DocuBot — AI-Powered Documentation Generator
DocuBot is an intelligent documentation generation platform that analyzes GitHub repositories using specialized micro-agents to automatically create comprehensive, well-structured README documentation with architecture diagrams, deployment guides, and troubleshooting sections.
Developed as an open-source blueprint under the Cloud2 Labs Innovation Hub, DocuBot demonstrates how multi-agent AI systems, LangGraph workflow orchestration, and multi-provider LLM integration can be combined into a practical end-to-end documentation automation workflow.
It showcases a production-style micro-agent architecture for analyzing codebases, extracting concrete evidence from repositories, generating factually accurate documentation with quality validation, and creating GitHub Pull Requests automatically using the Model Context Protocol (MCP).
What It Demonstrates
DocuBot illustrates how to
- Analyze GitHub repositories automatically to understand project structure and purpose
- Generate complete README sections using 9 specialized micro-agents working in parallel
- Create architecture diagrams with Mermaid using semantic validation against evidence
- Extract API endpoints, dependencies, configuration files, and error handlers from code
- Support multiple LLM providers (OpenAI, Groq, Ollama, OpenRouter, Custom APIs, Enterprise Inference)
- Validate documentation quality with evidence-based QA to prevent hallucinations
- Create GitHub Pull Requests automatically with generated documentation via MCP
Designed for development teams, DevOps engineers, documentation writers, and innovation groups, DocuBot serves as a reference implementation for AI-driven documentation automation.
Key Capabilities
Multi-Provider LLM Support
Supports OpenAI, Groq, Ollama, OpenRouter, custom OpenAI-compatible APIs, and enterprise inference endpoints for flexible deployment options.
9 Specialized Micro-Agents
Code Explorer (Overview & Features), API Reference (Endpoint Extraction), Call Graph (Architecture), Error Analysis (Troubleshooting), Env Config (Configuration), Dependency Analyzer (Prerequisites & Deployment), Planner (Section Planning), Mermaid (Diagram Generation), QA Validator (Quality Check).
Evidence-Based
Generation
Collects concrete evidence from filesystem (dependencies, Docker files, config files, languages) to ensure factually accurate documentation and prevent AI hallucinations.
Monorepo Detection & Project Selection
Automatically detects multiple projects in repositories and allows users to select which project to document.
Architecture Diagram Generation
Creates Mermaid diagrams with semantic validation to ensure diagrams match detected project components (backend, frontend, database).
Quality
Validation
QA agent validates all sections against collected evidence with quality scoring to ensure documentation accuracy.
Automated PR
Creation
Creates GitHub Pull Requests automatically using MCP (Model Context Protocol) with generated README on a new branch.
Real-Time Progress
Tracking
Provides Server-Sent Events (SSE) streaming for live agent activity updates and workflow progress visualization.
Containerized Microservices Architecture
Uses Docker-based services with LangGraph orchestration for reproducible deployment and experimentation.