refactor: move standalone scripts to scripts/ directory

Move batch_runner, trajectory_compressor, mini_swe_runner, and rl_cli
from the project root into scripts/, update all imports, logger names,
pyproject.toml, and downstream test references.

scripts/mini_swe_runner.py

@@ -0,0 +1,739 @@
+#!/usr/bin/env python3
+"""
+SWE Runner with Hermes Trajectory Format
+
+A runner that uses Hermes-Agent's built-in execution environments
+(local, docker, modal) and outputs trajectories in the Hermes-Agent format
+compatible with batch_runner.py and trajectory_compressor.py.
+
+Features:
+- Uses Hermes-Agent's Docker, Modal, or Local environments for command execution
+- Outputs trajectories in Hermes format (from/value pairs with <tool_call>/<tool_response> XML)
+- Compatible with the trajectory compression pipeline
+- Supports batch processing from JSONL prompt files
+
+Usage:
+    # Run a single task with local environment
+    python mini_swe_runner.py --task "Create a hello world Python script" --env local
+    
+    # Run with Docker
+    python mini_swe_runner.py --task "List files in /tmp" --env docker --image python:3.11-slim
+    
+    # Run with Modal (cloud)
+    python mini_swe_runner.py --task "Install numpy and test it" --env modal --image python:3.11-slim
+    
+    # Batch mode from JSONL file
+    python mini_swe_runner.py --prompts_file prompts.jsonl --output_file trajectories.jsonl --env docker
+"""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import json
+import logging
+import time
+import uuid
+from datetime import datetime
+from pathlib import Path
+from typing import List, Dict, Any, Optional, Literal
+
+import fire
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+
+def _effective_temperature_for_model(
+    model: str,
+    base_url: Optional[str] = None,
+) -> Optional[float]:
+    """Return a fixed temperature for models with strict sampling contracts.
+
+    Returns ``None`` when the model manages temperature server-side (Kimi);
+    callers must omit the ``temperature`` kwarg entirely in that case.
+    """
+    try:
+        from agent.auxiliary_client import _fixed_temperature_for_model, OMIT_TEMPERATURE
+    except Exception:
+        return None
+    result = _fixed_temperature_for_model(model, base_url)
+    if result is OMIT_TEMPERATURE:
+        return None  # caller must omit temperature
+    return result
+
+
+
+
+# ============================================================================
+# Terminal Tool Definition (matches Hermes-Agent format)
+# ============================================================================
+
+TERMINAL_TOOL_DEFINITION = {
+    "type": "function",
+    "function": {
+        "name": "terminal",
+        "description": """Execute bash commands in a sandboxed environment.
+
+**Environment:**
+- Isolated execution environment (local, Docker, or Modal cloud)
+- Filesystem persists between tool calls within the same task
+- Internet access available
+
+**Command Execution:**
+- Provide the command to execute via the 'command' parameter
+- Optional 'timeout' parameter in seconds (default: 60)
+
+**Examples:**
+- Run command: `{"command": "ls -la"}`
+- With timeout: `{"command": "long_task.sh", "timeout": 300}`
+
+**Best Practices:**
+- Use non-interactive commands (avoid vim, nano, interactive python)
+- Pipe to cat if output might be large
+- Install tools with apt-get or pip as needed
+
+**Completion:**
+- When task is complete, output: echo "MINI_SWE_AGENT_FINAL_OUTPUT" followed by your result
+""",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "command": {
+                    "type": "string",
+                    "description": "The bash command to execute"
+                },
+                "timeout": {
+                    "type": "integer",
+                    "description": "Command timeout in seconds (default: 60)"
+                }
+            },
+            "required": ["command"]
+        }
+    }
+}
+
+
+# ============================================================================
+# Environment Factory
+# ============================================================================
+
+def create_environment(
+    env_type: str = "local",
+    image: str = "python:3.11-slim",
+    cwd: str = "/tmp",
+    timeout: int = 60,
+    **kwargs
+):
+    """
+    Create an execution environment using Hermes-Agent's built-in backends.
+    
+    Args:
+        env_type: One of "local", "docker", "modal"
+        image: Docker/Modal image name (ignored for local)
+        cwd: Working directory
+        timeout: Default command timeout
+        **kwargs: Additional environment-specific options
+        
+    Returns:
+        Environment instance with execute() and cleanup() methods
+    """
+    if env_type == "local":
+        from tools.environments.local import LocalEnvironment
+        return LocalEnvironment(cwd=cwd, timeout=timeout)
+    
+    elif env_type == "docker":
+        from tools.environments.docker import DockerEnvironment
+        return DockerEnvironment(image=image, cwd=cwd, timeout=timeout, **kwargs)
+    
+    elif env_type == "modal":
+        from tools.environments.modal import ModalEnvironment
+        return ModalEnvironment(image=image, cwd=cwd, timeout=timeout, **kwargs)
+    
+    else:
+        raise ValueError(f"Unknown environment type: {env_type}. Use 'local', 'docker', or 'modal'")
+
+
+# ============================================================================
+# Mini-SWE Runner with Hermes Trajectory Format
+# ============================================================================
+
+class MiniSWERunner:
+    """
+    Agent runner that uses Hermes-Agent's built-in execution environments
+    and outputs trajectories in Hermes-Agent format.
+    """
+    
+    def __init__(
+        self,
+        model: str = "anthropic/claude-sonnet-4.6",
+        base_url: str = None,
+        api_key: str = None,
+        env_type: str = "local",
+        image: str = "python:3.11-slim",
+        cwd: str = "/tmp",
+        max_iterations: int = 15,
+        command_timeout: int = 60,
+        verbose: bool = False,
+    ):
+        """
+        Initialize the Mini-SWE Runner.
+        
+        Args:
+            model: Model name for OpenAI-compatible API
+            base_url: API base URL (optional, uses env vars if not provided)
+            api_key: API key (optional, uses env vars if not provided)
+            env_type: Environment type - "local", "docker", or "modal"
+            image: Docker/Modal image (ignored for local)
+            cwd: Working directory for commands
+            max_iterations: Maximum tool-calling iterations
+            command_timeout: Default timeout for commands
+            verbose: Enable verbose logging
+        """
+        self.model = model
+        self.max_iterations = max_iterations
+        self.command_timeout = command_timeout
+        self.verbose = verbose
+        self.env_type = env_type
+        self.image = image
+        self.cwd = cwd
+        
+        # Setup logging
+        logging.basicConfig(
+            level=logging.DEBUG if verbose else logging.INFO,
+            format='%(asctime)s - %(levelname)s - %(message)s',
+            datefmt='%H:%M:%S'
+        )
+        self.logger = logging.getLogger(__name__)
+        
+        # Initialize LLM client via centralized provider router.
+        # If explicit api_key/base_url are provided (e.g. from CLI args),
+        # construct directly.  Otherwise use the router for OpenRouter.
+        if api_key or base_url:
+            from openai import OpenAI
+            client_kwargs = {
+                "base_url": base_url or "https://openrouter.ai/api/v1",
+                "api_key": api_key or os.getenv(
+                    "OPENROUTER_API_KEY",
+                    os.getenv("ANTHROPIC_API_KEY",
+                              os.getenv("OPENAI_API_KEY", ""))),
+            }
+            self.client = OpenAI(**client_kwargs)
+        else:
+            from agent.auxiliary_client import resolve_provider_client
+            self.client, _ = resolve_provider_client("openrouter", model=model)
+            if self.client is None:
+                # Fallback: try auto-detection
+                self.client, _ = resolve_provider_client("auto", model=model)
+            if self.client is None:
+                from openai import OpenAI
+                self.client = OpenAI(
+                    base_url="https://openrouter.ai/api/v1",
+                    api_key=os.getenv("OPENROUTER_API_KEY", ""))
+        
+        # Environment will be created per-task
+        self.env = None
+        
+        # Tool definition
+        self.tools = [TERMINAL_TOOL_DEFINITION]
+        
+        print("🤖 Mini-SWE Runner initialized")
+        print(f"   Model: {self.model}")
+        print(f"   Environment: {self.env_type}")
+        if self.env_type != "local":
+            print(f"   Image: {self.image}")
+        print(f"   Max iterations: {self.max_iterations}")
+    
+    def _create_env(self):
+        """Create the execution environment."""
+        print(f"🔧 Creating {self.env_type} environment...")
+        self.env = create_environment(
+            env_type=self.env_type,
+            image=self.image,
+            cwd=self.cwd,
+            timeout=self.command_timeout
+        )
+        print("✅ Environment ready")
+    
+    def _cleanup_env(self):
+        """Cleanup the execution environment."""
+        if self.env is not None:
+            if hasattr(self.env, 'cleanup'):
+                self.env.cleanup()
+            elif hasattr(self.env, 'stop'):
+                self.env.stop()
+            self.env = None
+    
+    def _execute_command(self, command: str, timeout: int = None) -> Dict[str, Any]:
+        """
+        Execute a command in the environment.
+        
+        Args:
+            command: Bash command to execute
+            timeout: Optional timeout override
+            
+        Returns:
+            Dict with 'output' and 'returncode'
+        """
+        if self.env is None:
+            self._create_env()
+        
+        try:
+            result = self.env.execute(command, timeout=timeout or self.command_timeout)
+            return {
+                "output": result.get("output", ""),
+                "exit_code": result.get("returncode", 0),
+                "error": None
+            }
+        except Exception as e:
+            return {
+                "output": "",
+                "exit_code": -1,
+                "error": str(e)
+            }
+    
+    def _format_tools_for_system_message(self) -> str:
+        """Format tool definitions for the system message."""
+        formatted_tools = []
+        for tool in self.tools:
+            func = tool["function"]
+            formatted_tools.append({
+                "name": func["name"],
+                "description": func.get("description", ""),
+                "parameters": func.get("parameters", {}),
+                "required": None
+            })
+        return json.dumps(formatted_tools, ensure_ascii=False)
+    
+    def _convert_to_hermes_format(
+        self,
+        messages: List[Dict[str, Any]],
+        user_query: str,
+        completed: bool
+    ) -> List[Dict[str, Any]]:
+        """
+        Convert internal message format to Hermes trajectory format.
+        
+        This produces the exact format used by batch_runner.py.
+        """
+        trajectory = []
+        
+        # System message with tool definitions
+        system_msg = (
+            "You are a function calling AI model. You are provided with function signatures within <tools> </tools> XML tags. "
+            "You may call one or more functions to assist with the user query. If available tools are not relevant in assisting "
+            "with user query, just respond in natural conversational language. Don't make assumptions about what values to plug "
+            "into functions. After calling & executing the functions, you will be provided with function results within "
+            "<tool_response> </tool_response> XML tags. Here are the available tools:\n"
+            f"<tools>\n{self._format_tools_for_system_message()}\n</tools>\n"
+            "For each function call return a JSON object, with the following pydantic model json schema for each:\n"
+            "{'title': 'FunctionCall', 'type': 'object', 'properties': {'name': {'title': 'Name', 'type': 'string'}, "
+            "'arguments': {'title': 'Arguments', 'type': 'object'}}, 'required': ['name', 'arguments']}\n"
+            "Each function call should be enclosed within <tool_call> </tool_call> XML tags.\n"
+            "Example:\n<tool_call>\n{'name': <function-name>,'arguments': <args-dict>}\n</tool_call>"
+        )
+        
+        trajectory.append({"from": "system", "value": system_msg})
+        trajectory.append({"from": "human", "value": user_query})
+        
+        # Process messages (skip first user message as we already added it)
+        i = 1
+        while i < len(messages):
+            msg = messages[i]
+            
+            if msg["role"] == "assistant":
+                if "tool_calls" in msg and msg["tool_calls"]:
+                    # Assistant message with tool calls
+                    content = ""
+                    
+                    # Add reasoning if present
+                    if msg.get("reasoning"):
+                        content = f"<think>{msg['reasoning']}</think>"
+                    
+                    if msg.get("content"):
+                        content += msg["content"] + "\n"
+                    
+                    # Add tool calls in XML format
+                    for tool_call in msg["tool_calls"]:
+                        if not tool_call or not isinstance(tool_call, dict): continue
+                        try:
+                            arguments = json.loads(tool_call["function"]["arguments"]) \
+                                if isinstance(tool_call["function"]["arguments"], str) \
+                                else tool_call["function"]["arguments"]
+                        except json.JSONDecodeError:
+                            arguments = {}
+                        
+                        tool_call_json = {
+                            "name": tool_call["function"]["name"],
+                            "arguments": arguments
+                        }
+                        content += f"<tool_call>\n{json.dumps(tool_call_json, ensure_ascii=False)}\n</tool_call>\n"
+                    
+                    trajectory.append({"from": "gpt", "value": content.rstrip()})
+                    
+                    # Collect subsequent tool responses
+                    tool_responses = []
+                    j = i + 1
+                    while j < len(messages) and messages[j]["role"] == "tool":
+                        tool_msg = messages[j]
+                        tool_content = tool_msg["content"]
+                        
+                        # Try to parse as JSON
+                        try:
+                            if tool_content.strip().startswith(("{", "[")):
+                                tool_content = json.loads(tool_content)
+                        except (json.JSONDecodeError, AttributeError):
+                            pass
+                        
+                        tool_response = "<tool_response>\n"
+                        tool_response += json.dumps({
+                            "tool_call_id": tool_msg.get("tool_call_id", ""),
+                            "name": msg["tool_calls"][len(tool_responses)]["function"]["name"] \
+                                if len(tool_responses) < len(msg["tool_calls"]) else "unknown",
+                            "content": tool_content
+                        }, ensure_ascii=False)
+                        tool_response += "\n</tool_response>"
+                        tool_responses.append(tool_response)
+                        j += 1
+                    
+                    if tool_responses:
+                        trajectory.append({"from": "tool", "value": "\n".join(tool_responses)})
+                        i = j - 1
+                
+                else:
+                    # Regular assistant message (no tool calls)
+                    content = ""
+                    if msg.get("reasoning"):
+                        content = f"<think>{msg['reasoning']}</think>"
+                    content += msg.get("content") or ""
+                    trajectory.append({"from": "gpt", "value": content})
+            
+            elif msg["role"] == "user":
+                trajectory.append({"from": "human", "value": msg["content"]})
+            
+            i += 1
+        
+        return trajectory
+    
+    def run_task(self, task: str) -> Dict[str, Any]:
+        """
+        Run a single task and return the result with trajectory.
+        
+        Args:
+            task: The task/prompt to execute
+            
+        Returns:
+            Dict with trajectory, completion status, and metadata
+        """
+        print(f"\n{'='*60}")
+        print(f"📝 Task: {task[:80]}{'...' if len(task) > 80 else ''}")
+        print(f"{'='*60}")
+        
+        # Initialize environment
+        self._create_env()
+        
+        # Message history
+        messages = [{"role": "user", "content": task}]
+        
+        # System prompt for the LLM (ephemeral - not saved to trajectory)
+        system_prompt = """You are an AI agent that can execute bash commands to complete tasks.
+
+When you need to run commands, use the 'terminal' tool with your bash command.
+
+**Important:**
+- When you have completed the task successfully, run: echo "MINI_SWE_AGENT_FINAL_OUTPUT" followed by a summary
+- Be concise and efficient in your approach
+- Install any needed tools with apt-get or pip
+- Avoid interactive commands (no vim, nano, less, etc.)
+
+Complete the user's task step by step."""
+        
+        api_call_count = 0
+        completed = False
+        final_response = None
+        
+        try:
+            while api_call_count < self.max_iterations:
+                api_call_count += 1
+                print(f"\n🔄 API call #{api_call_count}/{self.max_iterations}")
+                
+                # Prepare API messages
+                api_messages = [{"role": "system", "content": system_prompt}] + messages
+                
+                # Make API call
+                try:
+                    api_kwargs = {
+                        "model": self.model,
+                        "messages": api_messages,
+                        "tools": self.tools,
+                        "timeout": 300.0,
+                    }
+                    fixed_temperature = _effective_temperature_for_model(
+                        self.model,
+                        str(getattr(self.client, "base_url", "") or ""),
+                    )
+                    if fixed_temperature is not None:
+                        api_kwargs["temperature"] = fixed_temperature
+
+                    response = self.client.chat.completions.create(**api_kwargs)
+                except Exception as e:
+                    self.logger.error(f"API call failed: {e}")
+                    break
+                
+                assistant_message = response.choices[0].message
+                
+                # Log assistant response
+                if assistant_message.content:
+                    print(f"🤖 Assistant: {assistant_message.content[:100]}...")
+                
+                # Check for tool calls
+                if assistant_message.tool_calls:
+                    print(f"🔧 Tool calls: {len(assistant_message.tool_calls)}")
+                    
+                    # Add assistant message with tool calls
+                    messages.append({
+                        "role": "assistant",
+                        "content": assistant_message.content,
+                        "tool_calls": [
+                            {
+                                "id": tc.id,
+                                "type": tc.type,
+                                "function": {
+                                    "name": tc.function.name,
+                                    "arguments": tc.function.arguments
+                                }
+                            }
+                            for tc in assistant_message.tool_calls
+                        ]
+                    })
+                    
+                    # Execute each tool call
+                    for tc in assistant_message.tool_calls:
+                        try:
+                            args = json.loads(tc.function.arguments)
+                        except json.JSONDecodeError:
+                            args = {}
+                        
+                        command = args.get("command", "echo 'No command provided'")
+                        timeout = args.get("timeout", self.command_timeout)
+                        
+                        print(f"   📞 terminal: {command[:60]}...")
+                        
+                        # Execute command
+                        result = self._execute_command(command, timeout)
+                        
+                        # Format result
+                        result_json = json.dumps({
+                            "content": {
+                                "output": result["output"],
+                                "exit_code": result["exit_code"],
+                                "error": result["error"]
+                            }
+                        }, ensure_ascii=False)
+                        
+                        # Check for task completion signal
+                        if "MINI_SWE_AGENT_FINAL_OUTPUT" in result["output"]:
+                            print("   ✅ Task completion signal detected!")
+                            completed = True
+                        
+                        # Add tool response
+                        messages.append({
+                            "role": "tool",
+                            "content": result_json,
+                            "tool_call_id": tc.id
+                        })
+                        
+                        print(f"   ✅ exit_code={result['exit_code']}, output={len(result['output'])} chars")
+                    
+                    # If task completed, we can stop
+                    if completed:
+                        final_response = assistant_message.content
+                        break
+                
+                else:
+                    # No tool calls - final response
+                    final_response = assistant_message.content or ""
+                    messages.append({
+                        "role": "assistant",
+                        "content": final_response
+                    })
+                    completed = True
+                    print("🎉 Agent finished (no more tool calls)")
+                    break
+            
+            if api_call_count >= self.max_iterations:
+                print(f"⚠️  Reached max iterations ({self.max_iterations})")
+        
+        finally:
+            # Cleanup environment
+            self._cleanup_env()
+        
+        # Convert to Hermes trajectory format
+        trajectory = self._convert_to_hermes_format(messages, task, completed)
+        
+        return {
+            "conversations": trajectory,
+            "completed": completed,
+            "api_calls": api_call_count,
+            "metadata": {
+                "model": self.model,
+                "env_type": self.env_type,
+                "timestamp": datetime.now().isoformat()
+            }
+        }
+    
+    def run_batch(
+        self,
+        prompts: List[str],
+        output_file: str
+    ) -> List[Dict[str, Any]]:
+        """
+        Run multiple tasks and save trajectories to a JSONL file.
+        
+        Args:
+            prompts: List of task prompts
+            output_file: Output JSONL file path
+            
+        Returns:
+            List of results
+        """
+        results = []
+        
+        print(f"\n📦 Running batch of {len(prompts)} tasks")
+        print(f"📁 Output: {output_file}")
+        
+        with open(output_file, 'w', encoding='utf-8') as f:
+            for i, prompt in enumerate(prompts, 1):
+                print(f"\n{'='*60}")
+                print(f"📋 Task {i}/{len(prompts)}")
+                print(f"{'='*60}")
+                
+                try:
+                    result = self.run_task(prompt)
+                    results.append(result)
+                    
+                    # Write to file immediately
+                    f.write(json.dumps(result, ensure_ascii=False) + "\n")
+                    f.flush()
+                    
+                    print(f"✅ Task {i} completed (api_calls={result['api_calls']})")
+                    
+                except Exception as e:
+                    self.logger.error(f"Error on task {i}: {e}")
+                    error_result = {
+                        "conversations": [],
+                        "completed": False,
+                        "api_calls": 0,
+                        "error": str(e),
+                        "metadata": {"timestamp": datetime.now().isoformat()}
+                    }
+                    results.append(error_result)
+                    f.write(json.dumps(error_result, ensure_ascii=False) + "\n")
+                    f.flush()
+        
+        print(f"\n✅ Batch complete! {len(results)} trajectories saved to {output_file}")
+        return results
+
+
+# ============================================================================
+# CLI Interface
+# ============================================================================
+
+def main(
+    task: str = None,
+    prompts_file: str = None,
+    output_file: str = "swe-runner-test1.jsonl",
+    model: str = "claude-sonnet-4-20250514",
+    base_url: str = None,
+    api_key: str = None,
+    env: str = "local",
+    image: str = "python:3.11-slim",
+    cwd: str = "/tmp",
+    max_iterations: int = 15,
+    timeout: int = 60,
+    verbose: bool = False,
+):
+    """
+    Run SWE tasks with Hermes trajectory format output.
+    
+    Args:
+        task: Single task to run (use this OR prompts_file)
+        prompts_file: JSONL file with prompts (each line: {"prompt": "..."})
+        output_file: Output JSONL file for trajectories
+        model: Model name (default: claude-sonnet-4-20250514)
+        base_url: API base URL (optional)
+        api_key: API key (optional, uses env vars)
+        env: Environment type - "local", "docker", or "modal"
+        image: Docker/Modal image (default: python:3.11-slim)
+        cwd: Working directory (default: /tmp)
+        max_iterations: Maximum tool-calling iterations (default: 15)
+        timeout: Command timeout in seconds (default: 60)
+        verbose: Enable verbose logging
+        
+    Examples:
+        # Single task with local environment
+        python mini_swe_runner.py --task "Create hello.py that prints Hello World"
+        
+        # Single task with Docker
+        python mini_swe_runner.py --task "List files" --env docker
+        
+        # Batch from file
+        python mini_swe_runner.py --prompts_file tasks.jsonl --output_file results.jsonl
+    """
+    print("🚀 Mini-SWE Runner with Hermes Trajectory Format")
+    print("=" * 60)
+    
+    # Initialize runner
+    runner = MiniSWERunner(
+        model=model,
+        base_url=base_url,
+        api_key=api_key,
+        env_type=env,
+        image=image,
+        cwd=cwd,
+        max_iterations=max_iterations,
+        command_timeout=timeout,
+        verbose=verbose,
+    )
+    
+    if task:
+        # Single task mode
+        result = runner.run_task(task)
+        
+        # Save to file
+        with open(output_file, 'w', encoding='utf-8') as f:
+            f.write(json.dumps(result, ensure_ascii=False) + "\n")
+        
+        print(f"\n📁 Trajectory saved to: {output_file}")
+        print(f"✅ Completed: {result['completed']}")
+        print(f"📞 API calls: {result['api_calls']}")
+        print(f"💬 Turns: {len(result['conversations'])}")
+        
+    elif prompts_file:
+        # Batch mode
+        prompts = []
+        with open(prompts_file, 'r', encoding='utf-8') as f:
+            for line in f:
+                line = line.strip()
+                if line:
+                    try:
+                        entry = json.loads(line)
+                        prompts.append(entry.get("prompt", entry.get("task", "")))
+                    except json.JSONDecodeError:
+                        prompts.append(line)
+        
+        if not prompts:
+            print(f"❌ No prompts found in {prompts_file}")
+            return
+        
+        runner.run_batch(prompts, output_file)
+    
+    else:
+        print("❌ Please provide either --task or --prompts_file")
+        print("   Example: python mini_swe_runner.py --task 'Create a hello world script'")
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

scripts/rl_cli.py

@@ -0,0 +1,449 @@
+#!/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 os
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import asyncio
+from pathlib import Path
+
+import fire
+import yaml
+
+from hermes_constants import get_hermes_home, OPENROUTER_BASE_URL
+
+# 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.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") 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)
+                response = 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:
+            response = 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)

scripts/sample_and_compress.py

@@ -267,7 +267,7 @@ def run_compression(input_dir: Path, output_dir: Path, config_path: str):
     # Import the compressor
     import sys
     sys.path.insert(0, str(Path(__file__).parent.parent))
-    from trajectory_compressor import TrajectoryCompressor, CompressionConfig
+    from scripts.trajectory_compressor import TrajectoryCompressor, CompressionConfig
     
     print(f"\n🗜️  Running trajectory compression...")
     print(f"   Input: {input_dir}")