ai-doc-gen

ai-doc-gen

AI-powered multi-agent system that automatically analyzes codebases and generates comprehensive documentation. Features GitLab integration, concurrent processing, and multiple LLM support for better code understanding and developer onboarding.

Stars: 616

Visit
 screenshot

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.

README:

AI Documentation Generator

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.

📝 Blog Posts

Read the full story behind this project:

Table of Contents

Features

  • 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
  • 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

Installation

Prerequisites

  • 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
  1. Install using uv (recommended):
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
  1. Or install with pip:
pip install -e .

Quick Start

  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
  1. Run analysis and generate documentation:
# Analyze your repository
uv run src/main.py analyze --repo-path .

# Generate documentation
uv run src/main.py document --repo-path .

Generated documentation will be saved to .ai/docs/ directory.

Usage

Advanced Options

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

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

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

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

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

Configuration

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

Configuration Options

  • 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.

Architecture

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

  • CLI Layer: Entry point with command parsing
  • Handler Layer: Command-specific business logic (analyze, document, cronjob)
  • Agent Layer: AI-powered analysis and documentation generation
  • Tool Layer: File system operations and utilities

Technology Stack

  • 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

License

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

Acknowledgments

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

For Tasks:

Click tags to check more tools for each tasks

For Jobs:

Alternative AI tools for ai-doc-gen

Similar Open Source Tools

For similar tasks

For similar jobs