Exception: - match = re.search(r'"score"\s*:\s*([0-9.]+)', text) - if match: - score = float(match.group(1)) - if 0.0 <= score <= 1.0: - return score - return None - - @staticmethod - def _heuristic_score(expected: str, model_answer: str) -> float: - """Lightweight keyword overlap score as fallback.""" - stopwords = { - "the", "a", "an", "is", "are", "was", "were", "of", "in", "on", - "at", "to", "for", "with", "and", "or", "but", "it", "its", - "this", "that", "as", "by", "from", "be", "has", "have", "had", - } - - def tokenize(text: str) -> set: - tokens = re.findall(r'\b\w+\b', text.lower()) - return {t for t in tokens if t not in stopwords and len(t) > 2} - - expected_tokens = tokenize(expected) - answer_tokens = tokenize(model_answer) - - if not expected_tokens: - return 0.5 - - overlap = len(expected_tokens & answer_tokens) - union = len(expected_tokens | answer_tokens) - - jaccard = overlap / union if union > 0 else 0.0 - recall = overlap / len(expected_tokens) - return min(1.0, 0.4 * jaccard + 0.6 * recall) - - @staticmethod - def _extract_domains(text: str) -> set: - """Extract unique domains from URLs cited in the response.""" - urls = re.findall(r'https?://[^\s\)>\]"\']+', text) - domains = set() - for url in urls: - try: - parsed = urlparse(url) - domain = parsed.netloc.lower().lstrip("www.") - if domain: - domains.add(domain) - except Exception: - pass - return domains - - -# --------------------------------------------------------------------------- -# Entry point -# --------------------------------------------------------------------------- - -if __name__ == "__main__": - WebResearchEnv.cli() diff --git a/hermes_cli/config.py b/hermes_cli/config.py index c3a8152f4a7..a560e1e6a1e 100644 --- a/hermes_cli/config.py +++ b/hermes_cli/config.py @@ -2138,22 +2138,6 @@ OPTIONAL_ENV_VARS = { "password": True, "category": "tool", }, - "TINKER_API_KEY": { - "description": "Tinker API key for RL training", - "prompt": "Tinker API key", - "url": "https://tinker-console.thinkingmachines.ai/keys", - "tools": ["rl_start_training", "rl_check_status", "rl_stop_training"], - "password": True, - "category": "tool", - }, - "WANDB_API_KEY": { - "description": "Weights & Biases API key for experiment tracking", - "prompt": "WandB API key", - "url": "https://wandb.ai/authorize", - "tools": ["rl_get_results", "rl_check_status"], - "password": True, - "category": "tool", - }, "VOICE_TOOLS_OPENAI_KEY": { "description": "OpenAI API key for voice transcription (Whisper) and OpenAI TTS", "prompt": "OpenAI API Key (for Whisper STT + TTS)", @@ -4990,8 +4974,7 @@ def set_config_value(key: str, value: str): 'FAL_KEY', 'TELEGRAM_BOT_TOKEN', 'DISCORD_BOT_TOKEN', 'TERMINAL_SSH_HOST', 'TERMINAL_SSH_USER', 'TERMINAL_SSH_KEY', 'SUDO_PASSWORD', 'SLACK_BOT_TOKEN', 'SLACK_APP_TOKEN', - 'GITHUB_TOKEN', 'HONCHO_API_KEY', 'WANDB_API_KEY', - 'TINKER_API_KEY', + 'GITHUB_TOKEN', 'HONCHO_API_KEY', ] if key.upper() in api_keys or key.upper().endswith(('_API_KEY', '_TOKEN')) or key.upper().startswith('TERMINAL_SSH'): diff --git a/hermes_cli/doctor.py b/hermes_cli/doctor.py index a551d4d204e..c2035b03e6e 100644 --- a/hermes_cli/doctor.py +++ b/hermes_cli/doctor.py @@ -1595,28 +1595,6 @@ def run_doctor(args): for _issue in _r.issues: issues.append(_issue) - # ========================================================================= - # Check: Submodules - # ========================================================================= - print() - print(color("◆ Submodules", Colors.CYAN, Colors.BOLD)) - - # tinker-atropos (RL training backend) - tinker_dir = PROJECT_ROOT / "tinker-atropos" - if tinker_dir.exists() and (tinker_dir / "pyproject.toml").exists(): - if py_version >= (3, 11): - try: - __import__("tinker_atropos") - check_ok("tinker-atropos", "(RL training backend)") - except ImportError: - install_cmd = f"{_python_install_cmd()} -e ./tinker-atropos" - check_warn("tinker-atropos found but not installed", f"(run: {install_cmd})") - issues.append(f"Install tinker-atropos: {install_cmd}") - else: - check_warn("tinker-atropos requires Python 3.11+", f"(current: {py_version.major}.{py_version.minor})") - else: - check_warn("tinker-atropos not found", "(run: git submodule update --init --recursive)") - # ========================================================================= # Check: Tool Availability # ========================================================================= diff --git a/hermes_cli/setup.py b/hermes_cli/setup.py index 6a8bf950589..5d635b2c464 100644 --- a/hermes_cli/setup.py +++ b/hermes_cli/setup.py @@ -522,14 +522,6 @@ def _print_setup_summary(config: dict, hermes_home): elif managed_nous_tools_enabled() and subscription_features.nous_auth_present: tool_status.append(("Modal Execution (optional via Nous subscription)", True, None)) - # Tinker + WandB (RL training) - if get_env_value("TINKER_API_KEY") and get_env_value("WANDB_API_KEY"): - tool_status.append(("RL Training (Tinker)", True, None)) - elif get_env_value("TINKER_API_KEY"): - tool_status.append(("RL Training (Tinker)", False, "WANDB_API_KEY")) - else: - tool_status.append(("RL Training (Tinker)", False, "TINKER_API_KEY")) - # Home Assistant if get_env_value("HASS_TOKEN"): tool_status.append(("Smart Home (Home Assistant)", True, None)) diff --git a/hermes_cli/status.py b/hermes_cli/status.py index b4417091ca7..f2164ac8a4d 100644 --- a/hermes_cli/status.py +++ b/hermes_cli/status.py @@ -141,8 +141,6 @@ def show_status(args): "Browser Use": "BROWSER_USE_API_KEY", # Optional — local browser works without this "Browserbase": "BROWSERBASE_API_KEY", # Optional — direct credentials only "FAL": "FAL_KEY", - "Tinker": "TINKER_API_KEY", - "WandB": "WANDB_API_KEY", "ElevenLabs": "ELEVENLABS_API_KEY", "GitHub": "GITHUB_TOKEN", } diff --git a/hermes_cli/tools_config.py b/hermes_cli/tools_config.py index 87474040530..fc5b1acf5cf 100644 --- a/hermes_cli/tools_config.py +++ b/hermes_cli/tools_config.py @@ -71,7 +71,6 @@ CONFIGURABLE_TOOLSETS = [ ("delegation", "👥 Task Delegation", "delegate_task"), ("cronjob", "⏰ Cron Jobs", "create/list/update/pause/resume/run, with optional attached skills"), ("messaging", "📨 Cross-Platform Messaging", "send_message"), - ("rl", "🧪 RL Training", "Tinker-Atropos training tools"), ("homeassistant", "🏠 Home Assistant", "smart home device control"), ("spotify", "🎵 Spotify", "playback, search, playlists, library"), ("discord", "💬 Discord (read/participate)", "fetch messages, search members, create thread"), @@ -87,7 +86,7 @@ CONFIGURABLE_TOOLSETS = [ # Video gen is off by default — it's a niche, paid, slow feature. Users # who want it opt in via `hermes tools` → Video Generation, which walks # them through provider + model selection. -_DEFAULT_OFF_TOOLSETS = {"moa", "homeassistant", "rl", "spotify", "discord", "discord_admin", "video", "video_gen"} +_DEFAULT_OFF_TOOLSETS = {"moa", "homeassistant", "spotify", "discord", "discord_admin", "video", "video_gen"} # Platform-scoped toolsets: only appear in the `hermes tools` checklist for # these platforms, and only resolve/save for these platforms. A toolset @@ -424,22 +423,6 @@ TOOL_CATEGORIES = { }, ], }, - "rl": { - "name": "RL Training", - "icon": "🧪", - "requires_python": (3, 11), - "providers": [ - { - "name": "Tinker / Atropos", - "tag": "RL training platform", - "env_vars": [ - {"key": "TINKER_API_KEY", "prompt": "Tinker API key", "url": "https://tinker-console.thinkingmachines.ai/keys"}, - {"key": "WANDB_API_KEY", "prompt": "WandB API key", "url": "https://wandb.ai/authorize"}, - ], - "post_setup": "rl_training", - }, - ], - }, "langfuse": { "name": "Langfuse Observability", "icon": "📊", @@ -912,24 +895,6 @@ def _run_post_setup(post_setup_key: str): _print_warning(f" Spotify login failed: {exc}") _print_info(" Run manually: hermes auth spotify") - elif post_setup_key == "rl_training": - try: - __import__("tinker_atropos") - except ImportError: - tinker_dir = PROJECT_ROOT / "tinker-atropos" - if tinker_dir.exists() and (tinker_dir / "pyproject.toml").exists(): - _print_info(" Installing tinker-atropos submodule...") - result = _pip_install(["-e", str(tinker_dir)]) - if result.returncode == 0: - _print_success(" tinker-atropos installed") - else: - _print_warning(" tinker-atropos install failed - run manually:") - _print_info(' uv pip install -e "./tinker-atropos"') - else: - _print_warning(" tinker-atropos submodule not found - run:") - _print_info(" git submodule update --init --recursive") - _print_info(' uv pip install -e "./tinker-atropos"') - elif post_setup_key == "langfuse": # Install the langfuse SDK. try: diff --git a/model_tools.py b/model_tools.py index 0b9178111a5..db19bb67e53 100644 --- a/model_tools.py +++ b/model_tools.py @@ -97,9 +97,7 @@ def _run_async(coro): asyncio.run()'s create-and-destroy lifecycle. This is the single source of truth for sync->async bridging in tool - handlers. The RL paths (agent_loop.py, tool_context.py) also provide - outer thread-pool wrapping as defense-in-depth, but each handler is - self-protecting via this function. + handlers. Each handler is self-protecting via this function. """ try: loop = asyncio.get_running_loop() @@ -231,13 +229,6 @@ _LEGACY_TOOLSET_MAP = { "browser_vision", "browser_console" ], "cronjob_tools": ["cronjob"], - "rl_tools": [ - "rl_list_environments", "rl_select_environment", - "rl_get_current_config", "rl_edit_config", - "rl_start_training", "rl_check_status", - "rl_stop_training", "rl_get_results", - "rl_list_runs", "rl_test_inference" - ], "file_tools": ["read_file", "write_file", "patch", "search_files"], "tts_tools": ["text_to_speech"], } diff --git a/nix/hermes-agent.nix b/nix/hermes-agent.nix index ce8be16cfdd..6c391878cc5 100644 --- a/nix/hermes-agent.nix +++ b/nix/hermes-agent.nix @@ -192,7 +192,6 @@ stdenv.mkDerivation { source .venv/bin/activate uv pip install -e ".[all]" [ -d mini-swe-agent ] && uv pip install -e ./mini-swe-agent 2>/dev/null || true - [ -d tinker-atropos ] && uv pip install -e ./tinker-atropos 2>/dev/null || true mkdir -p .nix-stamps echo "$STAMP_VALUE" > "$STAMP" else diff --git a/optional-skills/mlops/hermes-atropos-environments/SKILL.md b/optional-skills/mlops/hermes-atropos-environments/SKILL.md deleted file mode 100644 index 6766c381014..00000000000 --- a/optional-skills/mlops/hermes-atropos-environments/SKILL.md +++ /dev/null @@ -1,303 +0,0 @@ ---- -name: hermes-atropos-environments -description: Build, test, and debug Hermes Agent RL environments for Atropos training. Covers the HermesAgentBaseEnv interface, reward functions, agent loop integration, evaluation with tools, wandb logging, and the three CLI modes (serve/process/evaluate). Use when creating, reviewing, or fixing RL environments in the hermes-agent repo. -version: 1.1.0 -author: Hermes Agent -license: MIT -platforms: [linux, macos, windows] -metadata: - hermes: - tags: [atropos, rl, environments, training, reinforcement-learning, reward-functions] - related_skills: [axolotl, fine-tuning-with-trl, lm-evaluation-harness] ---- - -# Hermes Agent Atropos Environments - -Guide for building RL environments in the hermes-agent repo that integrate with the Atropos training framework. - -## Architecture Overview - -``` -Atropos BaseEnv (atroposlib/envs/base.py) - └── HermesAgentBaseEnv (environments/hermes_base_env.py) - ├── Handles agent loop orchestration - ├── Handles tool resolution per group - ├── Handles ToolContext for reward verification - └── YOUR ENVIRONMENT (environments/your_env.py) - Only implements: setup, get_next_item, format_prompt, - compute_reward, evaluate, wandb_log -``` - -Hermes environments are special because they run a **multi-turn agent loop with tool calling** — not just single-turn completions. The base env handles the loop; you implement the task and scoring. - -## File Locations - -| File | Purpose | -|------|---------| -| `environments/hermes_base_env.py` | Base class with agent loop + tool resolution | -| `environments/agent_loop.py` | `HermesAgentLoop` + `AgentResult` dataclass | -| `environments/tool_context.py` | `ToolContext` for reward verification | -| `environments/tool_call_parsers.py` | Phase 2 tool call parsers (hermes, mistral, etc.) | -| `environments/your_env.py` | Your environment implementation | - -## Inference Setup — Ask the User First - -**IMPORTANT:** Before running any test, evaluation, or data generation command, always ask the user how they want to handle inference. Do NOT assume OpenRouter or any specific endpoint. Present these options: - -1. **OpenRouter** — Ask which model they want to use (e.g., `anthropic/claude-sonnet-4.5`, `google/gemini-2.5-pro`, `meta-llama/llama-3.3-70b-instruct`, etc.). Requires `OPENROUTER_API_KEY` in environment. -2. **Self-hosted VLLM endpoint** — Ask for their base URL (e.g., `http://localhost:8000/v1`) and model name. Set `--openai.server_type vllm`. -3. **Other OpenAI-compatible API** — Ask for the base URL, model name, and any required API key. Set `--openai.server_type openai` and `--openai.health_check false`. -4. **Local Atropos training server** — For `serve` mode with a live training loop. Default `http://localhost:8000/v1`. - -Once the user tells you their setup, use those values in all CLI commands for that session. Example prompts: - -> "Before I run this, how would you like to handle inference? -> 1. OpenRouter (I'll need your preferred model, e.g. claude-sonnet-4.5) -> 2. A self-hosted VLLM endpoint (give me the URL and model name) -> 3. Another OpenAI-compatible API (give me the URL, model, and any auth details) -> 4. Local Atropos training server (serve mode)" - -### Key flags by provider: - -| Provider | `--openai.server_type` | `--openai.health_check` | `--openai.api_key` | -|----------|----------------------|------------------------|-------------------| -| OpenRouter | `openai` | `false` | `$OPENROUTER_API_KEY` | -| VLLM (self-hosted) | `vllm` | (default) | (not needed) | -| Other OpenAI-compatible | `openai` | `false` | As needed | -| Local Atropos | (default) | (default) | (not needed) | - -## Required Methods - -### 1. `setup()` — Load dataset and initialize state - -```python -async def setup(self) -> None: - """Called once at startup. Load datasets, initialize state.""" - # Try HuggingFace first, fallback to built-in samples - try: - from datasets import load_dataset - ds = load_dataset("your/dataset", split="test") - self._items = [...] - except Exception: - self._items = BUILTIN_SAMPLES - - # Always split into train/eval - random.shuffle(self._items) - eval_size = max(20, int(len(self._items) * 0.1)) - self._eval_items = self._items[:eval_size] - self._items = self._items[eval_size:] -``` - -### 2. `get_next_item()` — Return next training item - -```python -async def get_next_item(self) -> dict: - """Return next item, cycling through dataset.""" - item = self._items[self._index % len(self._items)] - self._index += 1 - return item -``` - -### 3. `format_prompt(item)` — Convert item to user message - -```python -def format_prompt(self, item: dict) -> str: - """Convert a dataset item into the user-facing prompt.""" - return f"Research this question: {item['question']}" -``` - -### 4. `compute_reward(item, result, ctx)` — Score the rollout - -**CRITICAL**: `result` is an `AgentResult`, NOT a dict. It has these attributes: -- `result.messages` — List of message dicts (OpenAI format) -- `result.turns_used` — Number of LLM calls made -- `result.finished_naturally` — True if model stopped voluntarily -- `result.tool_errors` — List of ToolError objects - -**AgentResult does NOT have**: `final_response`, `tool_calls`, `tools_used`. -You must extract these from `result.messages`: - -```python -async def compute_reward(self, item, result: AgentResult, ctx: ToolContext) -> float: - # Extract final response (last assistant message with content) - final_response = "" - tools_used = [] - for msg in reversed(result.messages): - if msg.get("role") == "assistant" and msg.get("content") and not final_response: - final_response = msg["content"] - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - fn = tc.get("function", {}) if isinstance(tc, dict) else {} - name = fn.get("name", "") - if name: - tools_used.append(name) - - # Score using LLM judge, heuristic, or ToolContext verification - correctness = await self._llm_judge(item, final_response) - return correctness -``` - -`ctx` (ToolContext) gives you terminal/file access to the agent's sandbox for verification: -```python -# Run tests in the agent's sandbox -result = ctx.terminal("pytest /workspace/test.py") -return 1.0 if result["exit_code"] == 0 else 0.0 -``` - -### 5. `evaluate()` — Periodic evaluation with full agent loop - -**MUST use the full agent loop with tools**, not single-turn chat_completion. -The whole point of hermes-agent environments is agentic evaluation: - -```python -async def evaluate(self, *args, **kwargs) -> None: - import time, uuid - from environments.agent_loop import HermesAgentLoop - from environments.tool_context import ToolContext - - start_time = time.time() - tools, valid_names = self._resolve_tools_for_group() - samples = [] - - for item in self._eval_items[:self.config.eval_size]: - task_id = str(uuid.uuid4()) - messages = [] - if self.config.system_prompt: - messages.append({"role": "system", "content": self.config.system_prompt}) - messages.append({"role": "user", "content": self.format_prompt(item)}) - - agent = HermesAgentLoop( - server=self.server, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=task_id, - temperature=0.0, # Deterministic for eval - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - ) - result = await agent.run(messages) - - ctx = ToolContext(task_id) - try: - reward = await self.compute_reward(item, result, ctx) - finally: - ctx.cleanup() - - samples.append({"prompt": ..., "response": ..., "reward": reward}) - - eval_metrics = {"eval/mean_reward": ...} - await self.evaluate_log(metrics=eval_metrics, samples=samples, - start_time=start_time, end_time=time.time()) -``` - -### 6. `wandb_log()` — Custom metrics logging - -Always call `super().wandb_log()` at the end: - -```python -async def wandb_log(self, wandb_metrics=None): - if wandb_metrics is None: - wandb_metrics = {} - if self._reward_buffer: - n = len(self._reward_buffer) - wandb_metrics["train/mean_reward"] = sum(self._reward_buffer) / n - self._reward_buffer.clear() - await super().wandb_log(wandb_metrics) # MUST call super -``` - -**Pitfall**: `compute_reward` appends to metric buffers. During eval, this pollutes training metrics. Roll back buffer entries added during eval. - -## Config Class - -Always create a custom config subclass with Pydantic Field descriptors. Key inherited fields you can tune: `enabled_toolsets`, `max_agent_turns`, `agent_temperature`, `system_prompt`, `terminal_backend`, `group_size`, `steps_per_eval`, `total_steps`. - -## config_init() — Default Configuration - -Classmethod returning `(YourEnvConfig, [APIServerConfig(...)])`. Set server_type to "openai" for OpenRouter/external APIs. Load API key from environment variable. - -## Three CLI Modes - -```bash -# SERVE — Full training loop (connects to Atropos API server) -python environments/my_env.py serve --openai.base_url http://localhost:8000/v1 - -# PROCESS — Offline data generation (saves JSONL) -python environments/my_env.py process --env.total_steps 10 --env.group_size 1 \ - --env.use_wandb false --env.data_path_to_save_groups output.jsonl \ - --openai.base_url "" \ - --openai.model_name "" \ - --openai.server_type --openai.health_check false - -# EVALUATE — Standalone eval (runs setup + evaluate only) -python environments/my_env.py evaluate --env.eval_size 20 \ - --env.data_dir_to_save_evals /tmp/eval_results \ - --openai.base_url "" \ - --openai.model_name "" \ - --openai.server_type --openai.health_check false -``` - -Config priority: CLI args > YAML file > config_init() defaults. - -## Common Pitfalls - -1. **AgentResult has .messages, not .final_response** — Extract the final response by iterating reversed(result.messages) looking for the last assistant message with content. - -2. **evaluate() must use HermesAgentLoop, not chat_completion** — Single-turn chat_completion has no tools. The whole point of hermes-agent benchmarks is agentic evaluation with tool use. - -3. **Don't call _llm_judge twice** — If compute_reward already calls it, extract the score from the buffer instead of calling judge separately in evaluate(). - -4. **Eval pollutes training buffers** — compute_reward appends to metric buffers. During eval, roll back buffer entries to keep training metrics clean. - -5. **Always set health_check=false for OpenRouter** — OpenRouter has no /health endpoint. - -6. **Set data_dir_to_save_evals in evaluate mode** — Without it, results aren't saved. - -7. **default_toolsets class variable vs enabled_toolsets config** — The class variable is a hint; the config field is what actually controls tool resolution. - -8. **Tool call parsing in messages** — Tool calls are dicts with `{"function": {"name": ..., "arguments": ...}}`. Always check `isinstance(tc, dict)`. - -9. **ToolContext.cleanup()** — Always call in a finally block to release sandbox resources. - -10. **server_type must be "openai" for external APIs** — Without it, Atropos assumes a local VLLM server. - -11. **Always ask the user for their inference setup** — Never hardcode or assume a specific provider/model. See the "Inference Setup" section above. - -## Reward Function Patterns - -### LLM Judge (for open-ended tasks) -Use `self.server.chat_completion()` with a scoring prompt. Parse JSON response for score float. Always include a heuristic fallback (keyword overlap) for when the judge call fails. - -### Binary Verification (for code/terminal tasks) -Use `ctx.terminal("pytest test.py -q")` to run tests in the agent's sandbox. Return 1.0 for pass, 0.0 for fail. - -### Multi-Signal (combine multiple indicators) -Weight correctness (0.6) + tool usage (0.2) + efficiency (0.2) + optional bonuses. Clamp to [0, 1]. - -## Testing Your Environment - -1. **Import test**: `python -c "from environments.my_env import MyEnv; print('OK')"` -2. **Ask the user for inference setup** (see "Inference Setup" section above) -3. **Process mode** (1 item): Verify JSONL output has valid tokens, masks, scores -4. **Evaluate mode**: Verify full agent loop runs with tools, metrics logged correctly -5. **Check reward range**: Scores should be in [0, 1], not all identical - -## Minimum Implementation Checklist - -```python -class MyEnv(HermesAgentBaseEnv): - name = "my-env" - env_config_cls = MyEnvConfig - - @classmethod - def config_init(cls): ... # Default server + env config - async def setup(self): ... # Load dataset + train/eval split - async def get_next_item(self): ... # Cycle through training items - def format_prompt(self, item): ... # Item → user message string - async def compute_reward(self, item, result, ctx): ... # Score rollout - async def evaluate(self, *args, **kwargs): ... # Full agent loop eval - async def wandb_log(self, metrics=None): ... # Custom metrics + super() - -if __name__ == "__main__": - MyEnv.cli() -``` diff --git a/optional-skills/mlops/hermes-atropos-environments/references/agentresult-fields.md b/optional-skills/mlops/hermes-atropos-environments/references/agentresult-fields.md deleted file mode 100644 index bc6d6050581..00000000000 --- a/optional-skills/mlops/hermes-atropos-environments/references/agentresult-fields.md +++ /dev/null @@ -1,59 +0,0 @@ -# AgentResult Fields Reference - -`AgentResult` is defined in `environments/agent_loop.py` as a dataclass. - -## Fields - -| Field | Type | Description | -|-------|------|-------------| -| `messages` | `List[Dict[str, Any]]` | Full conversation history in OpenAI message format | -| `managed_state` | `Optional[Dict]` | ManagedServer.get_state() if Phase 2, else None | -| `turns_used` | `int` | Number of LLM calls made during the loop | -| `finished_naturally` | `bool` | True if model stopped calling tools on its own | -| `reasoning_per_turn` | `List[Optional[str]]` | Extracted reasoning content per turn | -| `tool_errors` | `List[ToolError]` | Tool errors encountered during the loop | - -## ToolError Fields - -| Field | Type | Description | -|-------|------|-------------| -| `turn` | `int` | Which turn the error occurred | -| `tool_name` | `str` | Name of the tool that failed | -| `arguments` | `str` | Arguments passed to the tool | -| `error` | `str` | Error message | -| `tool_result` | `str` | The result returned to the model | - -## Extracting Data from Messages - -Messages follow OpenAI format. Common patterns: - -```python -# Get final assistant response -for msg in reversed(result.messages): - if msg.get("role") == "assistant" and msg.get("content"): - final_response = msg["content"] - break - -# Get all tool names used -tools = [] -for msg in result.messages: - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - fn = tc.get("function", {}) if isinstance(tc, dict) else {} - tools.append(fn.get("name", "")) - -# Get tool results -for msg in result.messages: - if msg.get("role") == "tool": - tool_output = msg.get("content", "") - call_id = msg.get("tool_call_id", "") -``` - -## Fields that DO NOT EXIST - -These are common mistakes — AgentResult does NOT have: -- `final_response` — extract from messages -- `tool_calls` — extract from messages -- `tools_used` — extract from messages -- `output` — extract from messages -- `response` — extract from messages diff --git a/optional-skills/mlops/hermes-atropos-environments/references/atropos-base-env.md b/optional-skills/mlops/hermes-atropos-environments/references/atropos-base-env.md deleted file mode 100644 index e76895905e1..00000000000 --- a/optional-skills/mlops/hermes-atropos-environments/references/atropos-base-env.md +++ /dev/null @@ -1,65 +0,0 @@ -# Atropos BaseEnv Reference - -Source: `atroposlib/envs/base.py` (~2124 lines) - -## Abstract Methods (MUST implement) - -| Method | Signature | Description | -|--------|-----------|-------------| -| `get_next_item()` | `async def get_next_item(self) -> Item` | Return next item for trajectory. Return None to pause. | -| `evaluate()` | `async def evaluate(self, *args, **kwargs)` | Called every steps_per_eval steps. | -| `setup()` | `async def setup(self)` | Called once at start. Load datasets, init models. | -| `collect_trajectory()` | `async def collect_trajectory(self, item) -> Tuple[Optional[ScoredDataItem], List[Item]]` | Single rollout. Or override collect_trajectories instead. | - -## Overridable Methods - -| Method | Default Behavior | Override When | -|--------|-----------------|---------------| -| `collect_trajectories()` | Runs collect_trajectory group_size times in parallel | Batch generation, MCTS, coupled rollouts | -| `wandb_log()` | Logs completion lengths, rollout table, perf stats | Add custom metrics (always call super) | -| `config_init()` | Returns (env_config_cls(), ServerBaseline()) | Custom defaults + server configs | -| `postprocess_histories()` | Passthrough | Final processing before sending to trainer | -| `save_checkpoint()` | Saves JSON to checkpoint_dir | Custom serialization | -| `cleanup()` | No-op | Release resources after each rollout | - -## ScoredDataGroup Structure - -```python -ScoredDataGroup = TypedDict with: - tokens: List[List[int]] # Token IDs per rollout - masks: List[List[int]] # -100=prompt, token_id=completion - scores: List[float] # Score per rollout - advantages: Optional[...] # Per-token advantages - ref_logprobs: Optional[...] # Reference model logprobs - messages: Optional[...] # OpenAI-format messages - inference_logprobs: Optional[...] # Inference logprobs -``` - -## BaseEnvConfig Key Fields - -| Field | Default | Description | -|-------|---------|-------------| -| `group_size` | 4 | Responses grouped for scoring | -| `steps_per_eval` | 100 | Steps between evaluations | -| `max_token_length` | 2048 | Max token length for generations | -| `total_steps` | 1000 | Total training steps | -| `use_wandb` | True | Enable wandb logging | -| `tokenizer_name` | DeepHermes-3 | Tokenizer for token encoding | -| `ensure_scores_are_not_same` | True | Skip groups with identical scores | -| `worker_timeout` | 600 | Task timeout seconds | - -## Data Flow - -``` -env_manager() → add_train_workers() → handle_env() - → collect_trajectories() → postprocess_histories() - → handle_send_to_api() → training server -``` - -## Atropos Environment Statistics (82 environments analyzed) - -- 95% implement setup, collect_trajectories, evaluate, get_next_item -- 76% override wandb_log -- 54% have custom config class -- Most use collect_trajectories (plural), not collect_trajectory (singular) -- Common reward patterns: LLM-judge (~40), regex-extract (~35), code-exec (~12) diff --git a/optional-skills/mlops/hermes-atropos-environments/references/usage-patterns.md b/optional-skills/mlops/hermes-atropos-environments/references/usage-patterns.md deleted file mode 100644 index 5d4b3c1e820..00000000000 --- a/optional-skills/mlops/hermes-atropos-environments/references/usage-patterns.md +++ /dev/null @@ -1,199 +0,0 @@ -# Usage Patterns — Testing Environments and Evaluating Models - -## Pattern 1: Test Your Environment Works (process mode) - -Use `process` mode to verify your environment runs end-to-end before -committing. This generates trajectories without needing an Atropos -training server. - -**Before running:** Ask the user for their inference setup (see SKILL.md "Inference Setup" section). Replace ``, ``, and `` below with their chosen values. - -### Step 1: Run 1 trajectory - -```bash -cd ~/.hermes/hermes-agent -source venv/bin/activate - -python environments/your_env.py process \ - --env.total_steps 1 \ - --env.group_size 1 \ - --env.use_wandb false \ - --env.data_path_to_save_groups /tmp/test_output.jsonl \ - --openai.base_url "" \ - --openai.model_name "" \ - --openai.server_type \ - --openai.health_check false -``` - -### Step 2: Verify the output - -```python -import json -for line in open("/tmp/test_output.jsonl"): - data = json.loads(line) - print(f"Scores: {data.get('scores', [])}") - print(f"Token sequences: {len(data.get('tokens', []))}") - # Check messages include tool calls - for msg_list in data.get("messages", []): - roles = [m.get("role") for m in msg_list] - print(f"Roles: {roles}") - for m in reversed(msg_list): - if m.get("role") == "assistant" and m.get("content"): - print(f"Response: {m['content'][:200]}...") - break -``` - -### What to check: -- **Scores are not all 0.0** — if so, compute_reward is broken -- **Scores are in [0, 1]** — not negative, not >1 -- **Messages include "tool" role entries** — agent used tools -- **Token sequences are non-empty** -- **An HTML visualization is generated** next to the .jsonl - -### Common failures: -- `'AgentResult' object has no attribute 'X'` — accessing a field that doesn't exist. See agentresult-fields.md. -- Score always 0.0 — reward function erroring silently -- Score always 1.0 — verification too lenient or not running - - -## Pattern 2: Evaluate a Model (evaluate mode) - -Use `evaluate` mode to benchmark a model on your environment's eval -split. This runs the full agent loop with tools for each eval item. - -### Step 1: Run evaluation - -```bash -python environments/your_env.py evaluate \ - --env.eval_size 20 \ - --env.use_wandb false \ - --env.data_dir_to_save_evals /tmp/eval_results \ - --openai.base_url "" \ - --openai.model_name "" \ - --openai.server_type \ - --openai.health_check false -``` - -### Step 2: Read results - -Stdout shows a lighteval-compatible table: - -``` -Evaluation Results: your-env_eval -|Metric | Value| -|mean correctness| 0.850 | -|mean reward | 0.920 | -|mean tool calls | 4.300 | -|n items | 20 | -Evaluation completed in 367 seconds -``` - -JSON results saved to the eval directory: - -```python -import json -data = json.load(open("/tmp/eval_results/metrics.json")) -for metric, value in data["results"]["all"].items(): - print(f"{metric}: {value}") -``` - -### Step 3: Compare models - -Run evaluate with different models and compare the metrics.json files. - -### What to check: -- **"data_dir_to_save_evals is not set"** — you forgot the flag, results won't be saved -- **Tool usage rate = 0** — evaluate() is using chat_completion instead of HermesAgentLoop -- **All scores identical** — judge failing, falling back to heuristic -- **Very slow** — each item runs a full agent loop (~30-90s). Use `--env.eval_size 5` for quick checks. - - -## Pattern 3: Generate Training Data (process mode, larger scale) - -Generate trajectory data for offline training or analysis: - -```bash -python environments/your_env.py process \ - --env.total_steps 50 \ - --env.group_size 4 \ - --env.use_wandb false \ - --env.data_path_to_save_groups data/trajectories.jsonl \ - --openai.base_url "" \ - --openai.model_name "" \ - --openai.server_type \ - --openai.health_check false -``` - -### Analyze the distribution: - -```python -import json -scores = [] -for line in open("data/trajectories.jsonl"): - data = json.loads(line) - scores.extend(data.get("scores", [])) - -print(f"Total: {len(scores)}, Mean: {sum(scores)/len(scores):.3f}") -for bucket in [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]: - count = sum(1 for s in scores if abs(s - bucket) < 0.1) - print(f" {bucket:.1f}: {'█' * count} ({count})") -``` - -### What to check: -- **Score distribution has variance** — RL needs score variance. All-same scores are useless. - - -## Pattern 4: Full RL Training (serve mode) - -For actual RL training with Atropos: - -```bash -# Terminal 1: Start Atropos API server -run-api - -# Terminal 2: Start your environment -python environments/your_env.py serve \ - --config environments/your_env/default.yaml -``` - -For Phase 2 with VLLM: - -```bash -# Terminal 1: VLLM server -python -m vllm.entrypoints.openai.api_server --model your-model --port 8000 - -# Terminal 2: Atropos API -run-api - -# Terminal 3: Environment -python environments/your_env.py serve \ - --openai.base_url http://localhost:8000/v1 \ - --openai.model_name your-model \ - --openai.server_type vllm -``` - - -## Pattern 5: Quick Smoke Test - -Verify imports and config before spending money on API calls: - -```python -from environments.your_env import YourEnv -print(f"Name: {YourEnv.name}") -cfg, servers = YourEnv.config_init() -print(f"Toolsets: {cfg.enabled_toolsets}") -print(f"Server: {servers[0].model_name}") -print("All imports OK") -``` - - -## Timing Expectations - -| Mode | Items | Time per item | Total | -|------|-------|--------------|-------| -| process (1 item) | 1 | 30-90s | ~1 min | -| evaluate (5 items) | 5 | 30-90s | ~5 min | -| evaluate (20 items) | 20 | 30-90s | ~15-30 min | -| process (50 items) | 50 | 30-90s | ~30-75 min | - -Times are for cloud APIs with Claude Sonnet-class models. Local models may be faster or slower depending on hardware. diff --git a/pyproject.toml b/pyproject.toml index a880bcb05bf..982dc01be17 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -166,14 +166,6 @@ youtube = [ ] # `hermes dashboard` (localhost SPA + API). Not in core to keep the default install lean. web = ["fastapi==0.133.1", "uvicorn[standard]==0.41.0"] -rl = [ - "atroposlib @ git+https://github.com/NousResearch/atropos.git@c20c85256e5a45ad31edf8b7276e9c5ee1995a30", - "tinker @ git+https://github.com/thinking-machines-lab/tinker.git@30517b667f18a3dfb7ef33fb56cf686d5820ba2b", - "fastapi==0.133.1", - "uvicorn[standard]==0.41.0", - "wandb==0.25.1", -] -yc-bench = ["yc-bench @ git+https://github.com/collinear-ai/yc-bench.git@bfb0c88062450f46341bd9a5298903fc2e952a5c ; python_version >= '3.12'"] all = [ # Policy (2026-05-12): `[all]` includes only extras that genuinely # CAN'T be lazy-installed via `tools/lazy_deps.py` — i.e. things every @@ -215,7 +207,7 @@ hermes-agent = "run_agent:main" hermes-acp = "acp_adapter.entry:main" [tool.setuptools] -py-modules = ["run_agent", "model_tools", "toolsets", "batch_runner", "trajectory_compressor", "toolset_distributions", "cli", "hermes_bootstrap", "hermes_constants", "hermes_state", "hermes_time", "hermes_logging", "rl_cli", "utils"] +py-modules = ["run_agent", "model_tools", "toolsets", "batch_runner", "trajectory_compressor", "toolset_distributions", "cli", "hermes_bootstrap", "hermes_constants", "hermes_state", "hermes_time", "hermes_logging", "utils"] [tool.setuptools.package-data] hermes_cli = ["web_dist/**/*"] @@ -238,11 +230,7 @@ python-version = "3.13" unknown-argument = "warn" redundant-cast = "ignore" -[tool.ty.src] -exclude = ["tinker-atropos"] - [tool.ruff] -exclude = ["tinker-atropos"] preview = true # required for PLW1514 (unspecified-encoding) — preview rule [tool.ruff.lint] diff --git a/rl_cli.py b/rl_cli.py deleted file mode 100644 index e3996a29df6..00000000000 --- a/rl_cli.py +++ /dev/null @@ -1,446 +0,0 @@ -#!/usr/bin/env python3 -""" -RL Training CLI Runner - -Dedicated CLI runner for RL training workflows with: -- Extended timeouts for long-running training -- RL-focused system prompts -- Full toolset including RL training tools -- Special handling for 30-minute check intervals - -Usage: - python rl_cli.py "Train a model on GSM8k for math reasoning" - python rl_cli.py --interactive - python rl_cli.py --list-environments - -Environment Variables: - TINKER_API_KEY: API key for Tinker service (required) - WANDB_API_KEY: API key for WandB metrics (required) - OPENROUTER_API_KEY: API key for OpenRouter (required for agent) -""" - -import asyncio -import os -import sys -from pathlib import Path - -import fire -import yaml - -from hermes_constants import OPENROUTER_BASE_URL, get_hermes_home - -# Load .env from ~/.hermes/.env first, then project root as dev fallback. -# User-managed env files should override stale shell exports on restart. -_hermes_home = get_hermes_home() -_project_env = Path(__file__).parent / '.env' - -from hermes_cli.env_loader import load_hermes_dotenv - -_loaded_env_paths = load_hermes_dotenv(hermes_home=_hermes_home, project_env=_project_env) -for _env_path in _loaded_env_paths: - print(f"✅ Loaded environment variables from {_env_path}") - -# Set terminal working directory to tinker-atropos submodule -# This ensures terminal commands run in the right context for RL work -tinker_atropos_dir = Path(__file__).parent / 'tinker-atropos' -if tinker_atropos_dir.exists(): - os.environ['TERMINAL_CWD'] = str(tinker_atropos_dir) - os.environ['HERMES_QUIET'] = '1' # Disable temp subdirectory creation - print(f"📂 Terminal working directory: {tinker_atropos_dir}") -else: - # Fall back to hermes-agent directory if submodule not found - os.environ['TERMINAL_CWD'] = str(Path(__file__).parent) - os.environ['HERMES_QUIET'] = '1' - print(f"⚠️ tinker-atropos submodule not found, using: {Path(__file__).parent}") - -# Import agent and tools -from run_agent import AIAgent -from tools.rl_training_tool import get_missing_keys - - -# ============================================================================ -# Config Loading -# ============================================================================ - -DEFAULT_MODEL = "anthropic/claude-opus-4.5" -DEFAULT_BASE_URL = OPENROUTER_BASE_URL - - -def load_hermes_config() -> dict: - """ - Load configuration from ~/.hermes/config.yaml. - - Returns: - dict: Configuration with model, base_url, etc. - """ - config_path = _hermes_home / 'config.yaml' - - config = { - "model": DEFAULT_MODEL, - "base_url": DEFAULT_BASE_URL, - } - - if config_path.exists(): - try: - with open(config_path, "r", encoding='utf-8') as f: - file_config = yaml.safe_load(f) or {} - - # Get model from config - if "model" in file_config: - if isinstance(file_config["model"], str): - config["model"] = file_config["model"] - elif isinstance(file_config["model"], dict): - config["model"] = file_config["model"].get("default", DEFAULT_MODEL) - - # Get base_url if specified - if "base_url" in file_config: - config["base_url"] = file_config["base_url"] - - except Exception as e: - print(f"⚠️ Warning: Failed to load config.yaml: {e}") - - return config - - -# ============================================================================ -# RL-Specific Configuration -# ============================================================================ - -# Extended timeouts for long-running RL operations -RL_MAX_ITERATIONS = 200 # Allow many more iterations for long workflows - -# RL-focused system prompt -RL_SYSTEM_PROMPT = """You are an automated post-training engineer specializing in reinforcement learning for language models. - -## Your Capabilities - -You have access to RL training tools for running reinforcement learning on models through Tinker-Atropos: - -1. **DISCOVER**: Use `rl_list_environments` to see available RL environments -2. **INSPECT**: Read environment files to understand how they work (verifiers, data loading, rewards) -3. **INSPECT DATA**: Use terminal to explore HuggingFace datasets and understand their format -4. **CREATE**: Copy existing environments as templates, modify for your needs -5. **CONFIGURE**: Use `rl_select_environment` and `rl_edit_config` to set up training -6. **TEST**: Always use `rl_test_inference` before full training to validate your setup -7. **TRAIN**: Use `rl_start_training` to begin, `rl_check_status` to monitor -8. **EVALUATE**: Use `rl_get_results` and analyze WandB metrics to assess performance - -## Environment Files - -Environment files are located in: `tinker-atropos/tinker_atropos/environments/` - -Study existing environments to learn patterns. Look for: -- `load_dataset()` calls - how data is loaded -- `score_answer()` / `score()` - verification logic -- `get_next_item()` - prompt formatting -- `system_prompt` - instruction format -- `config_init()` - default configuration - -## Creating New Environments - -To create a new environment: -1. Read an existing environment file (e.g., gsm8k_tinker.py) -2. Use terminal to explore the target dataset format -3. Copy the environment file as a template -4. Modify the dataset loading, prompt formatting, and verifier logic -5. Test with `rl_test_inference` before training - -## Important Guidelines - -- **Always test before training**: Training runs take hours - verify everything works first -- **Monitor metrics**: Check WandB for reward/mean and percent_correct -- **Status check intervals**: Wait at least 30 minutes between status checks -- **Early stopping**: Stop training early if metrics look bad or stagnant -- **Iterate quickly**: Start with small total_steps to validate, then scale up - -## Available Toolsets - -You have access to: -- **RL tools**: Environment discovery, config management, training, testing -- **Terminal**: Run commands, inspect files, explore datasets -- **Web**: Search for information, documentation, papers -- **File tools**: Read and modify code files - -When asked to train a model, follow this workflow: -1. List available environments -2. Select and configure the appropriate environment -3. Test with sample prompts -4. Start training with conservative settings -5. Monitor progress and adjust as needed -""" - -# Toolsets to enable for RL workflows -RL_TOOLSETS = ["terminal", "web", "rl"] - - -# ============================================================================ -# Helper Functions -# ============================================================================ - -def check_requirements(): - """Check that all required environment variables and services are available.""" - errors = [] - - # Check API keys - if not os.getenv("OPENROUTER_API_KEY"): - errors.append("OPENROUTER_API_KEY not set - required for agent") - - missing_rl_keys = get_missing_keys() - if missing_rl_keys: - errors.append(f"Missing RL API keys: {', '.join(missing_rl_keys)}") - - if errors: - print("❌ Missing requirements:") - for error in errors: - print(f" - {error}") - print("\nPlease set these environment variables in your .env file or shell.") - return False - - return True - - -def check_tinker_atropos(): - """Check if tinker-atropos submodule is properly set up.""" - tinker_path = Path(__file__).parent / "tinker-atropos" - - if not tinker_path.exists(): - return False, "tinker-atropos submodule not found. Run: git submodule update --init" - - envs_path = tinker_path / "tinker_atropos" / "environments" - if not envs_path.exists(): - return False, f"environments directory not found at {envs_path}" - - env_files = list(envs_path.glob("*.py")) - env_files = [f for f in env_files if not f.name.startswith("_")] - - return True, {"path": str(tinker_path), "environments_count": len(env_files)} - - -def list_environments_sync(): - """List available environments (synchronous wrapper).""" - from tools.rl_training_tool import rl_list_environments - import json - - async def _list(): - result = await rl_list_environments() - return json.loads(result) - - return asyncio.run(_list()) - - -# ============================================================================ -# Main CLI -# ============================================================================ - -def main( - task: str = None, - model: str = None, - api_key: str = None, - base_url: str = None, - max_iterations: int = RL_MAX_ITERATIONS, - interactive: bool = False, - list_environments: bool = False, - check_server: bool = False, - verbose: bool = False, - save_trajectories: bool = True, -): - """ - RL Training CLI - Dedicated runner for RL training workflows. - - Args: - task: The training task/goal (e.g., "Train a model on GSM8k for math") - model: Model to use for the agent (reads from ~/.hermes/config.yaml if not provided) - api_key: OpenRouter API key (uses OPENROUTER_API_KEY env var if not provided) - base_url: API base URL (reads from config or defaults to OpenRouter) - max_iterations: Maximum agent iterations (default: 200 for long workflows) - interactive: Run in interactive mode (multiple conversations) - list_environments: Just list available RL environments and exit - check_server: Check if RL API server is running and exit - verbose: Enable verbose logging - save_trajectories: Save conversation trajectories (default: True for RL) - - Examples: - # Train on a specific environment - python rl_cli.py "Train a model on GSM8k math problems" - - # Interactive mode - python rl_cli.py --interactive - - # List available environments - python rl_cli.py --list-environments - - # Check server status - python rl_cli.py --check-server - """ - # Load config from ~/.hermes/config.yaml - config = load_hermes_config() - - # Use config values if not explicitly provided - if model is None: - model = config["model"] - if base_url is None: - base_url = config["base_url"] - - print("🎯 RL Training Agent") - print("=" * 60) - - # Handle setup check - if check_server: - print("\n🔍 Checking tinker-atropos setup...") - ok, result = check_tinker_atropos() - if ok: - print("✅ tinker-atropos submodule found") - print(f" Path: {result.get('path')}") - print(f" Environments found: {result.get('environments_count', 0)}") - - # Also check API keys - missing = get_missing_keys() - if missing: - print(f"\n⚠️ Missing API keys: {', '.join(missing)}") - print(" Add them to ~/.hermes/.env") - else: - print("✅ API keys configured") - else: - print(f"❌ tinker-atropos not set up: {result}") - print("\nTo set up:") - print(" git submodule update --init") - print(" pip install -e ./tinker-atropos") - return - - # Handle environment listing - if list_environments: - print("\n📋 Available RL Environments:") - print("-" * 40) - try: - data = list_environments_sync() - if "error" in data: - print(f"❌ Error: {data['error']}") - return - - envs = data.get("environments", []) - if not envs: - print("No environments found.") - print("\nMake sure tinker-atropos is set up:") - print(" git submodule update --init") - return - - for env in envs: - print(f"\n 📦 {env['name']}") - print(f" Class: {env['class_name']}") - print(f" Path: {env['file_path']}") - if env.get('description'): - desc = env['description'][:100] + "..." if len(env.get('description', '')) > 100 else env.get('description', '') - print(f" Description: {desc}") - - print(f"\n📊 Total: {len(envs)} environments") - print("\nUse `rl_select_environment(name)` to select an environment for training.") - except Exception as e: - print(f"❌ Error listing environments: {e}") - print("\nMake sure tinker-atropos is set up:") - print(" git submodule update --init") - print(" pip install -e ./tinker-atropos") - return - - # Check requirements - if not check_requirements(): - sys.exit(1) - - # Set default task if none provided - if not task and not interactive: - print("\n⚠️ No task provided. Use --interactive for interactive mode or provide a task.") - print("\nExamples:") - print(' python rl_cli.py "Train a model on GSM8k math problems"') - print(' python rl_cli.py "Create an RL environment for code generation"') - print(' python rl_cli.py --interactive') - return - - # Get API key - api_key = api_key or os.getenv("OPENROUTER_API_KEY") - if not api_key: - print("❌ No API key provided. Set OPENROUTER_API_KEY or pass --api-key") - sys.exit(1) - - print(f"\n🤖 Model: {model}") - print(f"🔧 Max iterations: {max_iterations}") - print(f"📁 Toolsets: {', '.join(RL_TOOLSETS)}") - print("=" * 60) - - # Create agent with RL configuration - agent = AIAgent( - base_url=base_url, - api_key=api_key, - model=model, - max_iterations=max_iterations, - enabled_toolsets=RL_TOOLSETS, - save_trajectories=save_trajectories, - verbose_logging=verbose, - quiet_mode=False, - ephemeral_system_prompt=RL_SYSTEM_PROMPT, - ) - - if interactive: - # Interactive mode - multiple conversations - print("\n🔄 Interactive RL Training Mode") - print("Type 'quit' or 'exit' to end the session.") - print("Type 'status' to check active training runs.") - print("-" * 40) - - while True: - try: - user_input = input("\n🎯 RL Task> ").strip() - - if not user_input: - continue - - if user_input.lower() in {'quit', 'exit', 'q'}: - print("\n👋 Goodbye!") - break - - if user_input.lower() == 'status': - # Quick status check - from tools.rl_training_tool import rl_list_runs - import json - result = asyncio.run(rl_list_runs()) - runs = json.loads(result) - if isinstance(runs, list) and runs: - print("\n📊 Active Runs:") - for run in runs: - print(f" - {run['run_id']}: {run['environment']} ({run['status']})") - else: - print("\nNo active runs.") - continue - - # Run the agent - print("\n" + "=" * 60) - agent.run_conversation(user_input) - print("\n" + "=" * 60) - - except KeyboardInterrupt: - print("\n\n👋 Interrupted. Goodbye!") - break - except Exception as e: - print(f"\n❌ Error: {e}") - if verbose: - import traceback - traceback.print_exc() - else: - # Single task mode - print(f"\n📝 Task: {task}") - print("-" * 40) - - try: - agent.run_conversation(task) - print("\n" + "=" * 60) - print("✅ Task completed") - except KeyboardInterrupt: - print("\n\n⚠️ Interrupted by user") - except Exception as e: - print(f"\n❌ Error: {e}") - if verbose: - import traceback - traceback.print_exc() - sys.exit(1) - - -if __name__ == "__main__": - fire.Fire(main) diff --git a/scripts/install.ps1 b/scripts/install.ps1 index 36cdf76ec70..2cf81969beb 100644 --- a/scripts/install.ps1 +++ b/scripts/install.ps1 @@ -958,20 +958,6 @@ except Exception: } } - # tinker-atropos (RL training) is optional and OFF by default. Matches the - # Linux/macOS install.sh behavior. Reasons not to auto-install: - # - tinker-atropos/pyproject.toml pulls atroposlib + tinker from git+https - # (NousResearch/atropos + thinking-machines-lab/tinker) which can fail on - # locked-down networks, flaky DNS, or rate-limited github.com and would - # previously kill the whole install mid-flight on Windows. - # - It's an RL training submodule, not part of the default agent surface. - # Users who don't do RL training never need it. - # Users who do want it can run the one-liner we print below. - if (Test-Path "tinker-atropos\pyproject.toml") { - Write-Info "tinker-atropos submodule found — skipping install (optional, for RL training)" - Write-Info " To install later: $UvCmd pip install -e `".\tinker-atropos`"" - } - Pop-Location Write-Success "All dependencies installed" diff --git a/scripts/install.sh b/scripts/install.sh index cf24912cc51..9c5db6b1c08 100755 --- a/scripts/install.sh +++ b/scripts/install.sh @@ -1051,11 +1051,6 @@ install_deps() { log_info "Termux note: matrix e2ee and local faster-whisper extras are excluded from .[termux-all] due to upstream Android wheel/toolchain blockers." log_info "Termux note: browser/WhatsApp tooling is not installed by default; see the Termux guide for optional follow-up steps." - if [ -d "tinker-atropos" ] && [ -f "tinker-atropos/pyproject.toml" ]; then - log_info "tinker-atropos submodule found — skipping install (optional, for RL training)" - log_info " To install later: $PIP_PYTHON -m pip install -e \"./tinker-atropos\"" - fi - log_success "All dependencies installed" return 0 fi @@ -1243,13 +1238,6 @@ PY log_success "Main package installed" - # tinker-atropos (RL training) is optional — skip by default. - # To enable RL tools: git submodule update --init tinker-atropos && uv pip install -e "./tinker-atropos" - if [ -d "tinker-atropos" ] && [ -f "tinker-atropos/pyproject.toml" ]; then - log_info "tinker-atropos submodule found — skipping install (optional, for RL training)" - log_info " To install: $UV_CMD pip install -e \"./tinker-atropos\"" - fi - log_success "All dependencies installed" } diff --git a/setup-hermes.sh b/setup-hermes.sh index 2aa773c1c9c..bdb8c1e9653 100755 --- a/setup-hermes.sh +++ b/setup-hermes.sh @@ -267,22 +267,6 @@ else fi # ============================================================================ -# Submodules (terminal backend + RL training) -# ============================================================================ - -echo -e "${CYAN}→${NC} Installing optional submodules..." - -# tinker-atropos (RL training backend) -if is_termux; then - echo -e "${CYAN}→${NC} Skipping tinker-atropos on Termux (not part of the tested Android path)" -elif [ -d "tinker-atropos" ] && [ -f "tinker-atropos/pyproject.toml" ]; then - $UV_CMD pip install -e "./tinker-atropos" && \ - echo -e "${GREEN}✓${NC} tinker-atropos installed" || \ - echo -e "${YELLOW}⚠${NC} tinker-atropos install failed (RL tools may not work)" -else - echo -e "${YELLOW}⚠${NC} tinker-atropos not found (run: git submodule update --init --recursive)" -fi - # ============================================================================ # Optional: ripgrep (for faster file search) # ============================================================================ diff --git a/tests/conftest.py b/tests/conftest.py index d9ae0c86ea6..aa2b1b1fbcb 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -101,7 +101,6 @@ _CREDENTIAL_NAMES = frozenset({ "RETAINDB_API_KEY", "HINDSIGHT_API_KEY", "HINDSIGHT_LLM_API_KEY", - "TINKER_API_KEY", "DAYTONA_API_KEY", "TWILIO_AUTH_TOKEN", "TELEGRAM_BOT_TOKEN", diff --git a/tests/environments/benchmarks/test_terminalbench2_env_security.py b/tests/environments/benchmarks/test_terminalbench2_env_security.py deleted file mode 100644 index b2610757762..00000000000 --- a/tests/environments/benchmarks/test_terminalbench2_env_security.py +++ /dev/null @@ -1,164 +0,0 @@ -"""Security tests for Terminal-Bench 2 archive extraction.""" - -import base64 -import importlib -import io -import sys -import tarfile -import types - -import pytest - - -def _stub_module(name: str, **attrs): - module = types.ModuleType(name) - for key, value in attrs.items(): - setattr(module, key, value) - return module - - -def _load_terminalbench_module(monkeypatch): - class _EvalHandlingEnum: - STOP_TRAIN = "stop_train" - - class _APIServerConfig: - def __init__(self, *args, **kwargs): - self.args = args - self.kwargs = kwargs - - class _AgentResult: - pass - - class _HermesAgentLoop: - pass - - class _HermesAgentBaseEnv: - pass - - class _HermesAgentEnvConfig: - pass - - class _ToolContext: - pass - - stub_modules = { - "atroposlib": _stub_module("atroposlib"), - "atroposlib.envs": _stub_module("atroposlib.envs"), - "atroposlib.envs.base": _stub_module( - "atroposlib.envs.base", - EvalHandlingEnum=_EvalHandlingEnum, - ), - "atroposlib.envs.server_handling": _stub_module("atroposlib.envs.server_handling"), - "atroposlib.envs.server_handling.server_manager": _stub_module( - "atroposlib.envs.server_handling.server_manager", - APIServerConfig=_APIServerConfig, - ), - "environments.agent_loop": _stub_module( - "environments.agent_loop", - AgentResult=_AgentResult, - HermesAgentLoop=_HermesAgentLoop, - ), - "environments.hermes_base_env": _stub_module( - "environments.hermes_base_env", - HermesAgentBaseEnv=_HermesAgentBaseEnv, - HermesAgentEnvConfig=_HermesAgentEnvConfig, - ), - "environments.tool_context": _stub_module( - "environments.tool_context", - ToolContext=_ToolContext, - ), - "tools.terminal_tool": _stub_module( - "tools.terminal_tool", - register_task_env_overrides=lambda *args, **kwargs: None, - clear_task_env_overrides=lambda *args, **kwargs: None, - cleanup_vm=lambda *args, **kwargs: None, - ), - } - - stub_modules["atroposlib"].envs = stub_modules["atroposlib.envs"] - stub_modules["atroposlib.envs"].base = stub_modules["atroposlib.envs.base"] - stub_modules["atroposlib.envs"].server_handling = stub_modules["atroposlib.envs.server_handling"] - stub_modules["atroposlib.envs.server_handling"].server_manager = stub_modules[ - "atroposlib.envs.server_handling.server_manager" - ] - - for name, module in stub_modules.items(): - monkeypatch.setitem(sys.modules, name, module) - - module_name = "environments.benchmarks.terminalbench_2.terminalbench2_env" - sys.modules.pop(module_name, None) - return importlib.import_module(module_name) - - -def _build_tar_b64(entries): - buf = io.BytesIO() - with tarfile.open(fileobj=buf, mode="w:gz") as tar: - for entry in entries: - kind = entry["kind"] - info = tarfile.TarInfo(entry["name"]) - - if kind == "dir": - info.type = tarfile.DIRTYPE - tar.addfile(info) - continue - - if kind == "file": - data = entry["data"].encode("utf-8") - info.size = len(data) - tar.addfile(info, io.BytesIO(data)) - continue - - if kind == "symlink": - info.type = tarfile.SYMTYPE - info.linkname = entry["target"] - tar.addfile(info) - continue - - raise ValueError(f"Unknown tar entry kind: {kind}") - - return base64.b64encode(buf.getvalue()).decode("ascii") - - -def test_extract_base64_tar_allows_safe_files(tmp_path, monkeypatch): - module = _load_terminalbench_module(monkeypatch) - archive = _build_tar_b64( - [ - {"kind": "dir", "name": "nested"}, - {"kind": "file", "name": "nested/hello.txt", "data": "hello"}, - ] - ) - - target = tmp_path / "extract" - module._extract_base64_tar(archive, target) - - assert (target / "nested" / "hello.txt").read_text(encoding="utf-8") == "hello" - - -def test_extract_base64_tar_rejects_path_traversal(tmp_path, monkeypatch): - module = _load_terminalbench_module(monkeypatch) - archive = _build_tar_b64( - [ - {"kind": "file", "name": "../escape.txt", "data": "owned"}, - ] - ) - - target = tmp_path / "extract" - with pytest.raises(ValueError, match="Unsafe archive member path"): - module._extract_base64_tar(archive, target) - - assert not (tmp_path / "escape.txt").exists() - - -def test_extract_base64_tar_rejects_symlinks(tmp_path, monkeypatch): - module = _load_terminalbench_module(monkeypatch) - archive = _build_tar_b64( - [ - {"kind": "symlink", "name": "link", "target": "../../escape.txt"}, - ] - ) - - target = tmp_path / "extract" - with pytest.raises(ValueError, match="Unsupported archive member type"): - module._extract_base64_tar(archive, target) - - assert not (target / "link").exists() diff --git a/tests/hermes_cli/test_set_config_value.py b/tests/hermes_cli/test_set_config_value.py index 617a915e322..39faa83cf58 100644 --- a/tests/hermes_cli/test_set_config_value.py +++ b/tests/hermes_cli/test_set_config_value.py @@ -39,8 +39,6 @@ class TestExplicitAllowlist: "OPENROUTER_API_KEY", "OPENAI_API_KEY", "ANTHROPIC_API_KEY", - "WANDB_API_KEY", - "TINKER_API_KEY", "HONCHO_API_KEY", "FIRECRAWL_API_KEY", "BROWSERBASE_API_KEY", diff --git a/tests/hermes_cli/test_setup_hermes_script.py b/tests/hermes_cli/test_setup_hermes_script.py index 7978e660a89..a4eb5ccb7d0 100644 --- a/tests/hermes_cli/test_setup_hermes_script.py +++ b/tests/hermes_cli/test_setup_hermes_script.py @@ -18,4 +18,3 @@ def test_setup_hermes_script_has_termux_path(): assert ".[termux]" in content assert "constraints-termux.txt" in content assert "$PREFIX/bin" in content - assert "Skipping tinker-atropos on Termux" in content diff --git a/tests/run_agent/test_agent_loop.py b/tests/run_agent/test_agent_loop.py deleted file mode 100644 index bd9e41b91e2..00000000000 --- a/tests/run_agent/test_agent_loop.py +++ /dev/null @@ -1,505 +0,0 @@ -""" -Tests for environments/agent_loop.py — HermesAgentLoop. - -Tests the multi-turn agent engine using mocked servers, without needing -real API keys or running servers. -""" - -import asyncio -import json -import sys -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional -from unittest.mock import MagicMock - -import pytest - -# Ensure repo root is importable -sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent)) - -try: - from environments.agent_loop import ( - AgentResult, - HermesAgentLoop, - ToolError, - _extract_reasoning_from_message, - resize_tool_pool, - ) -except ImportError: - pytest.skip("atroposlib not installed", allow_module_level=True) - - -# ─── Mock server infrastructure ───────────────────────────────────────── - - -@dataclass -class MockFunction: - name: str - arguments: str - - -@dataclass -class MockToolCall: - id: str - function: MockFunction - type: str = "function" - - -@dataclass -class MockMessage: - content: Optional[str] - role: str = "assistant" - tool_calls: Optional[List[MockToolCall]] = None - reasoning_content: Optional[str] = None - reasoning: Optional[str] = None - reasoning_details: Optional[list] = None - - -@dataclass -class MockChoice: - message: MockMessage - finish_reason: str = "stop" - index: int = 0 - - -@dataclass -class MockChatCompletion: - choices: List[MockChoice] - id: str = "chatcmpl-mock" - model: str = "mock-model" - - -class MockServer: - """ - Mock server that returns pre-configured responses in sequence. - Mimics the chat_completion() interface. - """ - - def __init__(self, responses: List[MockChatCompletion]): - self.responses = responses - self.call_count = 0 - self.call_history: List[Dict[str, Any]] = [] - - async def chat_completion(self, **kwargs) -> MockChatCompletion: - self.call_history.append(kwargs) - if self.call_count >= len(self.responses): - # Return a simple text response if we run out - return MockChatCompletion( - choices=[MockChoice(message=MockMessage(content="Done."))] - ) - resp = self.responses[self.call_count] - self.call_count += 1 - return resp - - -def make_text_response(content: str) -> MockChatCompletion: - """Create a simple text-only response (no tool calls).""" - return MockChatCompletion( - choices=[MockChoice(message=MockMessage(content=content))] - ) - - -def make_tool_response( - tool_name: str, - arguments: dict, - content: str = "", - tool_call_id: str = "call_001", -) -> MockChatCompletion: - """Create a response with a single tool call.""" - return MockChatCompletion( - choices=[ - MockChoice( - message=MockMessage( - content=content, - tool_calls=[ - MockToolCall( - id=tool_call_id, - function=MockFunction( - name=tool_name, - arguments=json.dumps(arguments), - ), - ) - ], - ), - finish_reason="tool_calls", - ) - ] - ) - - -# ─── Tests ─────────────────────────────────────────────────────────────── - - -class TestAgentResult: - def test_defaults(self): - result = AgentResult(messages=[]) - assert result.messages == [] - assert result.managed_state is None - assert result.turns_used == 0 - assert result.finished_naturally is False - assert result.reasoning_per_turn == [] - assert result.tool_errors == [] - - -class TestExtractReasoning: - def test_reasoning_content_field(self): - msg = MockMessage(content="hello", reasoning_content="I think...") - assert _extract_reasoning_from_message(msg) == "I think..." - - def test_reasoning_field(self): - msg = MockMessage(content="hello", reasoning="Let me consider...") - assert _extract_reasoning_from_message(msg) == "Let me consider..." - - def test_reasoning_details(self): - detail = MagicMock() - detail.text = "Detail reasoning" - msg = MockMessage(content="hello", reasoning_details=[detail]) - assert _extract_reasoning_from_message(msg) == "Detail reasoning" - - def test_reasoning_details_dict_format(self): - msg = MockMessage( - content="hello", - reasoning_details=[{"text": "Dict reasoning"}], - ) - assert _extract_reasoning_from_message(msg) == "Dict reasoning" - - def test_no_reasoning(self): - msg = MockMessage(content="hello") - assert _extract_reasoning_from_message(msg) is None - - def test_reasoning_content_takes_priority(self): - msg = MockMessage( - content="hello", - reasoning_content="First", - reasoning="Second", - ) - assert _extract_reasoning_from_message(msg) == "First" - - -class TestHermesAgentLoop: - """Test the agent loop with mock servers.""" - - @pytest.fixture - def basic_tools(self): - """Minimal tool schema for testing.""" - return [ - { - "type": "function", - "function": { - "name": "terminal", - "description": "Run a command", - "parameters": { - "type": "object", - "properties": { - "command": { - "type": "string", - "description": "Command to run", - } - }, - "required": ["command"], - }, - }, - }, - { - "type": "function", - "function": { - "name": "read_file", - "description": "Read a file", - "parameters": { - "type": "object", - "properties": { - "path": {"type": "string"}, - }, - "required": ["path"], - }, - }, - }, - ] - - @pytest.fixture - def valid_names(self): - return {"terminal", "read_file", "todo"} - - @pytest.mark.asyncio - async def test_simple_text_response(self, basic_tools, valid_names): - """Model responds with text only, no tool calls.""" - server = MockServer([make_text_response("Hello! How can I help?")]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - ) - messages = [{"role": "user", "content": "Hi"}] - result = await agent.run(messages) - - assert result.finished_naturally is True - assert result.turns_used == 1 - assert len(result.messages) >= 2 # user + assistant - assert result.messages[-1]["role"] == "assistant" - assert result.messages[-1]["content"] == "Hello! How can I help?" - - @pytest.mark.asyncio - async def test_tool_call_then_text(self, basic_tools, valid_names): - """Model calls a tool, then responds with text.""" - server = MockServer([ - make_tool_response("todo", {"todos": [{"id": "1", "content": "test", "status": "pending"}]}), - make_text_response("I created a todo for you."), - ]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - ) - messages = [{"role": "user", "content": "Create a todo"}] - result = await agent.run(messages) - - assert result.finished_naturally is True - assert result.turns_used == 2 - # Should have: user, assistant (tool_call), tool (result), assistant (text) - roles = [m["role"] for m in result.messages] - assert roles == ["user", "assistant", "tool", "assistant"] - - @pytest.mark.asyncio - async def test_max_turns_reached(self, basic_tools, valid_names): - """Model keeps calling tools until max_turns is hit.""" - # Create responses that always call a tool - responses = [ - make_tool_response("todo", {"todos": [{"id": str(i), "content": f"task {i}", "status": "pending"}]}, tool_call_id=f"call_{i}") - for i in range(10) - ] - server = MockServer(responses) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=3, - ) - messages = [{"role": "user", "content": "Keep going"}] - result = await agent.run(messages) - - assert result.finished_naturally is False - assert result.turns_used == 3 - - @pytest.mark.asyncio - async def test_unknown_tool_name(self, basic_tools, valid_names): - """Model calls a tool not in valid_tool_names.""" - server = MockServer([ - make_tool_response("nonexistent_tool", {"arg": "val"}), - make_text_response("OK, that didn't work."), - ]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - ) - messages = [{"role": "user", "content": "Call something weird"}] - result = await agent.run(messages) - - # Should record a tool error - assert len(result.tool_errors) >= 1 - assert result.tool_errors[0].tool_name == "nonexistent_tool" - - @pytest.mark.asyncio - async def test_empty_response(self, basic_tools, valid_names): - """Server returns empty response.""" - server = MockServer([MockChatCompletion(choices=[])]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - ) - messages = [{"role": "user", "content": "Hi"}] - result = await agent.run(messages) - - assert result.finished_naturally is False - assert result.turns_used == 1 - - @pytest.mark.asyncio - async def test_api_error_handling(self, basic_tools, valid_names): - """Server raises an exception.""" - - class FailingServer: - async def chat_completion(self, **kwargs): - raise ConnectionError("Server unreachable") - - agent = HermesAgentLoop( - server=FailingServer(), - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - ) - messages = [{"role": "user", "content": "Hi"}] - result = await agent.run(messages) - - assert result.finished_naturally is False - assert result.turns_used == 1 - - @pytest.mark.asyncio - async def test_tools_passed_to_server(self, basic_tools, valid_names): - """Verify tools are passed in the chat_completion kwargs.""" - server = MockServer([make_text_response("OK")]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - ) - messages = [{"role": "user", "content": "Hi"}] - await agent.run(messages) - - assert len(server.call_history) == 1 - assert "tools" in server.call_history[0] - assert server.call_history[0]["tools"] == basic_tools - - @pytest.mark.asyncio - async def test_extra_body_forwarded(self, basic_tools, valid_names): - """extra_body should be forwarded to server.""" - extra = {"provider": {"ignore": ["DeepInfra"]}} - server = MockServer([make_text_response("OK")]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - extra_body=extra, - ) - messages = [{"role": "user", "content": "Hi"}] - await agent.run(messages) - - assert server.call_history[0].get("extra_body") == extra - - @pytest.mark.asyncio - async def test_managed_state_returned(self, basic_tools, valid_names): - """If server has get_state(), result should include managed_state.""" - server = MockServer([make_text_response("OK")]) - server.get_state = lambda: {"nodes": [{"test": True}]} - - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - ) - messages = [{"role": "user", "content": "Hi"}] - result = await agent.run(messages) - - assert result.managed_state is not None - assert "nodes" in result.managed_state - - @pytest.mark.asyncio - async def test_no_managed_state_without_get_state(self, basic_tools, valid_names): - """Regular server without get_state() should return None managed_state.""" - server = MockServer([make_text_response("OK")]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - ) - messages = [{"role": "user", "content": "Hi"}] - result = await agent.run(messages) - - assert result.managed_state is None - - @pytest.mark.asyncio - async def test_memory_tool_blocked(self, basic_tools): - """Memory tool should return error in RL environments.""" - valid = {"terminal", "read_file", "todo", "memory"} - server = MockServer([ - make_tool_response("memory", {"action": "add", "target": "user", "content": "test"}), - make_text_response("Done"), - ]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid, - max_turns=10, - ) - messages = [{"role": "user", "content": "Remember this"}] - result = await agent.run(messages) - - # Find the tool response - tool_msgs = [m for m in result.messages if m["role"] == "tool"] - assert len(tool_msgs) >= 1 - tool_result = json.loads(tool_msgs[0]["content"]) - assert "error" in tool_result - assert "not available" in tool_result["error"].lower() - - @pytest.mark.asyncio - async def test_session_search_blocked(self, basic_tools): - """session_search should return error in RL environments.""" - valid = {"terminal", "read_file", "todo", "session_search"} - server = MockServer([ - make_tool_response("session_search", {"query": "test"}), - make_text_response("Done"), - ]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid, - max_turns=10, - ) - messages = [{"role": "user", "content": "Search sessions"}] - result = await agent.run(messages) - - tool_msgs = [m for m in result.messages if m["role"] == "tool"] - assert len(tool_msgs) >= 1 - tool_result = json.loads(tool_msgs[0]["content"]) - assert "error" in tool_result - - @pytest.mark.asyncio - async def test_reasoning_content_preserved(self, basic_tools, valid_names): - """Reasoning content should be extracted and preserved.""" - resp = MockChatCompletion( - choices=[ - MockChoice( - message=MockMessage( - content="The answer is 42.", - reasoning_content="Let me think about this step by step...", - ) - ) - ] - ) - server = MockServer([resp]) - agent = HermesAgentLoop( - server=server, - tool_schemas=basic_tools, - valid_tool_names=valid_names, - max_turns=10, - ) - messages = [{"role": "user", "content": "What is the meaning of life?"}] - result = await agent.run(messages) - - assert len(result.reasoning_per_turn) == 1 - assert result.reasoning_per_turn[0] == "Let me think about this step by step..." - - -class TestResizeToolPool: - def test_resize_works(self): - """resize_tool_pool should not raise.""" - resize_tool_pool(16) # Small pool for testing - resize_tool_pool(128) # Restore default - - def test_resize_shuts_down_previous_executor(self, monkeypatch): - """Replacing the global tool executor should shut down the old pool.""" - import environments.agent_loop as agent_loop_module - - old_executor = MagicMock() - new_executor = MagicMock() - - monkeypatch.setattr(agent_loop_module, "_tool_executor", old_executor) - monkeypatch.setattr( - agent_loop_module.concurrent.futures, - "ThreadPoolExecutor", - MagicMock(return_value=new_executor), - ) - - resize_tool_pool(16) - - old_executor.shutdown.assert_called_once_with(wait=False) - assert agent_loop_module._tool_executor is new_executor diff --git a/tests/run_agent/test_agent_loop_tool_calling.py b/tests/run_agent/test_agent_loop_tool_calling.py deleted file mode 100644 index 3b8d6ac5988..00000000000 --- a/tests/run_agent/test_agent_loop_tool_calling.py +++ /dev/null @@ -1,552 +0,0 @@ -"""Integration tests for HermesAgentLoop tool calling. - -Tests the full agent loop with real LLM calls via OpenRouter. -Uses stepfun/step-3.5-flash:free by default (zero cost), falls back -to anthropic/claude-sonnet-4 if the free model is unavailable. - -These tests verify: -1. Single tool call: model calls a tool, gets result, responds -2. Multi-tool call: model calls multiple tools in one turn -3. Multi-turn: model calls tools across multiple turns -4. Unknown tool rejection: model calling a non-existent tool gets an error -5. Max turns: loop stops when max_turns is reached -6. No tools: model responds without calling any tools -7. Tool error handling: tool execution errors are captured - -Run: - pytest tests/test_agent_loop_tool_calling.py -v - pytest tests/test_agent_loop_tool_calling.py -v -k "single" # run one test -""" - -import asyncio -import json -import os -import sys -from pathlib import Path -from typing import Any, Dict, List, Set -from unittest.mock import patch - -import pytest - -# pytestmark removed — tests skip gracefully via OPENROUTER_API_KEY check on line 59 - -# Ensure repo root is importable -_repo_root = Path(__file__).resolve().parent.parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -try: - from environments.agent_loop import AgentResult, HermesAgentLoop - from atroposlib.envs.server_handling.openai_server import OpenAIServer # noqa: F401 -except ImportError: - pytest.skip("atroposlib not installed", allow_module_level=True) - - -# ========================================================================= -# Test infrastructure -# ========================================================================= - -# Models to try, in order of preference (free first) -_MODELS = [ - "stepfun/step-3.5-flash:free", - "google/gemini-2.0-flash-001", - "anthropic/claude-sonnet-4", -] - -def _get_api_key(): - key = os.getenv("OPENROUTER_API_KEY", "") - if not key: - pytest.skip("OPENROUTER_API_KEY not set") - return key - - -def _make_server(model: str = None): - """Create an OpenAI server for testing.""" - from atroposlib.envs.server_handling.openai_server import OpenAIServer - from atroposlib.envs.server_handling.server_manager import APIServerConfig - - config = APIServerConfig( - base_url="https://openrouter.ai/api/v1", - model_name=model or _MODELS[0], - server_type="openai", - api_key=_get_api_key(), - health_check=False, - ) - return OpenAIServer(config) - - -async def _try_models(test_fn): - """Try running a test with each model until one works.""" - last_error = None - for model in _MODELS: - try: - server = _make_server(model) - return await test_fn(server, model) - except Exception as e: - last_error = e - if "rate" in str(e).lower() or "limit" in str(e).lower(): - continue # Rate limited, try next model - raise # Real error - pytest.skip(f"All models failed. Last error: {last_error}") - - -# ========================================================================= -# Fake tools for testing -# ========================================================================= - -# Simple calculator tool -CALC_TOOL = { - "type": "function", - "function": { - "name": "calculate", - "description": "Calculate a math expression. Returns the numeric result.", - "parameters": { - "type": "object", - "properties": { - "expression": { - "type": "string", - "description": "Math expression to evaluate, e.g. '2 + 3'" - } - }, - "required": ["expression"], - }, - }, -} - -# Weather lookup tool -WEATHER_TOOL = { - "type": "function", - "function": { - "name": "get_weather", - "description": "Get the current weather for a city. Returns temperature and conditions.", - "parameters": { - "type": "object", - "properties": { - "city": { - "type": "string", - "description": "City name, e.g. 'Tokyo'" - } - }, - "required": ["city"], - }, - }, -} - -# Lookup tool (always succeeds) -LOOKUP_TOOL = { - "type": "function", - "function": { - "name": "lookup", - "description": "Look up a fact. Returns a short answer string.", - "parameters": { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "What to look up" - } - }, - "required": ["query"], - }, - }, -} - -# Error tool (always fails) -ERROR_TOOL = { - "type": "function", - "function": { - "name": "failing_tool", - "description": "A tool that always fails with an error.", - "parameters": { - "type": "object", - "properties": { - "input": {"type": "string"} - }, - "required": ["input"], - }, - }, -} - - -def _fake_tool_handler(tool_name: str, args: Dict[str, Any], **kwargs) -> str: - """Handle fake tool calls for testing.""" - if tool_name == "calculate": - expr = args.get("expression", "0") - try: - # Safe eval for simple math - result = eval(expr, {"__builtins__": {}}, {}) - return json.dumps({"result": result}) - except Exception as e: - return json.dumps({"error": str(e)}) - - elif tool_name == "get_weather": - city = args.get("city", "Unknown") - # Return canned weather - return json.dumps({ - "city": city, - "temperature": 22, - "conditions": "sunny", - "humidity": 45, - }) - - elif tool_name == "lookup": - query = args.get("query", "") - return json.dumps({"answer": f"The answer to '{query}' is 42."}) - - elif tool_name == "failing_tool": - raise RuntimeError("This tool always fails!") - - return json.dumps({"error": f"Unknown tool: {tool_name}"}) - - -# ========================================================================= -# Tests -# ========================================================================= - -@pytest.mark.asyncio -async def test_single_tool_call(): - """Model should call a single tool, get the result, and respond.""" - - async def _run(server, model): - agent = HermesAgentLoop( - server=server, - tool_schemas=[WEATHER_TOOL], - valid_tool_names={"get_weather"}, - max_turns=5, - temperature=0.0, - max_tokens=500, - ) - - messages = [ - {"role": "user", "content": "What's the weather in Tokyo? Use the get_weather tool."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - assert isinstance(result, AgentResult) - assert result.turns_used >= 2, f"Expected at least 2 turns (tool call + response), got {result.turns_used}" - - # Verify a tool call happened - tool_calls_found = False - for msg in result.messages: - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - if tc["function"]["name"] == "get_weather": - tool_calls_found = True - args = json.loads(tc["function"]["arguments"]) - assert "city" in args - assert tool_calls_found, "Model should have called get_weather" - - # Verify tool result is in conversation - tool_results = [m for m in result.messages if m.get("role") == "tool"] - assert len(tool_results) >= 1, "Should have at least one tool result" - - # Verify the final response references the weather - final_msg = result.messages[-1] - assert final_msg["role"] == "assistant" - assert final_msg["content"], "Final response should have content" - - return result - - await _try_models(_run) - - -@pytest.mark.asyncio -async def test_multi_tool_single_turn(): - """Model should call multiple tools in a single turn.""" - - async def _run(server, model): - agent = HermesAgentLoop( - server=server, - tool_schemas=[WEATHER_TOOL, CALC_TOOL], - valid_tool_names={"get_weather", "calculate"}, - max_turns=5, - temperature=0.0, - max_tokens=500, - ) - - messages = [ - {"role": "user", "content": ( - "I need two things at once: " - "1) What's the weather in Paris? Use get_weather. " - "2) What is 15 * 7? Use calculate. " - "Call BOTH tools in a single response." - )}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - # Count distinct tools called - tools_called = set() - for msg in result.messages: - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - tools_called.add(tc["function"]["name"]) - - # At minimum, both tools should have been called (maybe in different turns) - assert "get_weather" in tools_called, f"get_weather not called. Called: {tools_called}" - assert "calculate" in tools_called, f"calculate not called. Called: {tools_called}" - - return result - - await _try_models(_run) - - -@pytest.mark.asyncio -async def test_multi_turn_conversation(): - """Agent should handle multiple turns of tool calls.""" - - async def _run(server, model): - agent = HermesAgentLoop( - server=server, - tool_schemas=[LOOKUP_TOOL, CALC_TOOL], - valid_tool_names={"lookup", "calculate"}, - max_turns=10, - temperature=0.0, - max_tokens=500, - ) - - messages = [ - {"role": "user", "content": ( - "First, use the lookup tool to look up 'meaning of life'. " - "Then use calculate to compute 6 * 7. " - "Do these in separate tool calls, one at a time." - )}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - # Should have used both tools - tools_called = set() - for msg in result.messages: - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - tools_called.add(tc["function"]["name"]) - - assert "lookup" in tools_called, f"lookup not called. Called: {tools_called}" - assert "calculate" in tools_called, f"calculate not called. Called: {tools_called}" - - # Should finish naturally - assert result.finished_naturally, "Should finish naturally after answering" - - return result - - await _try_models(_run) - - -@pytest.mark.asyncio -async def test_unknown_tool_rejected(): - """If the model calls a tool not in valid_tool_names, it gets an error.""" - - async def _run(server, model): - # Only allow "calculate" but give schema for both - agent = HermesAgentLoop( - server=server, - tool_schemas=[CALC_TOOL, WEATHER_TOOL], - valid_tool_names={"calculate"}, # weather NOT allowed - max_turns=5, - temperature=0.0, - max_tokens=500, - ) - - messages = [ - {"role": "user", "content": "What's the weather in London? Use get_weather."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - # Check if get_weather was called and rejected - if result.tool_errors: - weather_errors = [e for e in result.tool_errors if e.tool_name == "get_weather"] - assert len(weather_errors) > 0, "get_weather should have been rejected" - assert "Unknown tool" in weather_errors[0].error - - return result - - await _try_models(_run) - - -@pytest.mark.asyncio -async def test_max_turns_limit(): - """Agent should stop after max_turns even if model keeps calling tools.""" - - async def _run(server, model): - agent = HermesAgentLoop( - server=server, - tool_schemas=[LOOKUP_TOOL], - valid_tool_names={"lookup"}, - max_turns=2, # Very low limit - temperature=0.0, - max_tokens=500, - ) - - messages = [ - {"role": "user", "content": ( - "Keep looking up facts. Look up 'fact 1', then 'fact 2', " - "then 'fact 3', then 'fact 4'. Do them one at a time." - )}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - assert result.turns_used <= 2, f"Should stop at max_turns=2, used {result.turns_used}" - assert not result.finished_naturally, "Should NOT finish naturally (hit max_turns)" - - return result - - await _try_models(_run) - - -@pytest.mark.asyncio -async def test_no_tools_direct_response(): - """When no tools are useful, model should respond directly.""" - - async def _run(server, model): - agent = HermesAgentLoop( - server=server, - tool_schemas=[WEATHER_TOOL], - valid_tool_names={"get_weather"}, - max_turns=5, - temperature=0.0, - max_tokens=200, - ) - - messages = [ - {"role": "user", "content": "What is 2 + 2? Just answer directly, no tools needed."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - assert result.finished_naturally, "Should finish naturally with a direct response" - assert result.turns_used == 1, f"Should take exactly 1 turn for a direct answer, took {result.turns_used}" - - final = result.messages[-1] - assert final["role"] == "assistant" - assert final["content"], "Should have text content" - assert "4" in final["content"], "Should contain the answer '4'" - - return result - - await _try_models(_run) - - -@pytest.mark.asyncio -async def test_tool_error_handling(): - """Tool execution errors should be captured and reported to the model.""" - - async def _run(server, model): - agent = HermesAgentLoop( - server=server, - tool_schemas=[ERROR_TOOL], - valid_tool_names={"failing_tool"}, - max_turns=5, - temperature=0.0, - max_tokens=500, - ) - - messages = [ - {"role": "user", "content": "Please call the failing_tool with input 'test'."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - # The tool error should be recorded - assert len(result.tool_errors) >= 1, "Should have at least one tool error" - assert "RuntimeError" in result.tool_errors[0].error or "always fails" in result.tool_errors[0].error - - # The error should be in the conversation as a tool result - tool_results = [m for m in result.messages if m.get("role") == "tool"] - assert len(tool_results) >= 1 - error_result = json.loads(tool_results[0]["content"]) - assert "error" in error_result - - return result - - await _try_models(_run) - - -@pytest.mark.asyncio -async def test_agent_result_structure(): - """Verify the AgentResult has all expected fields populated.""" - - async def _run(server, model): - agent = HermesAgentLoop( - server=server, - tool_schemas=[CALC_TOOL], - valid_tool_names={"calculate"}, - max_turns=5, - temperature=0.0, - max_tokens=300, - ) - - messages = [ - {"role": "user", "content": "What is 3 + 4? Use the calculate tool."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - # Structural checks - assert isinstance(result, AgentResult) - assert isinstance(result.messages, list) - assert len(result.messages) >= 3, "Should have user + assistant(tool) + tool_result + assistant(final)" - assert isinstance(result.turns_used, int) - assert result.turns_used > 0 - assert isinstance(result.finished_naturally, bool) - assert isinstance(result.tool_errors, list) - assert isinstance(result.reasoning_per_turn, list) - - # Messages should follow OpenAI format - for msg in result.messages: - assert "role" in msg, f"Message missing 'role': {msg}" - assert msg["role"] in ("system", "user", "assistant", "tool"), f"Invalid role: {msg['role']}" - - return result - - await _try_models(_run) - - -@pytest.mark.asyncio -async def test_conversation_history_preserved(): - """The full conversation history should be in result.messages.""" - - async def _run(server, model): - agent = HermesAgentLoop( - server=server, - tool_schemas=[WEATHER_TOOL], - valid_tool_names={"get_weather"}, - max_turns=5, - temperature=0.0, - max_tokens=500, - ) - - messages = [ - {"role": "system", "content": "You are a helpful weather assistant."}, - {"role": "user", "content": "What's the weather in Berlin? Use get_weather."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - # System message should be preserved - assert result.messages[0]["role"] == "system" - assert "weather assistant" in result.messages[0]["content"] - - # User message should be preserved - assert result.messages[1]["role"] == "user" - assert "Berlin" in result.messages[1]["content"] - - # Should have assistant + tool + assistant sequence - roles = [m["role"] for m in result.messages] - assert "tool" in roles, "Should have tool results in conversation" - - return result - - await _try_models(_run) diff --git a/tests/run_agent/test_agent_loop_vllm.py b/tests/run_agent/test_agent_loop_vllm.py deleted file mode 100644 index d4284909414..00000000000 --- a/tests/run_agent/test_agent_loop_vllm.py +++ /dev/null @@ -1,359 +0,0 @@ -"""Integration tests for HermesAgentLoop with a local vLLM server. - -Tests the full Phase 2 flow: ManagedServer + tool calling with a real -vLLM backend, producing actual token IDs and logprobs for RL training. - -Requires a running vLLM server. Start one from the atropos directory: - - python -m example_trainer.vllm_api_server \ - --model Qwen/Qwen3-4B-Thinking-2507 \ - --port 9001 \ - --gpu-memory-utilization 0.8 \ - --max-model-len=32000 - -Tests are automatically skipped if the server is not reachable. - -Run: - pytest tests/test_agent_loop_vllm.py -v - pytest tests/test_agent_loop_vllm.py -v -k "single" -""" - -import asyncio -import json -import os -import sys -from pathlib import Path -from typing import Any, Dict -from unittest.mock import patch - -import pytest -import requests - -# Ensure repo root is importable -_repo_root = Path(__file__).resolve().parent.parent.parent -if str(_repo_root) not in sys.path: - sys.path.insert(0, str(_repo_root)) - -try: - from environments.agent_loop import AgentResult, HermesAgentLoop -except ImportError: - pytest.skip("atroposlib not installed", allow_module_level=True) - - -# ========================================================================= -# Configuration -# ========================================================================= - -VLLM_HOST = "localhost" -VLLM_PORT = 9001 -VLLM_BASE_URL = f"http://{VLLM_HOST}:{VLLM_PORT}" -VLLM_MODEL = "Qwen/Qwen3-4B-Thinking-2507" - - -def _vllm_is_running() -> bool: - """Check if the vLLM server is reachable.""" - try: - r = requests.get(f"{VLLM_BASE_URL}/health", timeout=3) - return r.status_code == 200 - except Exception: - return False - - -# Skip all tests in this module if vLLM is not running -pytestmark = pytest.mark.skipif( - not _vllm_is_running(), - reason=( - f"vLLM server not reachable at {VLLM_BASE_URL}. " - "Start it with: python -m example_trainer.vllm_api_server " - f"--model {VLLM_MODEL} --port {VLLM_PORT} " - "--gpu-memory-utilization 0.8 --max-model-len=32000" - ), -) - - -# ========================================================================= -# Server setup -# ========================================================================= - -def _make_server_manager(): - """Create a ServerManager pointing to the local vLLM server.""" - from atroposlib.envs.server_handling.server_manager import ( - ServerManager, - APIServerConfig, - ) - - config = APIServerConfig( - base_url=VLLM_BASE_URL, - model_name=VLLM_MODEL, - server_type="vllm", - health_check=False, - ) - sm = ServerManager([config], tool_parser="hermes") - sm.servers[0].server_healthy = True - return sm - - -def _get_tokenizer(): - """Load the tokenizer for the model.""" - from transformers import AutoTokenizer - return AutoTokenizer.from_pretrained(VLLM_MODEL) - - -# ========================================================================= -# Fake tools -# ========================================================================= - -WEATHER_TOOL = { - "type": "function", - "function": { - "name": "get_weather", - "description": "Get the current weather for a city. Returns temperature and conditions.", - "parameters": { - "type": "object", - "properties": { - "city": { - "type": "string", - "description": "City name, e.g. 'Tokyo'", - } - }, - "required": ["city"], - }, - }, -} - -CALC_TOOL = { - "type": "function", - "function": { - "name": "calculate", - "description": "Calculate a math expression. Returns the numeric result.", - "parameters": { - "type": "object", - "properties": { - "expression": { - "type": "string", - "description": "Math expression, e.g. '2 + 3'", - } - }, - "required": ["expression"], - }, - }, -} - - -def _fake_tool_handler(tool_name: str, args: Dict[str, Any], **kwargs) -> str: - """Handle fake tool calls for testing.""" - if tool_name == "get_weather": - city = args.get("city", "Unknown") - return json.dumps({ - "city": city, - "temperature": 22, - "conditions": "sunny", - "humidity": 45, - }) - elif tool_name == "calculate": - expr = args.get("expression", "0") - try: - result = eval(expr, {"__builtins__": {}}, {}) - return json.dumps({"result": result}) - except Exception as e: - return json.dumps({"error": str(e)}) - return json.dumps({"error": f"Unknown tool: {tool_name}"}) - - -# ========================================================================= -# Tests -# ========================================================================= - -@pytest.mark.asyncio -async def test_vllm_single_tool_call(): - """vLLM model calls a tool, gets result, responds — full Phase 2 flow.""" - sm = _make_server_manager() - tokenizer = _get_tokenizer() - - async with sm.managed_server(tokenizer=tokenizer) as managed: - agent = HermesAgentLoop( - server=managed, - tool_schemas=[WEATHER_TOOL], - valid_tool_names={"get_weather"}, - max_turns=5, - temperature=0.6, - max_tokens=1000, - ) - - messages = [ - {"role": "user", "content": "What's the weather in Tokyo? Use the get_weather tool."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - assert isinstance(result, AgentResult) - assert result.turns_used >= 2, f"Expected at least 2 turns, got {result.turns_used}" - - # Verify tool call happened - tool_calls_found = False - for msg in result.messages: - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - if tc["function"]["name"] == "get_weather": - tool_calls_found = True - args = json.loads(tc["function"]["arguments"]) - assert "city" in args - assert tool_calls_found, "Model should have called get_weather" - - # Verify tool results in conversation - tool_results = [m for m in result.messages if m.get("role") == "tool"] - assert len(tool_results) >= 1 - - -@pytest.mark.asyncio -async def test_vllm_multi_tool_calls(): - """vLLM model calls multiple tools across turns.""" - sm = _make_server_manager() - tokenizer = _get_tokenizer() - - async with sm.managed_server(tokenizer=tokenizer) as managed: - agent = HermesAgentLoop( - server=managed, - tool_schemas=[WEATHER_TOOL, CALC_TOOL], - valid_tool_names={"get_weather", "calculate"}, - max_turns=10, - temperature=0.6, - max_tokens=1000, - ) - - messages = [ - {"role": "user", "content": ( - "I need two things: " - "1) What's the weather in Paris? Use get_weather. " - "2) What is 15 * 7? Use calculate." - )}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - # Both tools should be called - tools_called = set() - for msg in result.messages: - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - tools_called.add(tc["function"]["name"]) - - assert "get_weather" in tools_called, f"get_weather not called. Called: {tools_called}" - assert "calculate" in tools_called, f"calculate not called. Called: {tools_called}" - - -@pytest.mark.asyncio -async def test_vllm_managed_server_produces_nodes(): - """ManagedServer should produce SequenceNodes with tokens and logprobs.""" - sm = _make_server_manager() - tokenizer = _get_tokenizer() - - async with sm.managed_server(tokenizer=tokenizer) as managed: - agent = HermesAgentLoop( - server=managed, - tool_schemas=[WEATHER_TOOL], - valid_tool_names={"get_weather"}, - max_turns=5, - temperature=0.6, - max_tokens=1000, - ) - - messages = [ - {"role": "user", "content": "What's the weather in Berlin? Use get_weather."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - # Get the managed state — should have SequenceNodes - state = managed.get_state() - - assert state is not None, "ManagedServer should return state" - nodes = state.get("nodes", []) - assert len(nodes) >= 1, f"Should have at least 1 node, got {len(nodes)}" - - node = nodes[0] - assert hasattr(node, "tokens"), "Node should have tokens" - assert hasattr(node, "logprobs"), "Node should have logprobs" - assert len(node.tokens) > 0, "Tokens should not be empty" - assert len(node.logprobs) > 0, "Logprobs should not be empty" - assert len(node.tokens) == len(node.logprobs), ( - f"Tokens ({len(node.tokens)}) and logprobs ({len(node.logprobs)}) should have same length" - ) - - -@pytest.mark.asyncio -async def test_vllm_no_tools_direct_response(): - """vLLM model should respond directly when no tools are needed.""" - sm = _make_server_manager() - tokenizer = _get_tokenizer() - - async with sm.managed_server(tokenizer=tokenizer) as managed: - agent = HermesAgentLoop( - server=managed, - tool_schemas=[WEATHER_TOOL], - valid_tool_names={"get_weather"}, - max_turns=5, - temperature=0.6, - max_tokens=500, - ) - - messages = [ - {"role": "user", "content": "What is 2 + 2? Answer directly, no tools."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - assert result.finished_naturally, "Should finish naturally" - assert result.turns_used == 1, f"Should take 1 turn, took {result.turns_used}" - - final = result.messages[-1] - assert final["role"] == "assistant" - assert final["content"], "Should have content" - - -@pytest.mark.asyncio -async def test_vllm_thinking_content_extracted(): - """Qwen3-Thinking model should produce reasoning content.""" - sm = _make_server_manager() - tokenizer = _get_tokenizer() - - async with sm.managed_server( - tokenizer=tokenizer, - preserve_think_blocks=True, - ) as managed: - agent = HermesAgentLoop( - server=managed, - tool_schemas=[CALC_TOOL], - valid_tool_names={"calculate"}, - max_turns=5, - temperature=0.6, - max_tokens=1000, - ) - - messages = [ - {"role": "user", "content": "What is 123 * 456? Use the calculate tool."}, - ] - - with patch("environments.agent_loop.handle_function_call", side_effect=_fake_tool_handler): - result = await agent.run(messages) - - # Qwen3-Thinking should generate blocks - # Check if any content contains thinking markers - has_thinking = False - for msg in result.messages: - content = msg.get("content", "") or "" - if "" in content or "" in content: - has_thinking = True - break - - # Also check reasoning_per_turn - has_reasoning = any(r for r in result.reasoning_per_turn if r) - - # At least one of these should be true for a thinking model - assert has_thinking or has_reasoning, ( - "Qwen3-Thinking should produce blocks or reasoning content" - ) diff --git a/tests/run_agent/test_streaming_tool_call_repair.py b/tests/run_agent/test_streaming_tool_call_repair.py index dadfaec33e5..e85c0e22d18 100644 --- a/tests/run_agent/test_streaming_tool_call_repair.py +++ b/tests/run_agent/test_streaming_tool_call_repair.py @@ -23,7 +23,7 @@ class TestStreamingAssemblyRepair: These tests verify the REPAIR FUNCTION itself works correctly for the cases that arise during streaming assembly. Integration tests that - exercise the full streaming path are in test_agent_loop_tool_calling.py. + exercise the full streaming path are in run_agent.py's streaming tests. """ # -- Truncation cases (most common streaming failure) -- diff --git a/tests/test_model_tools.py b/tests/test_model_tools.py index 379aac2bbcf..beae3daa65e 100644 --- a/tests/test_model_tools.py +++ b/tests/test_model_tools.py @@ -278,7 +278,7 @@ class TestLegacyToolsetMap: expected = [ "web_tools", "terminal_tools", "vision_tools", "moa_tools", "image_tools", "skills_tools", "browser_tools", "cronjob_tools", - "rl_tools", "file_tools", "tts_tools", + "file_tools", "tts_tools", ] for name in expected: assert name in _LEGACY_TOOLSET_MAP, f"Missing legacy toolset: {name}" diff --git a/tests/tools/test_managed_server_tool_support.py b/tests/tools/test_managed_server_tool_support.py deleted file mode 100644 index 5b917f3da89..00000000000 --- a/tests/tools/test_managed_server_tool_support.py +++ /dev/null @@ -1,178 +0,0 @@ -""" -Tests for ManagedServer / tool-parser integration. - -Validates that: -1. The installed atroposlib API still matches Hermes's expectations -2. Hermes's parser registry remains compatible with ManagedServer parsing -3. HermesAgentBaseEnv wires the selected parser into ServerManager correctly - -These tests verify the contract between hermes-agent's environments/ code -and atroposlib's ManagedServer. They detect API incompatibilities early. -""" - -import inspect -import sys -from pathlib import Path - -import pytest - -sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) - -try: - import atroposlib # noqa: F401 -except ImportError: - pytest.skip("atroposlib not installed", allow_module_level=True) - - -class TestManagedServerAPI: - """Test that ManagedServer's API matches what hermes-agent expects.""" - - def test_managed_server_init_signature(self): - """ManagedServer should accept tool_call_parser parameter.""" - from atroposlib.envs.server_handling.managed_server import ManagedServer - - sig = inspect.signature(ManagedServer.__init__) - params = list(sig.parameters.keys()) - - # Core params that must exist - assert "self" in params - assert "server" in params - assert "tokenizer" in params - assert "track_tree" in params - - # tool_call_parser — required for tool_call_support branch - # If this fails, atroposlib hasn't been updated to tool_call_support - has_tool_parser = "tool_call_parser" in params - if not has_tool_parser: - pytest.skip( - "ManagedServer does not have tool_call_parser param — " - "baseline atroposlib (pre tool_call_support branch)" - ) - - def test_server_manager_managed_server_signature(self): - """ServerManager.managed_server() should accept tool_call_parser.""" - from atroposlib.envs.server_handling.server_manager import ServerManager - - sig = inspect.signature(ServerManager.managed_server) - params = list(sig.parameters.keys()) - - assert "self" in params - assert "tokenizer" in params - - has_tool_parser = "tool_call_parser" in params - if not has_tool_parser: - pytest.skip( - "ServerManager.managed_server() does not have tool_call_parser param — " - "baseline atroposlib (pre tool_call_support branch)" - ) - - def test_managed_server_chat_template_kwargs(self): - """ManagedServer should have CHAT_TEMPLATE_KWARGS for forwarding tools/thinking.""" - from atroposlib.envs.server_handling.managed_server import ManagedServer - - if not hasattr(ManagedServer, "CHAT_TEMPLATE_KWARGS"): - pytest.skip( - "ManagedServer does not have CHAT_TEMPLATE_KWARGS — " - "baseline atroposlib (pre tool_call_support branch)" - ) - - kwargs = ManagedServer.CHAT_TEMPLATE_KWARGS - assert "tools" in kwargs, "tools must be in CHAT_TEMPLATE_KWARGS" - - def test_no_get_logprobs_method(self): - """get_logprobs should be removed in tool_call_support branch.""" - from atroposlib.envs.server_handling.managed_server import ManagedServer - - # In baseline, get_logprobs exists. In tool_call_support, it's removed. - # We just note the state — not a hard fail either way. - has_get_logprobs = hasattr(ManagedServer, "get_logprobs") - if has_get_logprobs: - pytest.skip( - "ManagedServer still has get_logprobs — baseline atroposlib" - ) - - -class TestParserCompatibility: - """Test that hermes-agent's parsers match ManagedServer's expectations.""" - - def test_parser_parse_returns_correct_format(self): - """ - ManagedServer expects parser.parse(text) -> (content, tool_calls) - where tool_calls is a list of objects with .id, .function.name, .function.arguments - """ - from environments.tool_call_parsers import get_parser - - parser = get_parser("hermes") - text = '{"name": "terminal", "arguments": {"command": "ls"}}' - content, tool_calls = parser.parse(text) - - assert tool_calls is not None - assert len(tool_calls) == 1 - - tc = tool_calls[0] - # ManagedServer accesses these attrs directly - assert hasattr(tc, "id") - assert hasattr(tc, "function") - assert hasattr(tc.function, "name") - assert hasattr(tc.function, "arguments") - - def test_parser_no_tools_returns_none(self): - """ManagedServer checks `if parsed_tool_calls:` — None should be falsy.""" - from environments.tool_call_parsers import get_parser - - parser = get_parser("hermes") - content, tool_calls = parser.parse("Just text, no tools") - assert tool_calls is None - - def test_parser_content_is_string_or_none(self): - """ManagedServer uses `parsed_content or ""` — must be str or None.""" - from environments.tool_call_parsers import get_parser - - parser = get_parser("hermes") - - # With tool calls - text = '{"name": "terminal", "arguments": {"command": "ls"}}' - content, _ = parser.parse(text) - assert content is None or isinstance(content, str) - - # Without tool calls - content2, _ = parser.parse("Just text") - assert isinstance(content2, str) - - -class TestBaseEnvCompatibility: - """Test that hermes_base_env.py's tool-parser wiring matches the current API.""" - - def test_hermes_base_env_sets_server_manager_tool_parser(self): - """Hermes wires parser selection through ServerManager.tool_parser.""" - import ast - - base_env_path = Path(__file__).parent.parent.parent / "environments" / "hermes_base_env.py" - source = base_env_path.read_text() - tree = ast.parse(source) - - found_assignment = False - for node in ast.walk(tree): - if isinstance(node, ast.Assign): - for target in node.targets: - if isinstance(target, ast.Attribute) and target.attr == "tool_parser": - parent = target.value - if ( - isinstance(parent, ast.Attribute) - and parent.attr == "server" - and isinstance(parent.value, ast.Name) - and parent.value.id == "self" - ): - found_assignment = True - - assert found_assignment, ( - "hermes_base_env.py should set self.server.tool_parser from config.tool_call_parser" - ) - - def test_hermes_base_env_uses_config_tool_call_parser(self): - """Verify hermes_base_env uses the config field rather than a local parser instance.""" - base_env_path = Path(__file__).parent.parent.parent / "environments" / "hermes_base_env.py" - source = base_env_path.read_text() - - assert 'tool_call_parser: str = Field(' in source - assert 'self.server.tool_parser = config.tool_call_parser' in source diff --git a/tests/tools/test_rl_training_tool.py b/tests/tools/test_rl_training_tool.py deleted file mode 100644 index 8b68ea8d946..00000000000 --- a/tests/tools/test_rl_training_tool.py +++ /dev/null @@ -1,142 +0,0 @@ -"""Tests for rl_training_tool.py — file handle lifecycle and cleanup. - -Verifies that _stop_training_run properly closes log file handles, -terminates processes, and handles edge cases on failure paths. -Inspired by PR #715 (0xbyt4). -""" - -from unittest.mock import MagicMock - -import pytest - -from tools.rl_training_tool import RunState, _stop_training_run - - -def _make_run_state(**overrides) -> RunState: - """Create a minimal RunState for testing.""" - defaults = { - "run_id": "test-run-001", - "environment": "test_env", - "config": {}, - } - defaults.update(overrides) - return RunState(**defaults) - - -class TestStopTrainingRunFileHandles: - """Verify that _stop_training_run closes log file handles stored as attributes.""" - - def test_closes_all_log_file_handles(self): - state = _make_run_state() - files = {} - for attr in ("api_log_file", "trainer_log_file", "env_log_file"): - fh = MagicMock() - setattr(state, attr, fh) - files[attr] = fh - - _stop_training_run(state) - - for attr, fh in files.items(): - fh.close.assert_called_once() - assert getattr(state, attr) is None - - def test_clears_file_attrs_to_none(self): - state = _make_run_state() - state.api_log_file = MagicMock() - - _stop_training_run(state) - - assert state.api_log_file is None - - def test_close_exception_does_not_propagate(self): - """If a file handle .close() raises, it must not crash.""" - state = _make_run_state() - bad_fh = MagicMock() - bad_fh.close.side_effect = OSError("already closed") - good_fh = MagicMock() - state.api_log_file = bad_fh - state.trainer_log_file = good_fh - - _stop_training_run(state) # should not raise - - bad_fh.close.assert_called_once() - good_fh.close.assert_called_once() - - def test_handles_missing_file_attrs(self): - """RunState without log file attrs should not crash.""" - state = _make_run_state() - # No log file attrs set at all — getattr(..., None) should handle it - _stop_training_run(state) # should not raise - - -class TestStopTrainingRunProcesses: - """Verify that _stop_training_run terminates processes correctly.""" - - def test_terminates_running_processes(self): - state = _make_run_state() - for attr in ("api_process", "trainer_process", "env_process"): - proc = MagicMock() - proc.poll.return_value = None # still running - setattr(state, attr, proc) - - _stop_training_run(state) - - for attr in ("api_process", "trainer_process", "env_process"): - getattr(state, attr).terminate.assert_called_once() - - def test_does_not_terminate_exited_processes(self): - state = _make_run_state() - proc = MagicMock() - proc.poll.return_value = 0 # already exited - state.api_process = proc - - _stop_training_run(state) - - proc.terminate.assert_not_called() - - def test_handles_none_processes(self): - state = _make_run_state() - # All process attrs are None by default - _stop_training_run(state) # should not raise - - def test_handles_mixed_running_and_exited_processes(self): - state = _make_run_state() - # api still running - api = MagicMock() - api.poll.return_value = None - state.api_process = api - # trainer already exited - trainer = MagicMock() - trainer.poll.return_value = 0 - state.trainer_process = trainer - # env is None - state.env_process = None - - _stop_training_run(state) - - api.terminate.assert_called_once() - trainer.terminate.assert_not_called() - - -class TestStopTrainingRunStatus: - """Verify status transitions in _stop_training_run.""" - - def test_sets_status_to_stopped_when_running(self): - state = _make_run_state(status="running") - _stop_training_run(state) - assert state.status == "stopped" - - def test_does_not_change_status_when_failed(self): - state = _make_run_state(status="failed") - _stop_training_run(state) - assert state.status == "failed" - - def test_does_not_change_status_when_pending(self): - state = _make_run_state(status="pending") - _stop_training_run(state) - assert state.status == "pending" - - def test_no_crash_with_no_processes_and_no_files(self): - state = _make_run_state() - _stop_training_run(state) # should not raise - assert state.status == "pending" diff --git a/tests/tools/test_tool_call_parsers.py b/tests/tools/test_tool_call_parsers.py deleted file mode 100644 index bdea75698a8..00000000000 --- a/tests/tools/test_tool_call_parsers.py +++ /dev/null @@ -1,274 +0,0 @@ -""" -Tests for environments/tool_call_parsers/ — client-side tool call parsers. - -These parsers extract structured tool_calls from raw model output text. -Used in Phase 2 (VLLM/generate) where the server returns raw tokens. -""" - -import json -import sys -from pathlib import Path - -import pytest - -# Ensure repo root is importable -sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) - -try: - from environments.tool_call_parsers import ( - ParseResult, - ToolCallParser, - get_parser, - list_parsers, - ) -except ImportError: - pytest.skip("atroposlib not installed", allow_module_level=True) - - -# ─── Registry tests ───────────────────────────────────────────────────── - -class TestParserRegistry: - def test_list_parsers_returns_nonempty(self): - parsers = list_parsers() - assert len(parsers) > 0 - - def test_hermes_parser_registered(self): - parsers = list_parsers() - assert "hermes" in parsers - - def test_get_parser_returns_instance(self): - parser = get_parser("hermes") - assert isinstance(parser, ToolCallParser) - - def test_get_parser_unknown_raises(self): - with pytest.raises(KeyError): - get_parser("nonexistent_parser_xyz") - - def test_all_registered_parsers_instantiate(self): - """Every registered parser should be importable and instantiable.""" - for name in list_parsers(): - parser = get_parser(name) - assert isinstance(parser, ToolCallParser) - assert hasattr(parser, "parse") - - -# ─── Hermes parser tests ──────────────────────────────────────────────── - -class TestHermesParser: - @pytest.fixture - def parser(self): - return get_parser("hermes") - - def test_no_tool_call(self, parser): - text = "Hello, I can help you with that." - content, tool_calls = parser.parse(text) - assert content == text - assert tool_calls is None - - def test_single_tool_call(self, parser): - text = '{"name": "terminal", "arguments": {"command": "ls -la"}}' - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 1 - assert tool_calls[0].function.name == "terminal" - args = json.loads(tool_calls[0].function.arguments) - assert args["command"] == "ls -la" - - def test_tool_call_with_surrounding_text(self, parser): - text = 'Let me check that for you.\n{"name": "terminal", "arguments": {"command": "pwd"}}' - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 1 - assert tool_calls[0].function.name == "terminal" - # Content should have the surrounding text - if content is not None: - assert "check that" in content or content.strip() != "" - - def test_multiple_tool_calls(self, parser): - text = ( - '{"name": "terminal", "arguments": {"command": "ls"}}\n' - '{"name": "read_file", "arguments": {"path": "test.py"}}' - ) - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 2 - names = {tc.function.name for tc in tool_calls} - assert "terminal" in names - assert "read_file" in names - - def test_tool_call_ids_are_unique(self, parser): - text = ( - '{"name": "terminal", "arguments": {"command": "ls"}}\n' - '{"name": "terminal", "arguments": {"command": "pwd"}}' - ) - _, tool_calls = parser.parse(text) - assert tool_calls is not None - ids = [tc.id for tc in tool_calls] - assert len(ids) == len(set(ids)), "Tool call IDs must be unique" - - def test_empty_string(self, parser): - content, tool_calls = parser.parse("") - assert tool_calls is None - - def test_malformed_json_in_tool_call(self, parser): - text = 'not valid json' - content, tool_calls = parser.parse(text) - # Should either return None tool_calls or handle gracefully - # (implementation may vary — some parsers return error tool calls) - - def test_truncated_tool_call(self, parser): - """Test handling of unclosed tool_call tag (model truncated mid-generation).""" - text = '{"name": "terminal", "arguments": {"command": "ls -la"}' - content, tool_calls = parser.parse(text) - # Parser should handle truncated output gracefully - # Either parse it successfully or return None - - -# ─── Parse result contract tests (applies to ALL parsers) ─────────────── - -class TestParseResultContract: - """Ensure all parsers conform to the ParseResult contract.""" - - @pytest.fixture(params=["hermes"]) # Add more as needed - def parser(self, request): - return get_parser(request.param) - - def test_returns_tuple_of_two(self, parser): - result = parser.parse("hello world") - assert isinstance(result, tuple) - assert len(result) == 2 - - def test_no_tools_returns_none_tool_calls(self, parser): - content, tool_calls = parser.parse("Just plain text, no tools.") - assert tool_calls is None - assert content is not None - - def test_tool_calls_are_proper_objects(self, parser): - """When tool calls are found, they should be ChatCompletionMessageToolCall objects.""" - # Use hermes format since that's universal - text = '{"name": "terminal", "arguments": {"command": "echo hi"}}' - content, tool_calls = parser.parse(text) - if tool_calls is not None: - for tc in tool_calls: - assert hasattr(tc, "id") - assert hasattr(tc, "function") - assert hasattr(tc.function, "name") - assert hasattr(tc.function, "arguments") - assert tc.id is not None - assert isinstance(tc.function.name, str) - assert isinstance(tc.function.arguments, str) - - -# ─── DeepSeek V3 parser tests ─────────────────────────────────────────── - -class TestDeepSeekV3Parser: - @pytest.fixture - def parser(self): - return get_parser("deepseek_v3") - - def test_no_tool_call(self, parser): - text = "Hello, how can I help you?" - content, tool_calls = parser.parse(text) - assert content == text - assert tool_calls is None - - def test_single_tool_call(self, parser): - text = ( - '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather\n' - '```json\n{"city": "London"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>' - ) - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 1 - assert tool_calls[0].function.name == "get_weather" - args = json.loads(tool_calls[0].function.arguments) - assert args["city"] == "London" - - def test_multiple_tool_calls(self, parser): - text = ( - '<｜tool▁calls▁begin｜>' - '<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather\n' - '```json\n{"city": "London"}\n```<｜tool▁call▁end｜>' - '<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_time\n' - '```json\n{"timezone": "UTC"}\n```<｜tool▁call▁end｜>' - '<｜tool▁calls▁end｜>' - ) - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 2, f"Expected 2 tool calls, got {len(tool_calls)}" - names = [tc.function.name for tc in tool_calls] - assert "get_weather" in names - assert "get_time" in names - - def test_tool_call_with_preceding_text(self, parser): - text = ( - 'Let me check that for you.\n' - '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>terminal\n' - '```json\n{"command": "ls"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>' - ) - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 1 - - -# ─── Mistral parser tests ─────────────────────────────────────────────── - -class TestMistralParser: - @pytest.fixture - def parser(self): - return get_parser("mistral") - - def test_no_tool_call(self, parser): - text = "Hello, how can I help you?" - content, tool_calls = parser.parse(text) - assert content == text - assert tool_calls is None - - def test_pre_v11_single_tool_call(self, parser): - text = '[TOOL_CALLS] [{"name": "func", "arguments": {"key": "val"}}]' - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 1 - assert tool_calls[0].function.name == "func" - args = json.loads(tool_calls[0].function.arguments) - assert args["key"] == "val" - - def test_pre_v11_nested_json(self, parser): - text = '[TOOL_CALLS] [{"name": "func", "arguments": {"nested": {"deep": true}}}]' - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 1 - assert tool_calls[0].function.name == "func" - args = json.loads(tool_calls[0].function.arguments) - assert args["nested"]["deep"] is True - - def test_v11_single_tool_call(self, parser): - text = '[TOOL_CALLS]get_weather{"city": "London"}' - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 1 - assert tool_calls[0].function.name == "get_weather" - args = json.loads(tool_calls[0].function.arguments) - assert args["city"] == "London" - - def test_v11_multiple_tool_calls(self, parser): - text = '[TOOL_CALLS]func1{"a": 1}[TOOL_CALLS]func2{"b": 2}' - content, tool_calls = parser.parse(text) - assert tool_calls is not None - assert len(tool_calls) == 2 - names = [tc.function.name for tc in tool_calls] - assert "func1" in names - assert "func2" in names - - def test_preceding_text_preserved(self, parser): - text = 'Hello[TOOL_CALLS]func{"a": 1}' - content, tool_calls = parser.parse(text) - assert content == "Hello" - assert tool_calls is not None - assert len(tool_calls) == 1 - assert tool_calls[0].function.name == "func" - - def test_malformed_json_fallback(self, parser): - text = "[TOOL_CALLS] not valid json" - content, tool_calls = parser.parse(text) - assert tool_calls is None diff --git a/tinker-atropos b/tinker-atropos deleted file mode 160000 index 65f084ee805..00000000000 --- a/tinker-atropos +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 65f084ee8054a5d02aeac76e24ed60388511c82b diff --git a/tools/budget_config.py b/tools/budget_config.py index 577e59442ee..093188d5c75 100644 --- a/tools/budget_config.py +++ b/tools/budget_config.py @@ -1,6 +1,5 @@ """Configurable budget constants for tool result persistence. -Overridable at the RL environment level via HermesAgentEnvConfig fields. Per-tool resolution: pinned > config overrides > registry > default. """ diff --git a/tools/rl_training_tool.py b/tools/rl_training_tool.py deleted file mode 100644 index c7acb8012e1..00000000000 --- a/tools/rl_training_tool.py +++ /dev/null @@ -1,1396 +0,0 @@ -#!/usr/bin/env python3 -""" -RL Training Tools Module - -This module provides tools for running RL training through Tinker-Atropos. -Directly manages training processes without requiring a separate API server. - -Features: -- Environment discovery (AST-based scanning for BaseEnv subclasses) -- Configuration management with locked infrastructure settings -- Training run lifecycle via subprocess management -- WandB metrics monitoring - -Required environment variables: -- TINKER_API_KEY: API key for Tinker service -- WANDB_API_KEY: API key for Weights & Biases metrics - -Usage: - from tools.rl_training_tool import ( - rl_list_environments, - rl_select_environment, - rl_get_current_config, - rl_edit_config, - rl_start_training, - rl_check_status, - rl_stop_training, - rl_get_results, - ) -""" - -import ast -import asyncio -import importlib.util -import json -import os -import subprocess -import sys -import time -import uuid -import logging -from datetime import datetime -import yaml -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional - -from hermes_constants import get_hermes_home - -logger = logging.getLogger(__name__) - -# ============================================================================ -# Path Configuration -# ============================================================================ - -# Path to tinker-atropos submodule (relative to hermes-agent root) -HERMES_ROOT = Path(__file__).parent.parent -TINKER_ATROPOS_ROOT = HERMES_ROOT / "tinker-atropos" -ENVIRONMENTS_DIR = TINKER_ATROPOS_ROOT / "tinker_atropos" / "environments" -CONFIGS_DIR = TINKER_ATROPOS_ROOT / "configs" -LOGS_DIR = get_hermes_home() / "logs" / "rl_training" - -def _ensure_logs_dir(): - """Lazily create logs directory on first use (avoid side effects at import time).""" - if TINKER_ATROPOS_ROOT.exists(): - LOGS_DIR.mkdir(exist_ok=True) - -# ============================================================================ -# Locked Configuration (Infrastructure Settings) -# ============================================================================ - -# These fields cannot be changed by the model - they're tuned for our infrastructure -LOCKED_FIELDS = { - "env": { - "tokenizer_name": "Qwen/Qwen3-8B", - "rollout_server_url": "http://localhost:8000", - "use_wandb": True, - "max_token_length": 8192, - "max_num_workers": 2048, - "worker_timeout": 3600, - "total_steps": 2500, - "steps_per_eval": 25, - "max_batches_offpolicy": 3, - "inference_weight": 1.0, - "eval_limit_ratio": 0.1, - }, - "openai": [ - { - "model_name": "Qwen/Qwen3-8B", - "base_url": "http://localhost:8001/v1", - "api_key": "x", - "weight": 1.0, - "num_requests_for_eval": 256, - "timeout": 3600, - "server_type": "sglang", # Tinker uses sglang for actual training - } - ], - "tinker": { - "lora_rank": 32, - "learning_rate": 0.00004, - "max_token_trainer_length": 9000, - "checkpoint_dir": "./temp/", - "save_checkpoint_interval": 25, - }, - "slurm": False, - "testing": False, -} - -LOCKED_FIELD_NAMES = set(LOCKED_FIELDS.get("env", {}).keys()) - - -# ============================================================================ -# State Management -# ============================================================================ - -@dataclass -class EnvironmentInfo: - """Information about a discovered environment.""" - name: str - class_name: str - file_path: str - description: str = "" - config_class: str = "BaseEnvConfig" - - -@dataclass -class RunState: - """State for a training run.""" - run_id: str - environment: str - config: Dict[str, Any] - status: str = "pending" # pending, starting, running, stopping, stopped, completed, failed - error_message: str = "" - wandb_project: str = "" - wandb_run_name: str = "" - start_time: float = 0.0 - # Process handles - api_process: Optional[subprocess.Popen] = None - trainer_process: Optional[subprocess.Popen] = None - env_process: Optional[subprocess.Popen] = None - - -# Global state -_environments: List[EnvironmentInfo] = [] -_current_env: Optional[str] = None -_current_config: Dict[str, Any] = {} -_env_config_cache: Dict[str, Dict[str, Dict[str, Any]]] = {} -_active_runs: Dict[str, RunState] = {} -_last_status_check: Dict[str, float] = {} - -# Rate limiting for status checks (30 minutes) -MIN_STATUS_CHECK_INTERVAL = 30 * 60 - - -# ============================================================================ -# Environment Discovery -# ============================================================================ - -def _scan_environments() -> List[EnvironmentInfo]: - """ - Scan the environments directory for BaseEnv subclasses using AST. - """ - environments = [] - - if not ENVIRONMENTS_DIR.exists(): - return environments - - for py_file in ENVIRONMENTS_DIR.glob("*.py"): - if py_file.name.startswith("_"): - continue - - try: - with open(py_file, "r", encoding="utf-8") as f: - tree = ast.parse(f.read()) - - for node in ast.walk(tree): - if isinstance(node, ast.ClassDef): - # Check if class has BaseEnv as base - for base in node.bases: - base_name = "" - if isinstance(base, ast.Name): - base_name = base.id - elif isinstance(base, ast.Attribute): - base_name = base.attr - - if base_name == "BaseEnv": - # Extract name from class attribute if present - env_name = py_file.stem - description = "" - config_class = "BaseEnvConfig" - - for item in node.body: - if isinstance(item, ast.Assign): - for target in item.targets: - if isinstance(target, ast.Name): - if target.id == "name" and isinstance(item.value, ast.Constant): - env_name = item.value.value - elif target.id == "env_config_cls" and isinstance(item.value, ast.Name): - config_class = item.value.id - - # Get docstring - if isinstance(item, ast.Expr) and isinstance(item.value, ast.Constant): - if isinstance(item.value.value, str) and not description: - description = item.value.value.split("\n")[0].strip() - - environments.append(EnvironmentInfo( - name=env_name, - class_name=node.name, - file_path=str(py_file), - description=description or f"Environment from {py_file.name}", - config_class=config_class, - )) - break - except Exception as e: - logger.warning("Could not parse %s: %s", py_file, e) - - return environments - - -def _get_env_config_fields(env_file_path: str) -> Dict[str, Dict[str, Any]]: - """ - Dynamically import an environment and extract its config fields. - - Uses config_init() to get the actual config class, with fallback to - directly importing BaseEnvConfig if config_init fails. - """ - try: - # Load the environment module - spec = importlib.util.spec_from_file_location("env_module", env_file_path) - module = importlib.util.module_from_spec(spec) - sys.modules["env_module"] = module - spec.loader.exec_module(module) - - # Find the BaseEnv subclass - env_class = None - for name, obj in vars(module).items(): - if isinstance(obj, type) and name != "BaseEnv": - if hasattr(obj, "config_init") and callable(getattr(obj, "config_init")): - env_class = obj - break - - if not env_class: - return {} - - # Try calling config_init to get the actual config class - config_class = None - try: - env_config, server_configs = env_class.config_init() - config_class = type(env_config) - except Exception as config_error: - # Fallback: try to import BaseEnvConfig directly from atroposlib - logger.info("config_init failed (%s), using BaseEnvConfig defaults", config_error) - try: - from atroposlib.envs.base import BaseEnvConfig - config_class = BaseEnvConfig - except ImportError: - return {} - - if not config_class: - return {} - - # Helper to make values JSON-serializable (handle enums, etc.) - def make_serializable(val): - if val is None: - return None - if hasattr(val, 'value'): # Enum - return val.value - if hasattr(val, 'name') and hasattr(val, '__class__') and 'Enum' in str(type(val)): - return val.name - return val - - # Extract fields from the Pydantic model - fields = {} - for field_name, field_info in config_class.model_fields.items(): - field_type = field_info.annotation - default = make_serializable(field_info.default) - description = field_info.description or "" - - is_locked = field_name in LOCKED_FIELD_NAMES - - # Convert type to string - type_name = getattr(field_type, "__name__", str(field_type)) - if hasattr(field_type, "__origin__"): - type_name = str(field_type) - - locked_value = LOCKED_FIELDS.get("env", {}).get(field_name, default) - current_value = make_serializable(locked_value) if is_locked else default - - fields[field_name] = { - "type": type_name, - "default": default, - "description": description, - "locked": is_locked, - "current_value": current_value, - } - - return fields - - except Exception as e: - logger.warning("Could not introspect environment config: %s", e) - return {} - - -def _initialize_environments(): - """Initialize environment list on first use.""" - global _environments - if not _environments: - _environments = _scan_environments() - - -# ============================================================================ -# Subprocess Management -# ============================================================================ - -async def _spawn_training_run(run_state: RunState, config_path: Path): - """ - Spawn the three processes needed for training: - 1. run-api (Atropos API server) - 2. launch_training.py (Tinker trainer + inference server) - 3. environment.py serve (the Atropos environment) - """ - run_id = run_state.run_id - - _ensure_logs_dir() - - # Log file paths - api_log = LOGS_DIR / f"api_{run_id}.log" - trainer_log = LOGS_DIR / f"trainer_{run_id}.log" - env_log = LOGS_DIR / f"env_{run_id}.log" - - try: - # Step 1: Start the Atropos API server (run-api) - logger.info("[%s] Starting Atropos API server (run-api)...", run_id) - - # File must stay open while the subprocess runs; we store the handle - # on run_state so _stop_training_run() can close it when done. - api_log_file = open(api_log, "w", encoding="utf-8") # closed by _stop_training_run - run_state.api_log_file = api_log_file - run_state.api_process = subprocess.Popen( - ["run-api"], - stdout=api_log_file, - stderr=subprocess.STDOUT, - cwd=str(TINKER_ATROPOS_ROOT), - ) - - # Wait for API to start - await asyncio.sleep(5) - - if run_state.api_process.poll() is not None: - run_state.status = "failed" - run_state.error_message = f"API server exited with code {run_state.api_process.returncode}. Check {api_log}" - _stop_training_run(run_state) - return - - logger.info("[%s] Atropos API server started", run_id) - - # Step 2: Start the Tinker trainer - logger.info("[%s] Starting Tinker trainer: launch_training.py --config %s", run_id, config_path) - - trainer_log_file = open(trainer_log, "w", encoding="utf-8") # closed by _stop_training_run - run_state.trainer_log_file = trainer_log_file - run_state.trainer_process = subprocess.Popen( - [sys.executable, "launch_training.py", "--config", str(config_path)], - stdout=trainer_log_file, - stderr=subprocess.STDOUT, - cwd=str(TINKER_ATROPOS_ROOT), - env={**os.environ, "TINKER_API_KEY": os.getenv("TINKER_API_KEY", "")}, - ) - - # Wait for trainer to initialize (it starts FastAPI inference server on 8001) - logger.info("[%s] Waiting 30 seconds for trainer to initialize...", run_id) - await asyncio.sleep(30) - - if run_state.trainer_process.poll() is not None: - run_state.status = "failed" - run_state.error_message = f"Trainer exited with code {run_state.trainer_process.returncode}. Check {trainer_log}" - _stop_training_run(run_state) - return - - logger.info("[%s] Trainer started, inference server on port 8001", run_id) - - # Step 3: Start the environment - logger.info("[%s] Waiting 90 more seconds before starting environment...", run_id) - await asyncio.sleep(90) - - # Find the environment file - env_info = None - for env in _environments: - if env.name == run_state.environment: - env_info = env - break - - if not env_info: - run_state.status = "failed" - run_state.error_message = f"Environment '{run_state.environment}' not found" - _stop_training_run(run_state) - return - - logger.info("[%s] Starting environment: %s serve", run_id, env_info.file_path) - - env_log_file = open(env_log, "w", encoding="utf-8") # closed by _stop_training_run - run_state.env_log_file = env_log_file - run_state.env_process = subprocess.Popen( - [sys.executable, str(env_info.file_path), "serve", "--config", str(config_path)], - stdout=env_log_file, - stderr=subprocess.STDOUT, - cwd=str(TINKER_ATROPOS_ROOT), - ) - - # Wait for environment to connect - await asyncio.sleep(10) - - if run_state.env_process.poll() is not None: - run_state.status = "failed" - run_state.error_message = f"Environment exited with code {run_state.env_process.returncode}. Check {env_log}" - _stop_training_run(run_state) - return - - run_state.status = "running" - run_state.start_time = time.time() - logger.info("[%s] Training run started successfully!", run_id) - - # Start background monitoring - asyncio.create_task(_monitor_training_run(run_state)) - - except Exception as e: - run_state.status = "failed" - run_state.error_message = str(e) - _stop_training_run(run_state) - - -async def _monitor_training_run(run_state: RunState): - """Background task to monitor a training run.""" - while run_state.status == "running": - await asyncio.sleep(30) # Check every 30 seconds - - # Check if any process has died - if run_state.env_process and run_state.env_process.poll() is not None: - exit_code = run_state.env_process.returncode - if exit_code == 0: - run_state.status = "completed" - else: - run_state.status = "failed" - run_state.error_message = f"Environment process exited with code {exit_code}" - _stop_training_run(run_state) - break - - if run_state.trainer_process and run_state.trainer_process.poll() is not None: - exit_code = run_state.trainer_process.returncode - if exit_code == 0: - run_state.status = "completed" - else: - run_state.status = "failed" - run_state.error_message = f"Trainer process exited with code {exit_code}" - _stop_training_run(run_state) - break - - if run_state.api_process and run_state.api_process.poll() is not None: - run_state.status = "failed" - run_state.error_message = "API server exited unexpectedly" - _stop_training_run(run_state) - break - - -def _stop_training_run(run_state: RunState): - """Stop all processes for a training run.""" - # Stop in reverse order: env -> trainer -> api - if run_state.env_process and run_state.env_process.poll() is None: - logger.info("[%s] Stopping environment process...", run_state.run_id) - run_state.env_process.terminate() - try: - run_state.env_process.wait(timeout=10) - except subprocess.TimeoutExpired: - run_state.env_process.kill() - - if run_state.trainer_process and run_state.trainer_process.poll() is None: - logger.info("[%s] Stopping trainer process...", run_state.run_id) - run_state.trainer_process.terminate() - try: - run_state.trainer_process.wait(timeout=10) - except subprocess.TimeoutExpired: - run_state.trainer_process.kill() - - if run_state.api_process and run_state.api_process.poll() is None: - logger.info("[%s] Stopping API server...", run_state.run_id) - run_state.api_process.terminate() - try: - run_state.api_process.wait(timeout=10) - except subprocess.TimeoutExpired: - run_state.api_process.kill() - - if run_state.status == "running": - run_state.status = "stopped" - - # Close log file handles that were opened for subprocess stdout. - for attr in ("env_log_file", "trainer_log_file", "api_log_file"): - fh = getattr(run_state, attr, None) - if fh is not None: - try: - fh.close() - except Exception: - pass - setattr(run_state, attr, None) - - -# ============================================================================ -# Environment Discovery Tools -# ============================================================================ - -async def rl_list_environments() -> str: - """ - List all available RL environments. - - Scans tinker-atropos/tinker_atropos/environments/ for Python files - containing classes that inherit from BaseEnv. - - Returns information about each environment including: - - name: Environment identifier - - class_name: Python class name - - file_path: Path to the environment file - - description: Brief description if available - - TIP: To create or modify RL environments: - 1. Use terminal/file tools to inspect existing environments - 2. Study how they load datasets, define verifiers, and structure rewards - 3. Inspect HuggingFace datasets to understand data formats - 4. Copy an existing environment as a template - - Returns: - JSON string with list of environments - """ - _initialize_environments() - - response = { - "environments": [ - { - "name": env.name, - "class_name": env.class_name, - "file_path": env.file_path, - "description": env.description, - } - for env in _environments - ], - "count": len(_environments), - "tips": [ - "Use rl_select_environment(name) to select an environment", - "Read the file_path with file tools to understand how each environment works", - "Look for load_dataset(), score_answer(), get_next_item() methods", - ] - } - - return json.dumps(response, indent=2) - - -async def rl_select_environment(name: str) -> str: - """ - Select an RL environment for training. - - This loads the environment's configuration fields into memory. - After selecting, use rl_get_current_config() to see all configurable options - and rl_edit_config() to modify specific fields. - - Args: - name: Name of the environment to select (from rl_list_environments) - - Returns: - JSON string with selection result, file path, and configurable field count - - TIP: Read the returned file_path to understand how the environment works. - """ - global _current_env, _current_config - - _initialize_environments() - - env_info = None - for env in _environments: - if env.name == name: - env_info = env - break - - if not env_info: - return json.dumps({ - "error": f"Environment '{name}' not found", - "available": [e.name for e in _environments], - }, indent=2) - - _current_env = name - - # Dynamically discover config fields - config_fields = _get_env_config_fields(env_info.file_path) - _env_config_cache[name] = config_fields - - # Initialize current config with defaults for non-locked fields - _current_config = {} - for field_name, field_info in config_fields.items(): - if not field_info.get("locked", False): - _current_config[field_name] = field_info.get("default") - - # Auto-set wandb_name to "{env_name}-DATETIME" to avoid overlaps - timestamp = datetime.now().strftime("%Y%m%d-%H%M%S") - _current_config["wandb_name"] = f"{name}-{timestamp}" - - return json.dumps({ - "message": f"Selected environment: {name}", - "environment": name, - "file_path": env_info.file_path, - }, indent=2) - - -# ============================================================================ -# Configuration Tools -# ============================================================================ - -async def rl_get_current_config() -> str: - """ - Get the current environment configuration. - - Returns all configurable fields for the selected environment. - Each environment may have different configuration options. - - Fields are divided into: - - configurable_fields: Can be changed with rl_edit_config() - - locked_fields: Infrastructure settings that cannot be changed - - Returns: - JSON string with configurable and locked fields - """ - if not _current_env: - return json.dumps({ - "error": "No environment selected. Use rl_select_environment(name) first.", - }, indent=2) - - config_fields = _env_config_cache.get(_current_env, {}) - - configurable = [] - locked = [] - - for field_name, field_info in config_fields.items(): - field_data = { - "name": field_name, - "type": field_info.get("type", "unknown"), - "default": field_info.get("default"), - "description": field_info.get("description", ""), - "current_value": _current_config.get(field_name, field_info.get("default")), - } - - if field_info.get("locked", False): - field_data["locked_value"] = LOCKED_FIELDS.get("env", {}).get(field_name) - locked.append(field_data) - else: - configurable.append(field_data) - - return json.dumps({ - "environment": _current_env, - "configurable_fields": configurable, - "locked_fields": locked, - "tip": "Use rl_edit_config(field, value) to change any configurable field.", - }, indent=2) - - -async def rl_edit_config(field: str, value: Any) -> str: - """ - Update a configuration field. - - Use rl_get_current_config() first to see available fields for the - selected environment. Each environment has different options. - - Locked fields (infrastructure settings) cannot be changed. - - Args: - field: Name of the field to update (from rl_get_current_config) - value: New value for the field - - Returns: - JSON string with updated config or error message - """ - if not _current_env: - return json.dumps({ - "error": "No environment selected. Use rl_select_environment(name) first.", - }, indent=2) - - config_fields = _env_config_cache.get(_current_env, {}) - - if field not in config_fields: - return json.dumps({ - "error": f"Unknown field '{field}'", - "available_fields": list(config_fields.keys()), - }, indent=2) - - field_info = config_fields[field] - if field_info.get("locked", False): - return json.dumps({ - "error": f"Field '{field}' is locked and cannot be changed", - "locked_value": LOCKED_FIELDS.get("env", {}).get(field), - }, indent=2) - - _current_config[field] = value - - return json.dumps({ - "message": f"Updated {field} = {value}", - "field": field, - "value": value, - "config": _current_config, - }, indent=2) - - -# ============================================================================ -# Training Management Tools -# ============================================================================ - -async def rl_start_training() -> str: - """ - Start a new RL training run with the current environment and config. - - Requires an environment to be selected first using rl_select_environment(). - Use rl_edit_config() to adjust configuration before starting. - - This spawns three processes: - 1. run-api (Atropos trajectory API) - 2. launch_training.py (Tinker trainer + inference server) - 3. environment.py serve (the selected environment) - - WARNING: Training runs take hours. Use rl_check_status() to monitor - progress (recommended: check every 30 minutes at most). - - Returns: - JSON string with run_id and initial status - """ - if not _current_env: - return json.dumps({ - "error": "No environment selected. Use rl_select_environment(name) first.", - }, indent=2) - - # Check API keys - if not os.getenv("TINKER_API_KEY"): - return json.dumps({ - "error": "TINKER_API_KEY not set. Add it to ~/.hermes/.env", - }, indent=2) - - # Find environment file - env_info = None - for env in _environments: - if env.name == _current_env: - env_info = env - break - - if not env_info or not Path(env_info.file_path).exists(): - return json.dumps({ - "error": f"Environment file not found for '{_current_env}'", - }, indent=2) - - # Generate run ID - run_id = str(uuid.uuid4())[:8] - - # Create config YAML - CONFIGS_DIR.mkdir(exist_ok=True) - config_path = CONFIGS_DIR / f"run_{run_id}.yaml" - - # Start with locked config as base - import copy - run_config = copy.deepcopy(LOCKED_FIELDS) - - if "env" not in run_config: - run_config["env"] = {} - - # Apply configurable fields - for field_name, value in _current_config.items(): - if value is not None and value != "": - run_config["env"][field_name] = value - - # Set WandB settings - wandb_project = _current_config.get("wandb_project", "atropos-tinker") - if "tinker" not in run_config: - run_config["tinker"] = {} - run_config["tinker"]["wandb_project"] = wandb_project - run_config["tinker"]["wandb_run_name"] = f"{_current_env}-{run_id}" - - if "wandb_name" in _current_config and _current_config["wandb_name"]: - run_config["env"]["wandb_name"] = _current_config["wandb_name"] - - with open(config_path, "w", encoding="utf-8") as f: - yaml.dump(run_config, f, default_flow_style=False) - - # Create run state - run_state = RunState( - run_id=run_id, - environment=_current_env, - config=_current_config.copy(), - status="starting", - wandb_project=wandb_project, - wandb_run_name=f"{_current_env}-{run_id}", - ) - - _active_runs[run_id] = run_state - - # Start training in background - asyncio.create_task(_spawn_training_run(run_state, config_path)) - - return json.dumps({ - "run_id": run_id, - "status": "starting", - "environment": _current_env, - "config": _current_config, - "wandb_project": wandb_project, - "wandb_run_name": f"{_current_env}-{run_id}", - "config_path": str(config_path), - "logs": { - "api": str(LOGS_DIR / f"api_{run_id}.log"), - "trainer": str(LOGS_DIR / f"trainer_{run_id}.log"), - "env": str(LOGS_DIR / f"env_{run_id}.log"), - }, - "message": "Training starting. Use rl_check_status(run_id) to monitor (recommended: every 30 minutes).", - }, indent=2) - - -async def rl_check_status(run_id: str) -> str: - """ - Get status and metrics for a training run. - - RATE LIMITED: For long-running training, this function enforces a - minimum 30-minute interval between checks for the same run_id. - - Args: - run_id: The run ID returned by rl_start_training() - - Returns: - JSON string with run status and metrics - """ - # Check rate limiting - now = time.time() - if run_id in _last_status_check: - elapsed = now - _last_status_check[run_id] - if elapsed < MIN_STATUS_CHECK_INTERVAL: - remaining = MIN_STATUS_CHECK_INTERVAL - elapsed - return json.dumps({ - "rate_limited": True, - "run_id": run_id, - "message": f"Rate limited. Next check available in {remaining/60:.0f} minutes.", - "next_check_in_seconds": remaining, - }, indent=2) - - _last_status_check[run_id] = now - - if run_id not in _active_runs: - return json.dumps({ - "error": f"Run '{run_id}' not found", - "active_runs": list(_active_runs.keys()), - }, indent=2) - - run_state = _active_runs[run_id] - - # Check process status - processes = { - "api": run_state.api_process.poll() if run_state.api_process else None, - "trainer": run_state.trainer_process.poll() if run_state.trainer_process else None, - "env": run_state.env_process.poll() if run_state.env_process else None, - } - - running_time = time.time() - run_state.start_time if run_state.start_time else 0 - - result = { - "run_id": run_id, - "status": run_state.status, - "environment": run_state.environment, - "running_time_minutes": running_time / 60, - "processes": { - name: "running" if code is None else f"exited ({code})" - for name, code in processes.items() - }, - "wandb_project": run_state.wandb_project, - "wandb_run_name": run_state.wandb_run_name, - "logs": { - "api": str(LOGS_DIR / f"api_{run_id}.log"), - "trainer": str(LOGS_DIR / f"trainer_{run_id}.log"), - "env": str(LOGS_DIR / f"env_{run_id}.log"), - }, - } - - if run_state.error_message: - result["error"] = run_state.error_message - - # Try to get WandB metrics if available - try: - import wandb - api = wandb.Api() - runs = api.runs( - f"{os.getenv('WANDB_ENTITY', 'nousresearch')}/{run_state.wandb_project}", - filters={"display_name": run_state.wandb_run_name} - ) - if runs: - wandb_run = runs[0] - result["wandb_url"] = wandb_run.url - result["metrics"] = { - "step": wandb_run.summary.get("_step", 0), - "reward_mean": wandb_run.summary.get("train/reward_mean"), - "percent_correct": wandb_run.summary.get("train/percent_correct"), - "eval_percent_correct": wandb_run.summary.get("eval/percent_correct"), - } - except Exception as e: - result["wandb_error"] = str(e) - - return json.dumps(result, indent=2) - - -async def rl_stop_training(run_id: str) -> str: - """ - Stop a running training job. - - Args: - run_id: The run ID to stop - - Returns: - JSON string with stop confirmation - """ - if run_id not in _active_runs: - return json.dumps({ - "error": f"Run '{run_id}' not found", - "active_runs": list(_active_runs.keys()), - }, indent=2) - - run_state = _active_runs[run_id] - - if run_state.status not in {"running", "starting"}: - return json.dumps({ - "message": f"Run '{run_id}' is not running (status: {run_state.status})", - }, indent=2) - - _stop_training_run(run_state) - - return json.dumps({ - "message": f"Stopped training run '{run_id}'", - "run_id": run_id, - "status": run_state.status, - }, indent=2) - - -async def rl_get_results(run_id: str) -> str: - """ - Get final results and metrics for a training run. - - Args: - run_id: The run ID to get results for - - Returns: - JSON string with final results - """ - if run_id not in _active_runs: - return json.dumps({ - "error": f"Run '{run_id}' not found", - }, indent=2) - - run_state = _active_runs[run_id] - - result = { - "run_id": run_id, - "status": run_state.status, - "environment": run_state.environment, - "wandb_project": run_state.wandb_project, - "wandb_run_name": run_state.wandb_run_name, - } - - # Get WandB metrics - try: - import wandb - api = wandb.Api() - runs = api.runs( - f"{os.getenv('WANDB_ENTITY', 'nousresearch')}/{run_state.wandb_project}", - filters={"display_name": run_state.wandb_run_name} - ) - if runs: - wandb_run = runs[0] - result["wandb_url"] = wandb_run.url - result["final_metrics"] = dict(wandb_run.summary) - result["history"] = [dict(row) for row in wandb_run.history(samples=10)] - except Exception as e: - result["wandb_error"] = str(e) - - return json.dumps(result, indent=2) - - -async def rl_list_runs() -> str: - """ - List all training runs (active and completed). - - Returns: - JSON string with list of runs and their status - """ - runs = [] - for run_id, run_state in _active_runs.items(): - runs.append({ - "run_id": run_id, - "environment": run_state.environment, - "status": run_state.status, - "wandb_run_name": run_state.wandb_run_name, - }) - - return json.dumps({ - "runs": runs, - "count": len(runs), - }, indent=2) - - -# ============================================================================ -# Inference Testing (via Atropos `process` mode with OpenRouter) -# ============================================================================ - -# Test models at different scales for robustness testing -# These are cheap, capable models on OpenRouter for testing parsing/scoring -TEST_MODELS = [ - {"id": "qwen/qwen3-8b", "name": "Qwen3 8B", "scale": "small"}, - {"id": "z-ai/glm-4.7-flash", "name": "GLM-4.7 Flash", "scale": "medium"}, - {"id": "minimax/minimax-m2.7", "name": "MiniMax M2.7", "scale": "large"}, -] - -# Default test parameters - quick but representative -DEFAULT_NUM_STEPS = 3 # Number of steps (items) to test -DEFAULT_GROUP_SIZE = 16 # Completions per item (like training) - - -async def rl_test_inference( - num_steps: int = DEFAULT_NUM_STEPS, - group_size: int = DEFAULT_GROUP_SIZE, - models: Optional[List[str]] = None, -) -> str: - """ - Quick inference test for any environment using Atropos's `process` mode. - - Runs a few steps of inference + scoring to validate: - - Environment loads correctly - - Prompt construction works - - Inference parsing is robust (tested with multiple model scales) - - Verifier/scoring logic works - - Default: 3 steps × 16 completions = 48 total rollouts per model. - Tests 3 models = 144 total rollouts. Quick sanity check. - - Test models (varying intelligence levels for robustness): - - qwen/qwen3-8b (small) - - zhipu-ai/glm-4-flash (medium) - - minimax/minimax-m1 (large) - - Args: - num_steps: Steps to run (default: 3, max recommended for testing) - group_size: Completions per step (default: 16, like training) - models: Optional model IDs to test. If None, uses all 3 test models. - - Returns: - JSON with results per model: steps_tested, accuracy, scores - """ - if not _current_env: - return json.dumps({ - "error": "No environment selected. Use rl_select_environment(name) first.", - }, indent=2) - - api_key = os.getenv("OPENROUTER_API_KEY") - if not api_key: - return json.dumps({ - "error": "OPENROUTER_API_KEY not set. Required for inference testing.", - }, indent=2) - - # Find environment info - env_info = None - for env in _environments: - if env.name == _current_env: - env_info = env - break - - if not env_info: - return json.dumps({ - "error": f"Environment '{_current_env}' not found", - }, indent=2) - - # Determine which models to test - if models: - test_models = [m for m in TEST_MODELS if m["id"] in models] - if not test_models: - test_models = [{"id": m, "name": m, "scale": "custom"} for m in models] - else: - test_models = TEST_MODELS - - # Calculate total rollouts for logging - total_rollouts_per_model = num_steps * group_size - total_rollouts = total_rollouts_per_model * len(test_models) - - results = { - "environment": _current_env, - "environment_file": env_info.file_path, - "test_config": { - "num_steps": num_steps, - "group_size": group_size, - "rollouts_per_model": total_rollouts_per_model, - "total_rollouts": total_rollouts, - }, - "models_tested": [], - } - - # Create output directory for test results - _ensure_logs_dir() - test_output_dir = LOGS_DIR / "inference_tests" - test_output_dir.mkdir(exist_ok=True) - - for model_info in test_models: - model_id = model_info["id"] - model_safe_name = model_id.replace("/", "_") - - print(f"\n{'='*60}") - print(f"Testing with {model_info['name']} ({model_id})") - print(f"{'='*60}") - - # Output file for this test run - output_file = test_output_dir / f"test_{_current_env}_{model_safe_name}.jsonl" - - # Generate unique run ID for wandb - test_run_id = str(uuid.uuid4())[:8] - wandb_run_name = f"test_inference_RSIAgent_{_current_env}_{test_run_id}" - - # Build the process command using Atropos's built-in CLI - # This runs the environment's actual code with OpenRouter as the inference backend - # We pass our locked settings + test-specific overrides via CLI args - cmd = [ - sys.executable, env_info.file_path, "process", - # Test-specific overrides - "--env.total_steps", str(num_steps), - "--env.group_size", str(group_size), - "--env.use_wandb", "true", # Enable wandb for test tracking - "--env.wandb_name", wandb_run_name, - "--env.data_path_to_save_groups", str(output_file), - # Use locked settings from our config - "--env.tokenizer_name", LOCKED_FIELDS["env"]["tokenizer_name"], - "--env.max_token_length", str(LOCKED_FIELDS["env"]["max_token_length"]), - "--env.max_num_workers", str(LOCKED_FIELDS["env"]["max_num_workers"]), - "--env.max_batches_offpolicy", str(LOCKED_FIELDS["env"]["max_batches_offpolicy"]), - # OpenRouter config for inference testing - # IMPORTANT: Use server_type=openai for OpenRouter (not sglang) - # sglang is only for actual training with Tinker's inference server - "--openai.base_url", "https://openrouter.ai/api/v1", - "--openai.api_key", api_key, - "--openai.model_name", model_id, - "--openai.server_type", "openai", # OpenRouter is OpenAI-compatible - "--openai.health_check", "false", # OpenRouter doesn't have health endpoint - ] - - # Debug: Print the full command - cmd_str = " ".join(str(c) for c in cmd) - # Hide API key in printed output - cmd_display = cmd_str.replace(api_key, "***API_KEY***") - print(f"Command: {cmd_display}") - print(f"Working dir: {TINKER_ATROPOS_ROOT}") - print(f"WandB run: {wandb_run_name}") - print(f" {num_steps} steps × {group_size} completions = {total_rollouts_per_model} rollouts") - - model_results = { - "model": model_id, - "name": model_info["name"], - "scale": model_info["scale"], - "wandb_run": wandb_run_name, - "output_file": str(output_file), - "steps": [], - "steps_tested": 0, - "total_completions": 0, - "correct_completions": 0, - } - - try: - # Run the process command with real-time output streaming - process = await asyncio.create_subprocess_exec( - *cmd, - stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE, - cwd=str(TINKER_ATROPOS_ROOT), - ) - - # Stream output in real-time while collecting for logs - stdout_lines = [] - stderr_lines = [] - log_file = test_output_dir / f"test_{_current_env}_{model_safe_name}.log" - - async def read_stream(stream, lines_list, prefix=""): - """Read stream line by line and print in real-time.""" - while True: - line = await stream.readline() - if not line: - break - decoded = line.decode().rstrip() - lines_list.append(decoded) - # Print progress-related lines in real-time - if any(kw in decoded.lower() for kw in ['processing', 'group', 'step', 'progress', '%', 'completed']): - print(f" {prefix}{decoded}") - - # Read both streams concurrently with timeout - try: - await asyncio.wait_for( - asyncio.gather( - read_stream(process.stdout, stdout_lines, "📊 "), - read_stream(process.stderr, stderr_lines, "⚠️ "), - ), - timeout=600, # 10 minute timeout per model - ) - except asyncio.TimeoutError: - process.kill() - raise - - await process.wait() - - # Combine output for logging - stdout_text = "\n".join(stdout_lines) - stderr_text = "\n".join(stderr_lines) - - # Write logs to files for inspection outside CLI - with open(log_file, "w", encoding="utf-8") as f: - f.write(f"Command: {cmd_display}\n") - f.write(f"Working dir: {TINKER_ATROPOS_ROOT}\n") - f.write(f"Return code: {process.returncode}\n") - f.write(f"\n{'='*60}\n") - f.write(f"STDOUT:\n{'='*60}\n") - f.write(stdout_text or "(empty)\n") - f.write(f"\n{'='*60}\n") - f.write(f"STDERR:\n{'='*60}\n") - f.write(stderr_text or "(empty)\n") - - print(f" Log file: {log_file}") - - if process.returncode != 0: - model_results["error"] = f"Process exited with code {process.returncode}" - model_results["stderr"] = stderr_text[-1000:] - model_results["stdout"] = stdout_text[-1000:] - model_results["log_file"] = str(log_file) - print(f"\n ❌ Error: {model_results['error']}") - # Print last few lines of stderr for debugging - if stderr_lines: - print(" Last errors:") - for line in stderr_lines[-5:]: - print(f" {line}") - else: - print("\n ✅ Process completed successfully") - print(f" Output file: {output_file}") - print(f" File exists: {output_file.exists()}") - - # Parse the output JSONL file - if output_file.exists(): - # Read JSONL file (one JSON object per line = one step) - with open(output_file, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - item = json.loads(line) - scores = item.get("scores", []) - model_results["steps_tested"] += 1 - model_results["total_completions"] += len(scores) - correct = sum(1 for s in scores if s > 0) - model_results["correct_completions"] += correct - - model_results["steps"].append({ - "step": model_results["steps_tested"], - "completions": len(scores), - "correct": correct, - "scores": scores, - }) - except json.JSONDecodeError: - continue - - print(f" Completed {model_results['steps_tested']} steps") - else: - model_results["error"] = f"Output file not created: {output_file}" - - except asyncio.TimeoutError: - model_results["error"] = "Process timed out after 10 minutes" - print(" Timeout!") - except Exception as e: - model_results["error"] = str(e) - print(f" Error: {e}") - - # Calculate stats - if model_results["total_completions"] > 0: - model_results["accuracy"] = round( - model_results["correct_completions"] / model_results["total_completions"], 3 - ) - else: - model_results["accuracy"] = 0 - - if model_results["steps_tested"] > 0: - steps_with_correct = sum(1 for s in model_results["steps"] if s.get("correct", 0) > 0) - model_results["steps_with_correct"] = steps_with_correct - model_results["step_success_rate"] = round( - steps_with_correct / model_results["steps_tested"], 3 - ) - else: - model_results["steps_with_correct"] = 0 - model_results["step_success_rate"] = 0 - - print(f" Results: {model_results['correct_completions']}/{model_results['total_completions']} correct") - print(f" Accuracy: {model_results['accuracy']:.1%}") - - results["models_tested"].append(model_results) - - # Overall summary - working_models = [m for m in results["models_tested"] if m.get("steps_tested", 0) > 0] - - results["summary"] = { - "steps_requested": num_steps, - "models_tested": len(test_models), - "models_succeeded": len(working_models), - "best_model": max(working_models, key=lambda x: x.get("accuracy", 0))["model"] if working_models else None, - "avg_accuracy": round( - sum(m.get("accuracy", 0) for m in working_models) / len(working_models), 3 - ) if working_models else 0, - "environment_working": bool(working_models), - "output_directory": str(test_output_dir), - } - - return json.dumps(results, indent=2) - - -# ============================================================================ -# Requirements Check -# ============================================================================ - -def check_rl_python_version() -> bool: - """ - Check if Python version meets the minimum for RL tools. - - tinker-atropos depends on the 'tinker' package which requires Python >= 3.11. - """ - return sys.version_info >= (3, 11) - - -def check_rl_api_keys() -> bool: - """ - Check if required API keys and Python version are available. - - RL training requires: - - Python >= 3.11 (tinker package requirement) - - TINKER_API_KEY for the Tinker training API - - WANDB_API_KEY for Weights & Biases metrics - """ - if not check_rl_python_version(): - return False - tinker_key = os.getenv("TINKER_API_KEY") - wandb_key = os.getenv("WANDB_API_KEY") - return bool(tinker_key) and bool(wandb_key) - - -def get_missing_keys() -> List[str]: - """ - Get list of missing requirements for RL tools (API keys and Python version). - """ - missing = [] - if not check_rl_python_version(): - missing.append(f"Python >= 3.11 (current: {sys.version_info.major}.{sys.version_info.minor})") - if not os.getenv("TINKER_API_KEY"): - missing.append("TINKER_API_KEY") - if not os.getenv("WANDB_API_KEY"): - missing.append("WANDB_API_KEY") - return missing - - -# --------------------------------------------------------------------------- -# Schemas + Registry -# --------------------------------------------------------------------------- -from tools.registry import registry - -RL_LIST_ENVIRONMENTS_SCHEMA = {"name": "rl_list_environments", "description": "List all available RL environments. Returns environment names, paths, and descriptions. TIP: Read the file_path with file tools to understand how each environment works (verifiers, data loading, rewards).", "parameters": {"type": "object", "properties": {}, "required": []}} -RL_SELECT_ENVIRONMENT_SCHEMA = {"name": "rl_select_environment", "description": "Select an RL environment for training. Loads the environment's default configuration. After selecting, use rl_get_current_config() to see settings and rl_edit_config() to modify them.", "parameters": {"type": "object", "properties": {"name": {"type": "string", "description": "Name of the environment to select (from rl_list_environments)"}}, "required": ["name"]}} -RL_GET_CURRENT_CONFIG_SCHEMA = {"name": "rl_get_current_config", "description": "Get the current environment configuration. Returns only fields that can be modified: group_size, max_token_length, total_steps, steps_per_eval, use_wandb, wandb_name, max_num_workers.", "parameters": {"type": "object", "properties": {}, "required": []}} -RL_EDIT_CONFIG_SCHEMA = {"name": "rl_edit_config", "description": "Update a configuration field. Use rl_get_current_config() first to see all available fields for the selected environment. Each environment has different configurable options. Infrastructure settings (tokenizer, URLs, lora_rank, learning_rate) are locked.", "parameters": {"type": "object", "properties": {"field": {"type": "string", "description": "Name of the field to update (get available fields from rl_get_current_config)"}, "value": {"description": "New value for the field"}}, "required": ["field", "value"]}} -RL_START_TRAINING_SCHEMA = {"name": "rl_start_training", "description": "Start a new RL training run with the current environment and config. Most training parameters (lora_rank, learning_rate, etc.) are fixed. Use rl_edit_config() to set group_size, batch_size, wandb_project before starting. WARNING: Training takes hours.", "parameters": {"type": "object", "properties": {}, "required": []}} -RL_CHECK_STATUS_SCHEMA = {"name": "rl_check_status", "description": "Get status and metrics for a training run. RATE LIMITED: enforces 30-minute minimum between checks for the same run. Returns WandB metrics: step, state, reward_mean, loss, percent_correct.", "parameters": {"type": "object", "properties": {"run_id": {"type": "string", "description": "The run ID from rl_start_training()"}}, "required": ["run_id"]}} -RL_STOP_TRAINING_SCHEMA = {"name": "rl_stop_training", "description": "Stop a running training job. Use if metrics look bad, training is stagnant, or you want to try different settings.", "parameters": {"type": "object", "properties": {"run_id": {"type": "string", "description": "The run ID to stop"}}, "required": ["run_id"]}} -RL_GET_RESULTS_SCHEMA = {"name": "rl_get_results", "description": "Get final results and metrics for a completed training run. Returns final metrics and path to trained weights.", "parameters": {"type": "object", "properties": {"run_id": {"type": "string", "description": "The run ID to get results for"}}, "required": ["run_id"]}} -RL_LIST_RUNS_SCHEMA = {"name": "rl_list_runs", "description": "List all training runs (active and completed) with their status.", "parameters": {"type": "object", "properties": {}, "required": []}} -RL_TEST_INFERENCE_SCHEMA = {"name": "rl_test_inference", "description": "Quick inference test for any environment. Runs a few steps of inference + scoring using OpenRouter. Default: 3 steps x 16 completions = 48 rollouts per model, testing 3 models = 144 total. Tests environment loading, prompt construction, inference parsing, and verifier logic. Use BEFORE training to catch issues.", "parameters": {"type": "object", "properties": {"num_steps": {"type": "integer", "description": "Number of steps to run (default: 3, recommended max for testing)", "default": 3}, "group_size": {"type": "integer", "description": "Completions per step (default: 16, like training)", "default": 16}, "models": {"type": "array", "items": {"type": "string"}, "description": "Optional list of OpenRouter model IDs. Default: qwen/qwen3-8b, z-ai/glm-4.7-flash, minimax/minimax-m2.7"}}, "required": []}} - -_rl_env = ["TINKER_API_KEY", "WANDB_API_KEY"] - -registry.register(name="rl_list_environments", emoji="🧪", toolset="rl", schema=RL_LIST_ENVIRONMENTS_SCHEMA, - handler=lambda args, **kw: rl_list_environments(), check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) -registry.register(name="rl_select_environment", emoji="🧪", toolset="rl", schema=RL_SELECT_ENVIRONMENT_SCHEMA, - handler=lambda args, **kw: rl_select_environment(name=args.get("name", "")), check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) -registry.register(name="rl_get_current_config", emoji="🧪", toolset="rl", schema=RL_GET_CURRENT_CONFIG_SCHEMA, - handler=lambda args, **kw: rl_get_current_config(), check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) -registry.register(name="rl_edit_config", emoji="🧪", toolset="rl", schema=RL_EDIT_CONFIG_SCHEMA, - handler=lambda args, **kw: rl_edit_config(field=args.get("field", ""), value=args.get("value")), check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) -registry.register(name="rl_start_training", emoji="🧪", toolset="rl", schema=RL_START_TRAINING_SCHEMA, - handler=lambda args, **kw: rl_start_training(), check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) -registry.register(name="rl_check_status", emoji="🧪", toolset="rl", schema=RL_CHECK_STATUS_SCHEMA, - handler=lambda args, **kw: rl_check_status(run_id=args.get("run_id", "")), check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) -registry.register(name="rl_stop_training", emoji="🧪", toolset="rl", schema=RL_STOP_TRAINING_SCHEMA, - handler=lambda args, **kw: rl_stop_training(run_id=args.get("run_id", "")), check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) -registry.register(name="rl_get_results", emoji="🧪", toolset="rl", schema=RL_GET_RESULTS_SCHEMA, - handler=lambda args, **kw: rl_get_results(run_id=args.get("run_id", "")), check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) -registry.register(name="rl_list_runs", emoji="🧪", toolset="rl", schema=RL_LIST_RUNS_SCHEMA, - handler=lambda args, **kw: rl_list_runs(), check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) -registry.register(name="rl_test_inference", emoji="🧪", toolset="rl", schema=RL_TEST_INFERENCE_SCHEMA, - handler=lambda args, **kw: rl_test_inference(num_steps=args.get("num_steps", 3), group_size=args.get("group_size", 16), models=args.get("models")), - check_fn=check_rl_api_keys, requires_env=_rl_env, is_async=True) diff --git a/toolsets.py b/toolsets.py index c664136c52a..8ec45f11a2f 100644 --- a/toolsets.py +++ b/toolsets.py @@ -169,18 +169,7 @@ TOOLSETS = { "tools": ["send_message"], "includes": [] }, - - "rl": { - "description": "RL training tools for running reinforcement learning on Tinker-Atropos", - "tools": [ - "rl_list_environments", "rl_select_environment", - "rl_get_current_config", "rl_edit_config", - "rl_start_training", "rl_check_status", - "rl_stop_training", "rl_get_results", - "rl_list_runs", "rl_test_inference" - ], - "includes": [] - }, + "file": { "description": "File manipulation tools: read, write, patch (with fuzzy matching), and search (content + files)", @@ -390,7 +379,7 @@ TOOLSETS = { # Mirrors hermes-cli so cron's "default" toolset is the same set of # core tools users see interactively — then `hermes tools` filters # them down per the platform config. _DEFAULT_OFF_TOOLSETS (moa, - # homeassistant, rl) are excluded by _get_platform_tools() unless + # homeassistant) are excluded by _get_platform_tools() unless # the user explicitly enables them. "description": "Default cron toolset - same core tools as hermes-cli; gated by `hermes tools`", "tools": _HERMES_CORE_TOOLS, diff --git a/uv.lock b/uv.lock index a519cc2b194..72cef3b0cdd 100644 --- a/uv.lock +++ b/uv.lock @@ -301,22 +301,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/72/9e/c394b4e2104766fb28a1e44e3ed36e4c7773b4d05c868e482be99d5635c9/alibabacloud_tea_util-0.3.14-py3-none-any.whl", hash = "sha256:10d3e5c340d8f7ec69dd27345eb2fc5a1dab07875742525edf07bbe86db93bfe", size = 6697, upload-time = "2025-11-19T06:01:07.355Z" }, ] -[[package]] -name = "altair" -version = "6.0.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "jinja2", marker = "python_full_version >= '3.12'" }, - { name = "jsonschema", marker = "python_full_version >= '3.12'" }, - { name = "narwhals", marker = "python_full_version >= '3.12'" }, - { name = "packaging", marker = "python_full_version >= '3.12'" }, - { name = "typing-extensions", marker = "python_full_version >= '3.12' and python_full_version < '3.15'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/f7/c0/184a89bd5feba14ff3c41cfaf1dd8a82c05f5ceedbc92145e17042eb08a4/altair-6.0.0.tar.gz", hash = "sha256:614bf5ecbe2337347b590afb111929aa9c16c9527c4887d96c9bc7f6640756b4", size = 763834, upload-time = "2025-11-12T08:59:11.519Z" } -wheels = [ - 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{ name = "litellm", marker = "python_full_version >= '3.12'" }, - { name = "matplotlib", marker = "python_full_version >= '3.12'" }, - { name = "plotly", marker = "python_full_version >= '3.12'" }, - { name = "pydantic", marker = "python_full_version >= '3.12'" }, - { name = "python-dotenv", marker = "python_full_version >= '3.12'" }, - { name = "sqlalchemy", marker = "python_full_version >= '3.12'" }, - { name = "streamlit", marker = "python_full_version >= '3.12'" }, - { name = "typer", marker = "python_full_version >= '3.12'" }, -] - [[package]] name = "youtube-transcript-api" version = "1.2.4" diff --git a/website/docs/developer-guide/architecture.md b/website/docs/developer-guide/architecture.md index af2b0a2fd4b..b5e2add8993 100644 --- a/website/docs/developer-guide/architecture.md +++ b/website/docs/developer-guide/architecture.md @@ -127,7 +127,6 @@ hermes-agent/ ├── cron/ # Scheduler (jobs.py, scheduler.py) ├── plugins/memory/ # Memory provider plugins ├── plugins/context_engine/ # Context engine plugins -├── environments/ # RL training environments (Atropos) ├── skills/ # Bundled skills (always available) ├── optional-skills/ # Official optional skills (install explicitly) ├── website/ # Docusaurus documentation site @@ -185,7 +184,6 @@ If you are new to the codebase: 8. **[Gateway Internals](./gateway-internals.md)** — messaging platform gateway 9. **[Context Compression & Prompt Caching](./context-compression-and-caching.md)** — compression and caching 10. **[ACP Internals](./acp-internals.md)** — IDE integration -11. **[Environments, Benchmarks & Data Generation](./environments.md)** — RL training ## Major Subsystems @@ -247,11 +245,11 @@ Exposes Hermes as an editor-native agent over stdio/JSON-RPC for VS Code, Zed, a → [ACP Internals](./acp-internals.md) -### RL / Environments / Trajectories +### Trajectories -Full environment framework for evaluation and RL training. Integrates with Atropos, supports multiple tool-call parsers, and generates ShareGPT-format trajectories. +Generates ShareGPT-format trajectories from agent sessions for training data generation. -→ [Environments, Benchmarks & Data Generation](./environments.md), [Trajectories & Training Format](./trajectory-format.md) +→ [Trajectories & Training Format](./trajectory-format.md) ## Design Principles diff --git a/website/docs/developer-guide/contributing.md b/website/docs/developer-guide/contributing.md index 6e00e367330..b3bf9799d71 100644 --- a/website/docs/developer-guide/contributing.md +++ b/website/docs/developer-guide/contributing.md @@ -50,9 +50,6 @@ export VIRTUAL_ENV="$(pwd)/venv" # Install with all extras (messaging, cron, CLI menus, dev tools) uv pip install -e ".[all,dev]" -# tinker-atropos is a git submodule — needs `git submodule update --init` first -# if you didn't clone with `--recurse-submodules` -uv pip install -e "./tinker-atropos" # Optional: browser tools npm install diff --git a/website/docs/developer-guide/environments.md b/website/docs/developer-guide/environments.md deleted file mode 100644 index 0a5aa00ffff..00000000000 --- a/website/docs/developer-guide/environments.md +++ /dev/null @@ -1,520 +0,0 @@ ---- -sidebar_position: 5 -title: "Environments, Benchmarks & Data Generation" -description: "Building RL training environments, running evaluation benchmarks, and generating SFT data with the Hermes-Agent Atropos integration" ---- - -# Environments, Benchmarks & Data Generation - -Hermes Agent includes a full environment framework that connects its tool-calling capabilities to the [Atropos](https://github.com/NousResearch/atropos) RL training framework. This enables three workflows: - -1. **RL Training** — Train language models on multi-turn agentic tasks with GRPO -2. **Benchmarks** — Evaluate models on standardised agentic benchmarks -3. **Data Generation** — Generate SFT training data from agent rollouts - -All three share the same core: an **environment** class that defines tasks, runs an agent loop, and scores the output. - -:::info Repo environments vs RL training tools -The Python environment framework documented here lives under the repo's `environments/` directory and is the implementation-level API for Hermes/Atropos integration. This is separate from the user-facing `rl_*` tools, which operate as an orchestration surface for remote RL training workflows. -::: - -:::tip Quick Links -- **Want to run benchmarks?** Jump to [Available Benchmarks](#available-benchmarks) -- **Want to train with RL?** See [RL Training Tools](/user-guide/features/rl-training) for the agent-driven interface, or [Running Environments](#running-environments) for manual execution -- **Want to create a new environment?** See [Creating Environments](#creating-environments) -::: - -## Architecture - -The environment system is built on a three-layer inheritance chain: - -```mermaid -classDiagram - class BaseEnv { - Server management - Worker scheduling - Wandb logging - CLI: serve / process / evaluate - } - - class HermesAgentBaseEnv { - Terminal backend configuration - Tool resolution - Agent loop engine - ToolContext access - } - - class TerminalTestEnv { - Stack testing - } - - class HermesSweEnv { - SWE training - } - - class TerminalBench2EvalEnv { - Benchmark evaluation - } - - class TBLiteEvalEnv { - Fast benchmark - } - - class YCBenchEvalEnv { - Long-horizon benchmark - } - - BaseEnv <|-- HermesAgentBaseEnv - HermesAgentBaseEnv <|-- TerminalTestEnv - HermesAgentBaseEnv <|-- HermesSweEnv - HermesAgentBaseEnv <|-- TerminalBench2EvalEnv - TerminalBench2EvalEnv <|-- TBLiteEvalEnv - TerminalBench2EvalEnv <|-- YCBenchEvalEnv -``` - -### BaseEnv (Atropos) - -The foundation from `atroposlib`. Provides: -- **Server management** — connects to OpenAI-compatible APIs (VLLM, SGLang, OpenRouter) -- **Worker scheduling** — parallel rollout coordination -- **Wandb integration** — metrics logging and rollout visualisation -- **CLI interface** — three subcommands: `serve`, `process`, `evaluate` -- **Eval logging** — `evaluate_log()` saves results to JSON + JSONL - -### HermesAgentBaseEnv - -The hermes-agent layer (`environments/hermes_base_env.py`). Adds: -- **Terminal backend configuration** — sets `TERMINAL_ENV` for sandboxed execution (local, Docker, Modal, Daytona, SSH, Singularity) -- **Tool resolution** — `_resolve_tools_for_group()` calls hermes-agent's `get_tool_definitions()` to get the right tool schemas based on enabled/disabled toolsets -- **Agent loop integration** — `collect_trajectory()` runs `HermesAgentLoop` and scores the result -- **Two-phase operation** — Phase 1 (OpenAI server) for eval/SFT, Phase 2 (VLLM ManagedServer) for full RL with logprobs -- **Async safety patches** — monkey-patches Modal backend to work inside Atropos's event loop - -### Concrete Environments - -Your environment inherits from `HermesAgentBaseEnv` and implements five methods: - -| Method | Purpose | -|--------|---------| -| `setup()` | Load dataset, initialise state | -| `get_next_item()` | Return the next item for rollout | -| `format_prompt(item)` | Convert an item into the user message | -| `compute_reward(item, result, ctx)` | Score the rollout (0.0–1.0) | -| `evaluate()` | Periodic evaluation logic | - -## Core Components - -### Agent Loop - -`HermesAgentLoop` (`environments/agent_loop.py`) is the reusable multi-turn agent engine. It runs the same tool-calling pattern as hermes-agent's main loop: - -1. Send messages + tool schemas to the API via `server.chat_completion()` -2. If the response contains `tool_calls`, dispatch each via `handle_function_call()` -3. Append tool results to the conversation, go back to step 1 -4. If no `tool_calls`, the agent is done - -Tool calls execute in a thread pool (`ThreadPoolExecutor(128)`) so that async backends (Modal, Docker) don't deadlock inside Atropos's event loop. - -Returns an `AgentResult`: - -```python -@dataclass -class AgentResult: - messages: List[Dict[str, Any]] # Full conversation history - turns_used: int # Number of LLM calls made - finished_naturally: bool # True if model stopped on its own - reasoning_per_turn: List[Optional[str]] # Extracted reasoning content - tool_errors: List[ToolError] # Errors encountered during tool dispatch - managed_state: Optional[Dict] # VLLM ManagedServer state (Phase 2) -``` - -### Tool Context - -`ToolContext` (`environments/tool_context.py`) gives reward functions direct access to the **same sandbox** the model used during its rollout. The `task_id` scoping means all state (files, processes, browser tabs) is preserved. - -```python -async def compute_reward(self, item, result, ctx: ToolContext): - # Run tests in the model's terminal sandbox - test = ctx.terminal("pytest -v") - if test["exit_code"] == 0: - return 1.0 - - # Check if a file was created - content = ctx.read_file("/workspace/solution.py") - if content.get("content"): - return 0.5 - - # Download files for local verification - ctx.download_file("/remote/output.bin", "/local/output.bin") - return 0.0 -``` - -Available methods: - -| Category | Methods | -|----------|---------| -| **Terminal** | `terminal(command, timeout)` | -| **Files** | `read_file(path)`, `write_file(path, content)`, `search(query, path)` | -| **Transfers** | `upload_file()`, `upload_dir()`, `download_file()`, `download_dir()` | -| **Web** | `web_search(query)`, `web_extract(urls)` | -| **Browser** | `browser_navigate(url)`, `browser_snapshot()` | -| **Generic** | `call_tool(name, args)` — escape hatch for any hermes-agent tool | -| **Cleanup** | `cleanup()` — release all resources | - -### Tool Call Parsers - -For **Phase 2** (VLLM ManagedServer), the server returns raw text without structured tool calls. Client-side parsers in `environments/tool_call_parsers/` extract `tool_calls` from raw output: - -```python -from environments.tool_call_parsers import get_parser - -parser = get_parser("hermes") # or "mistral", "llama3_json", "qwen", "deepseek_v3", etc. -content, tool_calls = parser.parse(raw_model_output) -``` - -Available parsers: `hermes`, `mistral`, `llama3_json`, `llama4_json`, `qwen`, `qwen3_coder`, `deepseek_v3`, `deepseek_v3_1` (alias `deepseek_v31`), `kimi_k2`, `longcat`, `glm45`, `glm47`. - -In Phase 1 (OpenAI server type), parsers are not needed — the server handles tool call parsing natively. - -## Available Benchmarks - -### TerminalBench2 - -**89 challenging terminal tasks** with per-task Docker sandbox environments. - -| | | -|---|---| -| **What it tests** | Single-task coding/sysadmin ability | -| **Scoring** | Binary pass/fail (test suite verification) | -| **Sandbox** | Modal cloud sandboxes (per-task Docker images) | -| **Tools** | `terminal` + `file` | -| **Tasks** | 89 tasks across multiple categories | -| **Cost** | ~$50–200 for full eval (parallel execution) | -| **Time** | ~2–4 hours | - -```bash -python environments/benchmarks/terminalbench_2/terminalbench2_env.py evaluate \ - --config environments/benchmarks/terminalbench_2/default.yaml - -# Run specific tasks -python environments/benchmarks/terminalbench_2/terminalbench2_env.py evaluate \ - --config environments/benchmarks/terminalbench_2/default.yaml \ - --env.task_filter fix-git,git-multibranch -``` - -Dataset: [NousResearch/terminal-bench-2](https://huggingface.co/datasets/NousResearch/terminal-bench-2) on HuggingFace. - -### TBLite (OpenThoughts Terminal Bench Lite) - -**100 difficulty-calibrated tasks** — a faster proxy for TerminalBench2. - -| | | -|---|---| -| **What it tests** | Same as TB2 (coding/sysadmin), calibrated difficulty tiers | -| **Scoring** | Binary pass/fail | -| **Sandbox** | Modal cloud sandboxes | -| **Tools** | `terminal` + `file` | -| **Tasks** | 100 tasks: Easy (40), Medium (26), Hard (26), Extreme (8) | -| **Correlation** | r=0.911 with full TB2 | -| **Speed** | 2.6–8× faster than TB2 | - -```bash -python environments/benchmarks/tblite/tblite_env.py evaluate \ - --config environments/benchmarks/tblite/default.yaml -``` - -TBLite is a thin subclass of TerminalBench2 — only the dataset and timeouts differ. Created by the OpenThoughts Agent team (Snorkel AI + Bespoke Labs). Dataset: [NousResearch/openthoughts-tblite](https://huggingface.co/datasets/NousResearch/openthoughts-tblite). - -### YC-Bench - -**Long-horizon strategic benchmark** — the agent plays CEO of an AI startup. - -| | | -|---|---| -| **What it tests** | Multi-turn strategic coherence over hundreds of turns | -| **Scoring** | Composite: `0.5 × survival + 0.5 × normalised_funds` | -| **Sandbox** | Local terminal (no Modal needed) | -| **Tools** | `terminal` only | -| **Runs** | 9 default (3 presets × 3 seeds), sequential | -| **Cost** | ~$50–200 for full eval | -| **Time** | ~3–6 hours | - -```bash -# Install yc-bench (optional dependency) -pip install "hermes-agent[yc-bench]" - -# Run evaluation -bash environments/benchmarks/yc_bench/run_eval.sh - -# Or directly -python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \ - --config environments/benchmarks/yc_bench/default.yaml - -# Quick single-preset test -python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \ - --config environments/benchmarks/yc_bench/default.yaml \ - --env.presets '["fast_test"]' --env.seeds '[1]' -``` - -YC-Bench uses [collinear-ai/yc-bench](https://github.com/collinear-ai/yc-bench) — a deterministic simulation with 4 skill domains (research, inference, data_environment, training), prestige system, employee management, and financial pressure. Unlike TB2's per-task binary scoring, YC-Bench measures whether an agent can maintain coherent strategy over hundreds of compounding decisions. - -## Training Environments - -### TerminalTestEnv - -A minimal self-contained environment with inline tasks (no external dataset). Used for **validating the full stack** end-to-end. Each task asks the model to create a file at a known path; the verifier checks the content. - -```bash -# Process mode (saves rollouts to JSONL, no training server needed) -python environments/terminal_test_env/terminal_test_env.py process \ - --env.data_path_to_save_groups terminal_test_output.jsonl - -# Serve mode (connects to Atropos API for RL training) -python environments/terminal_test_env/terminal_test_env.py serve -``` - -### HermesSweEnv - -SWE-bench style training environment. The model gets a coding task, uses terminal + file + web tools to solve it, and the reward function runs tests in the same Modal sandbox. - -```bash -python environments/hermes_swe_env/hermes_swe_env.py serve \ - --openai.model_name YourModel \ - --env.dataset_name bigcode/humanevalpack \ - --env.terminal_backend modal -``` - -## Running Environments - -Every environment is a standalone Python script with three CLI subcommands: - -### `evaluate` — Run a benchmark - -For eval-only environments (benchmarks). Runs all items, computes metrics, logs to wandb. - -```bash -python environments/benchmarks/tblite/tblite_env.py evaluate \ - --config environments/benchmarks/tblite/default.yaml \ - --openai.model_name anthropic/claude-sonnet-4.6 -``` - -No training server or `run-api` needed. The environment handles everything. - -### `process` — Generate SFT data - -Runs rollouts and saves scored trajectories to JSONL. Useful for generating training data without a full RL loop. - -```bash -python environments/terminal_test_env/terminal_test_env.py process \ - --env.data_path_to_save_groups output.jsonl \ - --openai.model_name anthropic/claude-sonnet-4.6 -``` - -Output format: each line is a scored trajectory with the full conversation history, reward, and metadata. - -### `serve` — Connect to Atropos for RL training - -Connects the environment to a running Atropos API server (`run-api`). Used during live RL training. - -```bash -# Terminal 1: Start the Atropos API -run-api - -# Terminal 2: Start the environment -python environments/hermes_swe_env/hermes_swe_env.py serve \ - --openai.model_name YourModel -``` - -The environment receives items from Atropos, runs agent rollouts, computes rewards, and sends scored trajectories back for training. - -## Two-Phase Operation - -### Phase 1: OpenAI Server (Eval / SFT) - -Uses `server.chat_completion()` with `tools=` parameter. The server (VLLM, SGLang, OpenRouter, OpenAI) handles tool call parsing natively. Returns `ChatCompletion` objects with structured `tool_calls`. - -- **Use for**: evaluation, SFT data generation, benchmarks, testing -- **Placeholder tokens** are created for the Atropos pipeline (since real token IDs aren't available from the OpenAI API) - -### Phase 2: VLLM ManagedServer (Full RL) - -Uses ManagedServer for exact token IDs + logprobs via `/generate`. A client-side [tool call parser](#tool-call-parsers) reconstructs structured `tool_calls` from raw output. - -- **Use for**: full RL training with GRPO/PPO -- **Real tokens**, masks, and logprobs flow through the pipeline -- Set `tool_call_parser` in config to match your model's format (e.g., `"hermes"`, `"qwen"`, `"mistral"`) - -## Creating Environments - -### Training Environment - -```python -from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig -from atroposlib.envs.server_handling.server_manager import APIServerConfig - -class MyEnvConfig(HermesAgentEnvConfig): - my_custom_field: str = "default_value" - -class MyEnv(HermesAgentBaseEnv): - name = "my-env" - env_config_cls = MyEnvConfig - - @classmethod - def config_init(cls): - env_config = MyEnvConfig( - enabled_toolsets=["terminal", "file"], - terminal_backend="modal", - max_agent_turns=30, - ) - server_configs = [APIServerConfig( - base_url="https://openrouter.ai/api/v1", - model_name="anthropic/claude-sonnet-4.6", - server_type="openai", - )] - return env_config, server_configs - - async def setup(self): - from datasets import load_dataset - self.dataset = list(load_dataset("my-dataset", split="train")) - self.iter = 0 - - async def get_next_item(self): - item = self.dataset[self.iter % len(self.dataset)] - self.iter += 1 - return item - - def format_prompt(self, item): - return item["instruction"] - - async def compute_reward(self, item, result, ctx): - # ctx gives full tool access to the rollout's sandbox - test = ctx.terminal("pytest -v") - return 1.0 if test["exit_code"] == 0 else 0.0 - - async def evaluate(self, *args, **kwargs): - # Periodic evaluation during training - pass - -if __name__ == "__main__": - MyEnv.cli() -``` - -### Eval-Only Benchmark - -For benchmarks, follow the pattern used by TerminalBench2, TBLite, and YC-Bench: - -1. **Create under** `environments/benchmarks/your-benchmark/` -2. **Set eval-only config**: `eval_handling=STOP_TRAIN`, `steps_per_eval=1`, `total_steps=1` -3. **Stub training methods**: `collect_trajectories()` returns `(None, [])`, `score()` returns `None` -4. **Implement** `rollout_and_score_eval(eval_item)` — the per-item agent loop + scoring -5. **Implement** `evaluate()` — orchestrates all runs, computes aggregate metrics -6. **Add streaming JSONL** for crash-safe result persistence -7. **Add cleanup**: `KeyboardInterrupt` handling, `cleanup_all_environments()`, `_tool_executor.shutdown()` -8. **Run with** `evaluate` subcommand - -See `environments/benchmarks/yc_bench/yc_bench_env.py` for a clean, well-documented reference implementation. - -## Configuration Reference - -### HermesAgentEnvConfig Fields - -| Field | Type | Default | Description | -|-------|------|---------|-------------| -| `enabled_toolsets` | `List[str]` | `None` (all) | Which hermes toolsets to enable | -| `disabled_toolsets` | `List[str]` | `None` | Toolsets to filter out | -| `distribution` | `str` | `None` | Probabilistic toolset distribution name | -| `max_agent_turns` | `int` | `30` | Max LLM calls per rollout | -| `agent_temperature` | `float` | `1.0` | Sampling temperature | -| `system_prompt` | `str` | `None` | System message for the agent | -| `terminal_backend` | `str` | `"local"` | `local`, `docker`, `modal`, `daytona`, `ssh`, `singularity` | -| `terminal_timeout` | `int` | `120` | Seconds per terminal command | -| `terminal_lifetime` | `int` | `3600` | Max sandbox lifetime | -| `dataset_name` | `str` | `None` | HuggingFace dataset identifier | -| `tool_pool_size` | `int` | `128` | Thread pool size for tool execution | -| `tool_call_parser` | `str` | `"hermes"` | Parser for Phase 2 raw output | -| `extra_body` | `Dict` | `None` | Extra params for OpenAI API (e.g., OpenRouter provider prefs) | -| `eval_handling` | `Enum` | `STOP_TRAIN` | `STOP_TRAIN`, `LIMIT_TRAIN`, `NONE` | - -### YAML Configuration - -Environments can be configured via YAML files passed with `--config`: - -```yaml -env: - enabled_toolsets: ["terminal", "file"] - max_agent_turns: 60 - max_token_length: 32000 - agent_temperature: 0.8 - terminal_backend: "modal" - terminal_timeout: 300 - dataset_name: "NousResearch/terminal-bench-2" - tokenizer_name: "NousResearch/Hermes-3-Llama-3.1-8B" - use_wandb: true - wandb_name: "my-benchmark" - -openai: - base_url: "https://openrouter.ai/api/v1" - model_name: "anthropic/claude-sonnet-4.6" - server_type: "openai" - health_check: false -``` - -YAML values override `config_init()` defaults. CLI arguments override YAML values: - -```bash -python my_env.py evaluate \ - --config my_config.yaml \ - --openai.model_name anthropic/claude-opus-4.6 # overrides YAML -``` - -## Prerequisites - -### For all environments - -- Python >= 3.11 -- `atroposlib`: `pip install git+https://github.com/NousResearch/atropos.git` -- An LLM API key (OpenRouter, OpenAI, or self-hosted VLLM/SGLang) - -### For Modal-sandboxed benchmarks (TB2, TBLite) - -- [Modal](https://modal.com) account and CLI: `pip install "hermes-agent[modal]"` -- `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` environment variables - -### For YC-Bench - -- `pip install "hermes-agent[yc-bench]"` (installs the yc-bench CLI + SQLAlchemy) -- No Modal needed — runs with local terminal backend - -### For RL training - -- `TINKER_API_KEY` — API key for the [Tinker](https://tinker.computer) training service -- `WANDB_API_KEY` — for Weights & Biases metrics tracking -- The `tinker-atropos` submodule (at `tinker-atropos/` in the repo) - -See [RL Training](/user-guide/features/rl-training) for the agent-driven RL workflow. - -## Directory Structure - -``` -environments/ -├── hermes_base_env.py # Abstract base class (HermesAgentBaseEnv) -├── agent_loop.py # Multi-turn agent engine (HermesAgentLoop) -├── tool_context.py # Per-rollout tool access for reward functions -├── patches.py # Async-safety patches for Modal backend -│ -├── tool_call_parsers/ # Phase 2 client-side parsers -│ ├── hermes_parser.py # Hermes/ChatML format -│ ├── mistral_parser.py # Mistral [TOOL_CALLS] format -│ ├── llama_parser.py # Llama 3 JSON tool calling -│ ├── qwen_parser.py # Qwen format -│ ├── deepseek_v3_parser.py # DeepSeek V3 format -│ └── ... # + kimi_k2, longcat, glm45/47, etc. -│ -├── terminal_test_env/ # Stack validation (inline tasks) -├── hermes_swe_env/ # SWE-bench training environment -│ -└── benchmarks/ # Evaluation benchmarks - ├── terminalbench_2/ # 89 terminal tasks, Modal sandboxes - ├── tblite/ # 100 calibrated tasks (fast TB2 proxy) - └── yc_bench/ # Long-horizon strategic benchmark -``` diff --git a/website/docs/getting-started/updating.md b/website/docs/getting-started/updating.md index 55df5a7f640..aa2a426db99 100644 --- a/website/docs/getting-started/updating.md +++ b/website/docs/getting-started/updating.md @@ -123,13 +123,11 @@ If you installed manually (not via the quick installer): cd /path/to/hermes-agent export VIRTUAL_ENV="$(pwd)/venv" -# Pull latest code and submodules +# Pull latest code git pull origin main -git submodule update --init --recursive # Reinstall (picks up new dependencies) uv pip install -e ".[all]" -uv pip install -e "./tinker-atropos" # Check for new config options hermes config check diff --git a/website/docs/integrations/index.md b/website/docs/integrations/index.md index 21235a12ba1..d80a61abd8c 100644 --- a/website/docs/integrations/index.md +++ b/website/docs/integrations/index.md @@ -97,5 +97,4 @@ See the [Messaging Gateway overview](/docs/user-guide/messaging) for the platfor ## Training & Evaluation -- **[RL Training](/docs/user-guide/features/rl-training)** — Generate trajectory data from agent sessions for reinforcement learning and model fine-tuning. Supports Atropos environments with customizable reward functions. - **[Batch Processing](/docs/user-guide/features/batch-processing)** — Run the agent across hundreds of prompts in parallel, generating structured ShareGPT-format trajectory data for training data generation or evaluation. diff --git a/website/docs/integrations/providers.md b/website/docs/integrations/providers.md index b53ab15ed84..af9e07814d7 100644 --- a/website/docs/integrations/providers.md +++ b/website/docs/integrations/providers.md @@ -1355,7 +1355,6 @@ You can switch between providers at any time with `hermes model` — no restart | Premium TTS voices | [ElevenLabs](https://elevenlabs.io/) | `ELEVENLABS_API_KEY` | | OpenAI TTS + voice transcription | [OpenAI](https://platform.openai.com/api-keys) | `VOICE_TOOLS_OPENAI_KEY` | | Mistral TTS + voice transcription | [Mistral](https://console.mistral.ai/) | `MISTRAL_API_KEY` | -| RL Training | [Tinker](https://tinker-console.thinkingmachines.ai/) + [WandB](https://wandb.ai/) | `TINKER_API_KEY`, `WANDB_API_KEY` | | Cross-session user modeling | [Honcho](https://honcho.dev/) | `HONCHO_API_KEY` | | Semantic long-term memory | [Supermemory](https://supermemory.ai) | `SUPERMEMORY_API_KEY` | diff --git a/website/docs/reference/environment-variables.md b/website/docs/reference/environment-variables.md index 4b581877849..93107fba147 100644 --- a/website/docs/reference/environment-variables.md +++ b/website/docs/reference/environment-variables.md @@ -148,8 +148,6 @@ For native Anthropic auth, Hermes prefers Claude Code's own credential files whe | `HONCHO_BASE_URL` | Base URL for self-hosted Honcho instances (default: Honcho cloud). No API key required for local instances | | `HINDSIGHT_TIMEOUT` | Timeout in seconds for Hindsight memory-provider API calls (default: `60`). Bump this if your Hindsight instance is slow to respond during `/sync` or `on_session_switch` and you're seeing timeouts in `errors.log`. | | `SUPERMEMORY_API_KEY` | Semantic long-term memory with profile recall and session ingest ([supermemory.ai](https://supermemory.ai)) | -| `TINKER_API_KEY` | RL training ([tinker-console.thinkingmachines.ai](https://tinker-console.thinkingmachines.ai/)) | -| `WANDB_API_KEY` | RL training metrics ([wandb.ai](https://wandb.ai/)) | | `DAYTONA_API_KEY` | Daytona cloud sandboxes ([daytona.io](https://daytona.io/)) | | `VERCEL_TOKEN` | Vercel Sandbox access token ([vercel.com](https://vercel.com/)) | | `VERCEL_PROJECT_ID` | Vercel project ID (required with `VERCEL_TOKEN`) | diff --git a/website/docs/reference/optional-skills-catalog.md b/website/docs/reference/optional-skills-catalog.md index 40f9c5539c8..8c4c2f36432 100644 --- a/website/docs/reference/optional-skills-catalog.md +++ b/website/docs/reference/optional-skills-catalog.md @@ -120,7 +120,6 @@ hermes skills uninstall | [**faiss**](/docs/user-guide/skills/optional/mlops/mlops-faiss) | Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or whe... | | [**optimizing-attention-flash**](/docs/user-guide/skills/optional/mlops/mlops-flash-attention) | Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster in... | | [**guidance**](/docs/user-guide/skills/optional/mlops/mlops-guidance) | Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework | -| [**hermes-atropos-environments**](/docs/user-guide/skills/optional/mlops/mlops-hermes-atropos-environments) | Build, test, and debug Hermes Agent RL environments for Atropos training. Covers the HermesAgentBaseEnv interface, reward functions, agent loop integration, evaluation with tools, wandb logging, and the three CLI modes (serve/process/eva... | | [**huggingface-tokenizers**](/docs/user-guide/skills/optional/mlops/mlops-huggingface-tokenizers) | Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integ... | | [**instructor**](/docs/user-guide/skills/optional/mlops/mlops-instructor) | Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library | | [**lambda-labs-gpu-cloud**](/docs/user-guide/skills/optional/mlops/mlops-lambda-labs) | Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training. | diff --git a/website/docs/reference/tools-reference.md b/website/docs/reference/tools-reference.md index 5d0100de79d..03930264f8c 100644 --- a/website/docs/reference/tools-reference.md +++ b/website/docs/reference/tools-reference.md @@ -148,21 +148,6 @@ Registered only when the agent is spawned by the kanban dispatcher (`HERMES_KANB |------|-------------|----------------------| | `mixture_of_agents` | Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced alg… | OPENROUTER_API_KEY | -## `rl` toolset - -| Tool | Description | Requires environment | -|------|-------------|----------------------| -| `rl_check_status` | Get status and metrics for a training run. RATE LIMITED: enforces 30-minute minimum between checks for the same run. Returns WandB metrics: step, state, reward_mean, loss, percent_correct. | TINKER_API_KEY, WANDB_API_KEY | -| `rl_edit_config` | Update a configuration field. Use rl_get_current_config() first to see all available fields for the selected environment. Each environment has different configurable options. Infrastructure settings (tokenizer, URLs, lora_rank, learning_ra… | TINKER_API_KEY, WANDB_API_KEY | -| `rl_get_current_config` | Get the current environment configuration. Returns only fields that can be modified: group_size, max_token_length, total_steps, steps_per_eval, use_wandb, wandb_name, max_num_workers. | TINKER_API_KEY, WANDB_API_KEY | -| `rl_get_results` | Get final results and metrics for a completed training run. Returns final metrics and path to trained weights. | TINKER_API_KEY, WANDB_API_KEY | -| `rl_list_environments` | List all available RL environments. Returns environment names, paths, and descriptions. TIP: Read the file_path with file tools to understand how each environment works (verifiers, data loading, rewards). | TINKER_API_KEY, WANDB_API_KEY | -| `rl_list_runs` | List all training runs (active and completed) with their status. | TINKER_API_KEY, WANDB_API_KEY | -| `rl_select_environment` | Select an RL environment for training. Loads the environment's default configuration. After selecting, use rl_get_current_config() to see settings and rl_edit_config() to modify them. | TINKER_API_KEY, WANDB_API_KEY | -| `rl_start_training` | Start a new RL training run with the current environment and config. Most training parameters (lora_rank, learning_rate, etc.) are fixed. Use rl_edit_config() to set group_size, batch_size, wandb_project before starting. WARNING: Training… | TINKER_API_KEY, WANDB_API_KEY | -| `rl_stop_training` | Stop a running training job. Use if metrics look bad, training is stagnant, or you want to try different settings. | TINKER_API_KEY, WANDB_API_KEY | -| `rl_test_inference` | Quick inference test for any environment. Runs a few steps of inference + scoring using OpenRouter. Default: 3 steps x 16 completions = 48 rollouts per model, testing 3 models = 144 total. Tests environment loading, prompt construction, in… | TINKER_API_KEY, WANDB_API_KEY | - ## `session_search` toolset | Tool | Description | Requires environment | diff --git a/website/docs/reference/toolsets-reference.md b/website/docs/reference/toolsets-reference.md index ce11d86cb41..5bf1f14260e 100644 --- a/website/docs/reference/toolsets-reference.md +++ b/website/docs/reference/toolsets-reference.md @@ -45,7 +45,7 @@ Or in-session: ``` /tools list /tools disable browser -/tools enable rl +/tools enable homeassistant ``` ## Core Toolsets @@ -71,7 +71,6 @@ Or in-session: | `memory` | `memory` | Persistent cross-session memory management. | | `messaging` | `send_message` | Send messages to other platforms (Telegram, Discord, etc.) from within a session. | | `moa` | `mixture_of_agents` | Multi-model consensus via Mixture of Agents. | -| `rl` | `rl_check_status`, `rl_edit_config`, `rl_get_current_config`, `rl_get_results`, `rl_list_environments`, `rl_list_runs`, `rl_select_environment`, `rl_start_training`, `rl_stop_training`, `rl_test_inference` | RL training environment management (Atropos). | | `safe` | `image_generate`, `vision_analyze`, `web_extract`, `web_search` (via `includes`) | Read-only research + media generation. No file writes, no terminal, no code execution. | | `search` | `web_search` | Web search only (without extract). | | `session_search` | `session_search` | Search past conversation sessions. | diff --git a/website/docs/user-guide/features/rl-training.md b/website/docs/user-guide/features/rl-training.md deleted file mode 100644 index 81fc6539b37..00000000000 --- a/website/docs/user-guide/features/rl-training.md +++ /dev/null @@ -1,234 +0,0 @@ ---- -sidebar_position: 13 -title: "RL Training" -description: "Reinforcement learning on agent behaviors with Tinker-Atropos — environment discovery, training, and evaluation" ---- - -# RL Training - -Hermes Agent includes an integrated RL (Reinforcement Learning) training pipeline built on **Tinker-Atropos**. This enables training language models on environment-specific tasks using GRPO (Group Relative Policy Optimization) with LoRA adapters, orchestrated entirely through the agent's tool interface. - -## Overview - -The RL training system consists of three components: - -1. **[Atropos](https://github.com/NousResearch/atropos)** — A trajectory API server that coordinates environment interactions, manages rollout groups, and computes advantages -2. **[Tinker](https://thinkingmachines.ai/tinker/)** — A training service that handles model weights, LoRA training, sampling/inference, and optimizer steps -3. **Environments** — Python classes that define tasks, scoring, and reward functions (e.g., GSM8K math problems) - -The agent can discover environments, configure training parameters, launch training runs, and monitor metrics — all through a set of `rl_*` tools. - -## Requirements - -RL training requires: - -- **Python >= 3.11** (Tinker package requirement) -- **TINKER_API_KEY** — API key for the Tinker training service -- **WANDB_API_KEY** — API key for [Weights & Biases](https://wandb.ai/) metrics tracking -- The `tinker-atropos` submodule (at `tinker-atropos/` relative to the Hermes root) - -```bash -# Set up API keys -hermes config set TINKER_API_KEY your-tinker-key -hermes config set WANDB_API_KEY your-wandb-key -``` - -When both keys are present and Python >= 3.11 is available, the `rl` toolset is automatically enabled. - -## Available Tools - -| Tool | Description | -|------|-------------| -| `rl_list_environments` | Discover available RL environments | -| `rl_select_environment` | Select an environment and load its config | -| `rl_get_current_config` | View configurable and locked fields | -| `rl_edit_config` | Modify configurable training parameters | -| `rl_start_training` | Launch a training run (spawns 3 processes) | -| `rl_check_status` | Monitor training progress and WandB metrics | -| `rl_stop_training` | Stop a running training job | -| `rl_get_results` | Get final metrics and model weights path | -| `rl_list_runs` | List all active and completed runs | -| `rl_test_inference` | Quick inference test using OpenRouter | - -## Workflow - -### 1. Discover Environments - -``` -List the available RL environments -``` - -The agent calls `rl_list_environments()` which scans `tinker-atropos/tinker_atropos/environments/` using AST parsing to find Python classes inheriting from `BaseEnv`. Each environment defines: - -- **Dataset loading** — where training data comes from (e.g., HuggingFace datasets) -- **Prompt construction** — how to format items for the model -- **Scoring/verification** — how to evaluate model outputs and assign rewards - -### 2. Select and Configure - -``` -Select the GSM8K environment and show me the configuration -``` - -The agent calls `rl_select_environment("gsm8k_tinker")`, then `rl_get_current_config()` to see all parameters. - -Configuration fields are divided into two categories: - -**Configurable fields** (can be modified): -- `group_size` — Number of completions per item (default: 16) -- `batch_size` — Training batch size (default: 128) -- `wandb_name` — WandB run name (auto-set to `{env}-{timestamp}`) -- Other environment-specific parameters - -**Locked fields** (infrastructure settings, cannot be changed): -- `tokenizer_name` — Model tokenizer (e.g., `Qwen/Qwen3-8B`) -- `rollout_server_url` — Atropos API URL (`http://localhost:8000`) -- `max_token_length` — Maximum token length (8192) -- `max_num_workers` — Maximum parallel workers (2048) -- `total_steps` — Total training steps (2500) -- `lora_rank` — LoRA adapter rank (32) -- `learning_rate` — Learning rate (4e-5) -- `max_token_trainer_length` — Max tokens for trainer (9000) - -### 3. Start Training - -``` -Start the training run -``` - -The agent calls `rl_start_training()` which: - -1. Generates a YAML config file merging locked settings with configurable overrides -2. Creates a unique run ID -3. Spawns three processes: - - **Atropos API server** (`run-api`) — trajectory coordination - - **Tinker trainer** (`launch_training.py`) — LoRA training + FastAPI inference server on port 8001 - - **Environment** (`environment.py serve`) — the selected environment connecting to Atropos - -The processes start with staggered delays (5s for API, 30s for trainer, 90s more for environment) to ensure proper initialization order. - -### 4. Monitor Progress - -``` -Check the status of training run abc12345 -``` - -The agent calls `rl_check_status(run_id)` which reports: - -- Process status (running/exited for each of the 3 processes) -- Running time -- WandB metrics (step, reward mean, percent correct, eval accuracy) -- Log file locations for debugging - -:::note Rate Limiting -Status checks are rate-limited to once every **30 minutes** per run ID. This prevents excessive polling during long-running training jobs that take hours. -::: - -### 5. Stop or Get Results - -``` -Stop the training run -# or -Get the final results for run abc12345 -``` - -`rl_stop_training()` terminates all three processes in reverse order (environment → trainer → API). `rl_get_results()` retrieves final WandB metrics and training history. - -## Inference Testing - -Before committing to a full training run, you can test if an environment works correctly using `rl_test_inference`. This runs a few steps of inference and scoring using OpenRouter — no Tinker API needed, just an `OPENROUTER_API_KEY`. - -``` -Test the selected environment with inference -``` - -Default configuration: -- **3 steps × 16 completions = 48 rollouts per model** -- Tests 3 models at different scales for robustness: - - `qwen/qwen3-8b` (small) - - `z-ai/glm-4.7-flash` (medium) - - `minimax/minimax-m2.7` (large) -- Total: ~144 rollouts - -This validates: -- Environment loads correctly -- Prompt construction works -- Inference response parsing is robust across model scales -- Verifier/scoring logic produces valid rewards - -## Tinker API Integration - -The trainer uses the [Tinker](https://tinker.computer) API for model training operations: - -- **ServiceClient** — Creates training and sampling clients -- **Training client** — Handles forward-backward passes with importance sampling loss, optimizer steps (Adam), and weight checkpointing -- **Sampling client** — Provides inference using the latest trained weights - -The training loop: -1. Fetches a batch of rollouts from Atropos (prompt + completions + scores) -2. Converts to Tinker Datum objects with padded logprobs and advantages -3. Runs forward-backward pass with importance sampling loss -4. Takes an optimizer step (Adam: lr=4e-5, β1=0.9, β2=0.95) -5. Saves weights and creates a new sampling client for next-step inference -6. Logs metrics to WandB - -## Architecture Diagram - -```mermaid -flowchart LR - api["Atropos API
run-api
port 8000"] - env["Environment
BaseEnv implementation"] - infer["OpenAI / sglang
inference API
port 8001"] - trainer["Tinker Trainer
LoRA training + FastAPI"] - - env <--> api - env --> infer - api -->|"batches: tokens, scores, logprobs"| trainer - trainer -->|"serves inference"| infer -``` - -## Creating Custom Environments - -To create a new RL environment: - -1. Create a Python file in `tinker-atropos/tinker_atropos/environments/` -2. Define a class that inherits from `BaseEnv` -3. Implement the required methods: - - `load_dataset()` — Load your training data - - `get_next_item()` — Provide the next item to the model - - `score_answer()` — Score model outputs and assign rewards - - `collect_trajectories()` — Collect and return trajectories -4. Optionally define a custom config class inheriting from `BaseEnvConfig` - -Study the existing `gsm8k_tinker.py` as a template. The agent can help you create new environments — it can read existing environment files, inspect HuggingFace datasets, and write new environment code. - -## WandB Metrics - -Training runs log to Weights & Biases with these key metrics: - -| Metric | Description | -|--------|-------------| -| `train/loss` | Training loss (importance sampling) | -| `train/learning_rate` | Current learning rate | -| `reward/mean` | Mean reward across groups | -| `logprobs/mean` | Mean reference logprobs | -| `logprobs/mean_training` | Mean training logprobs | -| `logprobs/diff` | Logprob drift (reference - training) | -| `advantages/mean` | Mean advantage values | -| `advantages/std` | Advantage standard deviation | - -## Log Files - -Each training run generates log files in `~/.hermes/logs/rl_training/`: - -``` -logs/ -├── api_{run_id}.log # Atropos API server logs -├── trainer_{run_id}.log # Tinker trainer logs -├── env_{run_id}.log # Environment process logs -└── inference_tests/ # Inference test results - ├── test_{env}_{model}.jsonl - └── test_{env}_{model}.log -``` - -These are invaluable for debugging when training fails or produces unexpected results. diff --git a/website/docs/user-guide/skills/optional/mlops/mlops-hermes-atropos-environments.md b/website/docs/user-guide/skills/optional/mlops/mlops-hermes-atropos-environments.md deleted file mode 100644 index 7cce92a7e0e..00000000000 --- a/website/docs/user-guide/skills/optional/mlops/mlops-hermes-atropos-environments.md +++ /dev/null @@ -1,323 +0,0 @@ ---- -title: "Hermes Atropos Environments — Build, test, and debug Hermes Agent RL environments for Atropos training" -sidebar_label: "Hermes Atropos Environments" -description: "Build, test, and debug Hermes Agent RL environments for Atropos training" ---- - -{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */} - -# Hermes Atropos Environments - -Build, test, and debug Hermes Agent RL environments for Atropos training. Covers the HermesAgentBaseEnv interface, reward functions, agent loop integration, evaluation with tools, wandb logging, and the three CLI modes (serve/process/evaluate). Use when creating, reviewing, or fixing RL environments in the hermes-agent repo. - -## Skill metadata - -| | | -|---|---| -| Source | Optional — install with `hermes skills install official/mlops/hermes-atropos-environments` | -| Path | `optional-skills/mlops/hermes-atropos-environments` | -| Version | `1.1.0` | -| Author | Hermes Agent | -| License | MIT | -| Platforms | linux, macos, windows | -| Tags | `atropos`, `rl`, `environments`, `training`, `reinforcement-learning`, `reward-functions` | -| Related skills | [`axolotl`](/docs/user-guide/skills/optional/mlops/mlops-training-axolotl), [`fine-tuning-with-trl`](/docs/user-guide/skills/optional/mlops/mlops-training-trl-fine-tuning), `lm-evaluation-harness` | - -## Reference: full SKILL.md - -:::info -The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active. -::: - -# Hermes Agent Atropos Environments - -Guide for building RL environments in the hermes-agent repo that integrate with the Atropos training framework. - -## Architecture Overview - - -``` -Atropos BaseEnv (atroposlib/envs/base.py) - └── HermesAgentBaseEnv (environments/hermes_base_env.py) - ├── Handles agent loop orchestration - ├── Handles tool resolution per group - ├── Handles ToolContext for reward verification - └── YOUR ENVIRONMENT (environments/your_env.py) - Only implements: setup, get_next_item, format_prompt, - compute_reward, evaluate, wandb_log -``` - - -Hermes environments are special because they run a **multi-turn agent loop with tool calling** — not just single-turn completions. The base env handles the loop; you implement the task and scoring. - -## File Locations - -| File | Purpose | -|------|---------| -| `environments/hermes_base_env.py` | Base class with agent loop + tool resolution | -| `environments/agent_loop.py` | `HermesAgentLoop` + `AgentResult` dataclass | -| `environments/tool_context.py` | `ToolContext` for reward verification | -| `environments/tool_call_parsers.py` | Phase 2 tool call parsers (hermes, mistral, etc.) | -| `environments/your_env.py` | Your environment implementation | - -## Inference Setup — Ask the User First - -**IMPORTANT:** Before running any test, evaluation, or data generation command, always ask the user how they want to handle inference. Do NOT assume OpenRouter or any specific endpoint. Present these options: - -1. **OpenRouter** — Ask which model they want to use (e.g., `anthropic/claude-sonnet-4.5`, `google/gemini-2.5-pro`, `meta-llama/llama-3.3-70b-instruct`, etc.). Requires `OPENROUTER_API_KEY` in environment. -2. **Self-hosted VLLM endpoint** — Ask for their base URL (e.g., `http://localhost:8000/v1`) and model name. Set `--openai.server_type vllm`. -3. **Other OpenAI-compatible API** — Ask for the base URL, model name, and any required API key. Set `--openai.server_type openai` and `--openai.health_check false`. -4. **Local Atropos training server** — For `serve` mode with a live training loop. Default `http://localhost:8000/v1`. - -Once the user tells you their setup, use those values in all CLI commands for that session. Example prompts: - -> "Before I run this, how would you like to handle inference? -> 1. OpenRouter (I'll need your preferred model, e.g. claude-sonnet-4.5) -> 2. A self-hosted VLLM endpoint (give me the URL and model name) -> 3. Another OpenAI-compatible API (give me the URL, model, and any auth details) -> 4. Local Atropos training server (serve mode)" - -### Key flags by provider: - -| Provider | `--openai.server_type` | `--openai.health_check` | `--openai.api_key` | -|----------|----------------------|------------------------|-------------------| -| OpenRouter | `openai` | `false` | `$OPENROUTER_API_KEY` | -| VLLM (self-hosted) | `vllm` | (default) | (not needed) | -| Other OpenAI-compatible | `openai` | `false` | As needed | -| Local Atropos | (default) | (default) | (not needed) | - -## Required Methods - -### 1. `setup()` — Load dataset and initialize state - -```python -async def setup(self) -> None: - """Called once at startup. Load datasets, initialize state.""" - # Try HuggingFace first, fallback to built-in samples - try: - from datasets import load_dataset - ds = load_dataset("your/dataset", split="test") - self._items = [...] - except Exception: - self._items = BUILTIN_SAMPLES - - # Always split into train/eval - random.shuffle(self._items) - eval_size = max(20, int(len(self._items) * 0.1)) - self._eval_items = self._items[:eval_size] - self._items = self._items[eval_size:] -``` - -### 2. `get_next_item()` — Return next training item - -```python -async def get_next_item(self) -> dict: - """Return next item, cycling through dataset.""" - item = self._items[self._index % len(self._items)] - self._index += 1 - return item -``` - -### 3. `format_prompt(item)` — Convert item to user message - -```python -def format_prompt(self, item: dict) -> str: - """Convert a dataset item into the user-facing prompt.""" - return f"Research this question: {item['question']}" -``` - -### 4. `compute_reward(item, result, ctx)` — Score the rollout - -**CRITICAL**: `result` is an `AgentResult`, NOT a dict. It has these attributes: -- `result.messages` — List of message dicts (OpenAI format) -- `result.turns_used` — Number of LLM calls made -- `result.finished_naturally` — True if model stopped voluntarily -- `result.tool_errors` — List of ToolError objects - -**AgentResult does NOT have**: `final_response`, `tool_calls`, `tools_used`. -You must extract these from `result.messages`: - -```python -async def compute_reward(self, item, result: AgentResult, ctx: ToolContext) -> float: - # Extract final response (last assistant message with content) - final_response = "" - tools_used = [] - for msg in reversed(result.messages): - if msg.get("role") == "assistant" and msg.get("content") and not final_response: - final_response = msg["content"] - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - fn = tc.get("function", {}) if isinstance(tc, dict) else {} - name = fn.get("name", "") - if name: - tools_used.append(name) - - # Score using LLM judge, heuristic, or ToolContext verification - correctness = await self._llm_judge(item, final_response) - return correctness -``` - -`ctx` (ToolContext) gives you terminal/file access to the agent's sandbox for verification: -```python -# Run tests in the agent's sandbox -result = ctx.terminal("pytest /workspace/test.py") -return 1.0 if result["exit_code"] == 0 else 0.0 -``` - -### 5. `evaluate()` — Periodic evaluation with full agent loop - -**MUST use the full agent loop with tools**, not single-turn chat_completion. -The whole point of hermes-agent environments is agentic evaluation: - -```python -async def evaluate(self, *args, **kwargs) -> None: - import time, uuid - from environments.agent_loop import HermesAgentLoop - from environments.tool_context import ToolContext - - start_time = time.time() - tools, valid_names = self._resolve_tools_for_group() - samples = [] - - for item in self._eval_items[:self.config.eval_size]: - task_id = str(uuid.uuid4()) - messages = [] - if self.config.system_prompt: - messages.append({"role": "system", "content": self.config.system_prompt}) - messages.append({"role": "user", "content": self.format_prompt(item)}) - - agent = HermesAgentLoop( - server=self.server, - tool_schemas=tools, - valid_tool_names=valid_names, - max_turns=self.config.max_agent_turns, - task_id=task_id, - temperature=0.0, # Deterministic for eval - max_tokens=self.config.max_token_length, - extra_body=self.config.extra_body, - ) - result = await agent.run(messages) - - ctx = ToolContext(task_id) - try: - reward = await self.compute_reward(item, result, ctx) - finally: - ctx.cleanup() - - samples.append({"prompt": ..., "response": ..., "reward": reward}) - - eval_metrics = {"eval/mean_reward": ...} - await self.evaluate_log(metrics=eval_metrics, samples=samples, - start_time=start_time, end_time=time.time()) -``` - -### 6. `wandb_log()` — Custom metrics logging - -Always call `super().wandb_log()` at the end: - -```python -async def wandb_log(self, wandb_metrics=None): - if wandb_metrics is None: - wandb_metrics = {} - if self._reward_buffer: - n = len(self._reward_buffer) - wandb_metrics["train/mean_reward"] = sum(self._reward_buffer) / n - self._reward_buffer.clear() - await super().wandb_log(wandb_metrics) # MUST call super -``` - -**Pitfall**: `compute_reward` appends to metric buffers. During eval, this pollutes training metrics. Roll back buffer entries added during eval. - -## Config Class - -Always create a custom config subclass with Pydantic Field descriptors. Key inherited fields you can tune: `enabled_toolsets`, `max_agent_turns`, `agent_temperature`, `system_prompt`, `terminal_backend`, `group_size`, `steps_per_eval`, `total_steps`. - -## config_init() — Default Configuration - -Classmethod returning `(YourEnvConfig, [APIServerConfig(...)])`. Set server_type to "openai" for OpenRouter/external APIs. Load API key from environment variable. - -## Three CLI Modes - -```bash -# SERVE — Full training loop (connects to Atropos API server) -python environments/my_env.py serve --openai.base_url http://localhost:8000/v1 - -# PROCESS — Offline data generation (saves JSONL) -python environments/my_env.py process --env.total_steps 10 --env.group_size 1 \ - --env.use_wandb false --env.data_path_to_save_groups output.jsonl \ - --openai.base_url "" \ - --openai.model_name "" \ - --openai.server_type --openai.health_check false - -# EVALUATE — Standalone eval (runs setup + evaluate only) -python environments/my_env.py evaluate --env.eval_size 20 \ - --env.data_dir_to_save_evals /tmp/eval_results \ - --openai.base_url "" \ - --openai.model_name "" \ - --openai.server_type --openai.health_check false -``` - -Config priority: CLI args > YAML file > config_init() defaults. - -## Common Pitfalls - -1. **AgentResult has .messages, not .final_response** — Extract the final response by iterating reversed(result.messages) looking for the last assistant message with content. - -2. **evaluate() must use HermesAgentLoop, not chat_completion** — Single-turn chat_completion has no tools. The whole point of hermes-agent benchmarks is agentic evaluation with tool use. - -3. **Don't call _llm_judge twice** — If compute_reward already calls it, extract the score from the buffer instead of calling judge separately in evaluate(). - -4. **Eval pollutes training buffers** — compute_reward appends to metric buffers. During eval, roll back buffer entries to keep training metrics clean. - -5. **Always set health_check=false for OpenRouter** — OpenRouter has no /health endpoint. - -6. **Set data_dir_to_save_evals in evaluate mode** — Without it, results aren't saved. - -7. **default_toolsets class variable vs enabled_toolsets config** — The class variable is a hint; the config field is what actually controls tool resolution. - -8. **Tool call parsing in messages** — Tool calls are dicts with `{"function": {"name": ..., "arguments": ...}}`. Always check `isinstance(tc, dict)`. - -9. **ToolContext.cleanup()** — Always call in a finally block to release sandbox resources. - -10. **server_type must be "openai" for external APIs** — Without it, Atropos assumes a local VLLM server. - -11. **Always ask the user for their inference setup** — Never hardcode or assume a specific provider/model. See the "Inference Setup" section above. - -## Reward Function Patterns - -### LLM Judge (for open-ended tasks) -Use `self.server.chat_completion()` with a scoring prompt. Parse JSON response for score float. Always include a heuristic fallback (keyword overlap) for when the judge call fails. - -### Binary Verification (for code/terminal tasks) -Use `ctx.terminal("pytest test.py -q")` to run tests in the agent's sandbox. Return 1.0 for pass, 0.0 for fail. - -### Multi-Signal (combine multiple indicators) -Weight correctness (0.6) + tool usage (0.2) + efficiency (0.2) + optional bonuses. Clamp to [0, 1]. - -## Testing Your Environment - -1. **Import test**: `python -c "from environments.my_env import MyEnv; print('OK')"` -2. **Ask the user for inference setup** (see "Inference Setup" section above) -3. **Process mode** (1 item): Verify JSONL output has valid tokens, masks, scores -4. **Evaluate mode**: Verify full agent loop runs with tools, metrics logged correctly -5. **Check reward range**: Scores should be in [0, 1], not all identical - -## Minimum Implementation Checklist - -```python -class MyEnv(HermesAgentBaseEnv): - name = "my-env" - env_config_cls = MyEnvConfig - - @classmethod - def config_init(cls): ... # Default server + env config - async def setup(self): ... # Load dataset + train/eval split - async def get_next_item(self): ... # Cycle through training items - def format_prompt(self, item): ... # Item → user message string - async def compute_reward(self, item, result, ctx): ... # Score rollout - async def evaluate(self, *args, **kwargs): ... # Full agent loop eval - async def wandb_log(self, metrics=None): ... # Custom metrics + super() - -if __name__ == "__main__": - MyEnv.cli() -``` diff --git a/website/sidebars.ts b/website/sidebars.ts index 37557df8d11..a2977c87eef 100644 --- a/website/sidebars.ts +++ b/website/sidebars.ts @@ -103,7 +103,6 @@ const sidebars: SidebarsConfig = { type: 'category', label: 'Advanced', items: [ - 'user-guide/features/rl-training', 'user-guide/features/spotify', ], }, @@ -238,7 +237,6 @@ const sidebars: SidebarsConfig = { 'developer-guide/tools-runtime', 'developer-guide/acp-internals', 'developer-guide/cron-internals', - 'developer-guide/environments', 'developer-guide/trajectory-format', ], },