rllm/mkdocs.yml at main · agentica-project/rllm · GitHub

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site_name: rLLM
site_description: A library for training LLM agents with reinforcement learning
repo_url: https://github.com/agentica-project/rllm
docs_dir: docs
theme:
  name: material
  logo: assets/agentica-logo.png
  favicon: assets/agentica-logo-black.png
  palette:
    primary: black
    accent: black
  features:
    - navigation.instant
    - navigation.tracking
    - navigation.tabs
    - navigation.sections
    - navigation.top
    - navigation.expand
    - navigation.indexes
    - content.code.copy
    - content.code.annotate

plugins:
  - search
  - mkdocstrings:
      handlers:
        python:
          paths: [../]
          options:
            docstring_style: google
            show_source: true
            show_root_heading: true
            show_root_toc_entry: true
            show_signature_annotations: true
            separate_signature: true
            show_symbol_type_heading: true
            show_symbol_type_toc: true

watch:
  - ./rllm/

markdown_extensions:
  - pymdownx.highlight:
      anchor_linenums: true
  - pymdownx.inlinehilite
  - pymdownx.snippets:
      base_path: ['.', '..']
      check_paths: true
  - pymdownx.superfences
  - admonition
  - pymdownx.details
  - pymdownx.tabbed:
      alternate_style: true
  - tables
  - footnotes

nav:
  - Home:
      - Overview: index.md
      - Getting Started:
        - Installation Guide: getting-started/installation.md
        - Quick Start: getting-started/quick-start.md
      - Core Concepts:
        - rLLM's Main Components: core-concepts/overview.md
        - Agent and Environment: core-concepts/agent_env.md
        - AgentExecutionEngine for Trajectory Rollout: core-concepts/execution-engine.md
        - AgentTrainer for RL Training: core-concepts/training.md
        - RL Algorithms for Agents: core-concepts/rl-algos.md
      - Examples: examples/index.md
      - API Reference: api/index.md
      - Contributing: contributing.md
  - Getting Started:
      - Installation: getting-started/installation.md
      - Quick Start: getting-started/quick-start.md
  - Core Concepts:
      - rLLM's Main Components: core-concepts/overview.md
      - Agent and Environment: core-concepts/agent_env.md
      - AgentExecutionEngine for Trajectory Rollout: core-concepts/execution-engine.md
      - AgentTrainer for RL Training: core-concepts/training.md
      - RL Algorithms for Agents: core-concepts/rl-algos.md
  - Examples:
      - Overview: examples/index.md
      - DeepScaleR: examples/deepscaler.md
      - DeepCoder: examples/deepcoder.md
      - DeepSWE: examples/swe.md
      - Search Agent: examples/search.md
      - FrozenLake Agent: examples/frozenlake.md
      - Math SFT: examples/sft.md

  - API Reference:
      - Overview: api/index.md
      - Agents:
          - Agent Base: api/agents/agent.md
          - Agent Utils: api/agents/utils.md
      - Environments:
          - Environment Base: api/environments/base.md
          - Environment Utils: api/environments/env_utils.md
      - Engine:
          - Agent Execution Engine: api/engine/agent_execution_engine.md
      - Trainer:
          - Agent Trainer: api/trainer/agent_trainer.md
      - Tools:
          - Tool Base Classes: api/tools/tool_base.md
          - Tool Registry: api/tools/registry.md
          - Web Tools: api/tools/web_tools.md
          - Code Tools: api/tools/code_tools.md
      - Parser:
          - Tool Parsers: api/parser/tool_parser.md
          - Chat Parsers: api/parser/chat_parser.md
  - Contributing: contributing.md