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docs: align terminal-backend count and naming across docs and code
README:24 claimed "Six terminal backends" while tools/environments/ exposes seven top-level backend choices through TERMINAL_ENV: local, docker, ssh, singularity, modal, daytona, vercel_sandbox. Modal additionally has direct and Nous-managed modes selected via terminal.modal_mode (the ManagedModalEnvironment class is a Modal sub-mode, not a separate top-level backend). The same drift appeared in five other doc and code-comment sites with inconsistent counts (six, seven, or implicit) and varying lists. Updated all sites to a consistent seven-backend list in canonical order. The configuration guide also clarifies how Modal's two modes are selected so operators do not search for a non-existent backend: managed_modal value. CONTRIBUTING.md:160 lists six backend filenames in a code tree but does not carry the "Six terminal" prose; left out of scope per cohesion sweep guidance to bundle only identical wording. Files updated: - README.md (line 24, marketing copy) - website/docs/index.md (line 49, landing page) - website/docs/user-guide/configuration.md (line 86, config guide) - tools/environments/__init__.py (lines 3-6, package docstring) - tools/file_operations.py (line 6, module docstring) - environments/README.md (line 43, RL training docs — TERMINAL_ENV list)
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@ -40,7 +40,7 @@ This directory contains the integration layer between **hermes-agent's** tool-ca
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- `evaluate_log()` for saving eval results to JSON + samples.jsonl
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**HermesAgentBaseEnv** (`hermes_base_env.py`) extends BaseEnv with hermes-agent specifics:
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- Sets `os.environ["TERMINAL_ENV"]` to configure the terminal backend (local, docker, modal, daytona, ssh, singularity)
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- Sets `os.environ["TERMINAL_ENV"]` to configure the terminal backend (local, docker, ssh, singularity, modal, daytona, vercel_sandbox)
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- Resolves hermes-agent toolsets via `_resolve_tools_for_group()` (calls `get_tool_definitions()` which queries `tools/registry.py`)
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- Implements `collect_trajectory()` which runs the full agent loop and computes rewards
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- Supports two-phase operation (Phase 1: OpenAI server, Phase 2: VLLM ManagedServer)
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