Overview
This project leverages CrewAI’s multi-agent framework to automatically generate comprehensive documentation for any public GitHub repository, powered by NVIDIA’s NeMo Retriever and Llama-3.3-70B models.
It goes beyond templated summaries — each agent collaborates through a reasoning-driven pipeline to understand, analyze, and explain code structures.
Key Features
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Multi-Agent Collaboration: Separate agents for planning, writing, and reviewing documentation.
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NVIDIA NIM Integration: Combines NeMo Retriever embeddings with Llama-3.3-70B for semantic depth.
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Mermaid Diagram Generation: Automatically produces architecture diagrams.
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Quality Assurance: Ensures coherence and accuracy through a review loop.
Architecture Summary
1. Ingestion Phase
The system indexes mermaid.js examples using NVIDIA’s embedding NIM.2. Analysis Phase
Agents map repository structures and dependencies.3. Documentation Phase
High-level and detailed documentation is generated and reviewed collaboratively.My Evaluation
I tested the system on a personal academic project.
Pros
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The staged reasoning pipeline delivers genuinely context-aware documentation.
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Multi-agent workflow feels modular and transparent.
Cons
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Default prompts are less optimized for research-oriented repositories.
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Diagram output could better handle abstract architectures.
To address this, I created a customized version fine-tuned for academic codebases: Github Link .
Takeaways
This experiment demonstrates how AI can bridge the gap between code understanding and documentation creation.
It’s a powerful preview of how collaborative AI systems like CrewAI will shape developer tooling — making documentation not a burden, but a byproduct of understanding.