An AI-powered code documentation generator that automatically analyzes repositories and creates comprehensive documentation using advanced language models. The system employs a multi-agent architecture to perform specialized code analysis and generate structured documentation.

Read the full story behind this project:

• 🇺🇸 English: Docs That Don’t Rot: How Multi-Agent AI Rewrote Our Workflow
• 🇮🇷 از دستیار کدنویس تا همکار هوشمند؛ گام اول: کابوس مستندسازی

• Features
• Installation
• Quick Start
• Usage
• Configuration
• Architecture
• License

• Multi-Agent Analysis: Specialized AI agents for code structure, data flow, dependency, request flow, and API analysis
• Automated Documentation: Generates comprehensive README files with configurable sections
• AI Assistant Configuration: Automatically generates CLAUDE.md, AGENTS.md, and .cursor/rules/ files for AI coding assistants
• GitLab Integration: Automated analysis for GitLab projects with merge request creation
• Concurrent Processing: Parallel execution of analysis agents for improved performance
• Flexible Configuration: YAML-based configuration with environment variable overrides
• Multiple LLM Support: Works with any OpenAI-compatible API (OpenAI, OpenRouter, local models, etc.)
• Observability: Built-in monitoring with OpenTelemetry tracing and Langfuse integration

• Python 3.13
• Git
• API access to an OpenAI-compatible LLM provider

1. Clone the repository:

git clone https://github.com/divar-ir/ai-doc-gen.git
cd ai-doc-gen

2. Install using uv (recommended):

curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync

3. Or install with pip:

pip install -e .

1. Set up your environment and configuration:

# Copy and edit environment variables
cp .env.sample .env

# Copy and edit configuration
mkdir -p .ai
cp config_example.yaml .ai/config.yaml

2. Run analysis and generate documentation:

# Analyze your repository
uv run src/main.py analyze --repo-path .

# Generate README documentation
uv run src/main.py generate readme --repo-path .

# Generate AI assistant configuration files (CLAUDE.md, AGENTS.md, .cursor/rules/)
uv run src/main.py generate ai-rules --repo-path .

Generated documentation will be saved to .ai/docs/ directory, and AI configuration files will be placed in your repository root.

# Analyze codebase
uv run src/main.py analyze --repo-path <path>

# Generate README documentation
uv run src/main.py generate readme --repo-path <path>

# Generate AI assistant configuration files
uv run src/main.py generate ai-rules --repo-path <path>

# Run cronjob (GitLab integration)
uv run src/main.py cronjob analyze

Analysis Options:

# Analyze with specific exclusions
uv run src/main.py analyze --repo-path . --exclude-code-structure --exclude-data-flow

# Use custom configuration file
uv run src/main.py analyze --repo-path . --config /path/to/config.yaml

README Generation Options:

# Generate with specific section exclusions
uv run src/main.py generate readme --repo-path . --exclude-architecture --exclude-c4-model

# Use existing README as context
uv run src/main.py generate readme --repo-path . --use-existing-readme

AI Rules Generation Options:

# Skip overwriting existing files
uv run src/main.py generate ai-rules --repo-path . \
    --skip-existing-claude-md \
    --skip-existing-agents-md \
    --skip-existing-cursor-rules

# Customize detail level and line limits
uv run src/main.py generate ai-rules --repo-path . \
    --detail-level comprehensive \
    --max-claude-lines 600 \
    --max-agents-lines 150

The tool automatically looks for configuration in .ai/config.yaml or .ai/config.yml in your repository.

• Exclude specific analyses: Skip code structure, data flow, dependencies, request flow, or API analysis
• Customize README sections: Control which sections appear in generated documentation
• Configure cronjob settings: Set working paths and commit recency filters

You can use CLI flags for quick configuration overrides. See config_example.yaml for all available options and .env.sample for environment variables.

The system uses a multi-agent architecture with specialized AI agents for different types of code analysis and generation:

• CLI Layer: Entry point with command parsing and subcommand routing
• Handler Layer: Command-specific business logic (analyze, generate, cronjob)
• Agent Layer: AI-powered analysis and documentation generation
  • Analyzer agents: Structure, data flow, dependencies, request flow, API analysis
  • Documentation agent: README generation
  • AI Rules generator: CLAUDE.md, AGENTS.md, and Cursor rules generation
• Tool Layer: File system operations and utilities

• Python 3.13 with pydantic-ai for AI agent orchestration
• OpenAI-compatible APIs for LLM access (OpenAI, OpenRouter, etc.)
• GitPython & python-gitlab for repository operations
• OpenTelemetry & Langfuse for observability
• YAML + Pydantic for configuration management

This project is licensed under the MIT License - see the LICENSE file for details.

• Built with pydantic-ai for AI agent orchestration
• Supports multiple LLM providers through OpenAI-compatible APIs (including OpenRouter)
• Uses Langfuse for LLM observability