I'm building this project to accelerate AI agent development, and I wanted to share a solution to a problem that's been costing me a lot of time.
The core issue is that when I use a general AI assistant to write code for a rapidly evolving library like LangGraph, its knowledge is often outdated. It suggests code based on its training data or old blog posts, which might be for v0.1 of a library when I'm using v0.2. This leads to a frustrating cycle of running code, hitting an error because an API changed, and then spending hours debugging the AI's "correct" but outdated answer.
My solution is to ground the AI assistant in the executable truth of the official, version-controlled documentation. That's why I created the LangGraph-Dev-Navigator.
Here's how it works:
A Local Source of Truth: The project uses a git submodule to clone the official langgraph repository locally. This means the documentation the AI uses is the exact same one that corresponds to the code on my machine. If I need to work with langgraph==0.0.56, I just check out that tag in the submodule, and the AI's entire knowledge base is instantly aligned.
RAG on Verified Docs: The assistant is equipped with tools (perform_rag_query, search_code_examples) that only perform Retrieval-Augmented Generation (RAG) on this local, version-aligned documentation. It doesn't poll the open internet or rely on its static internal knowledge. This ensures the context for code generation is always current and correct for my environment (future goal is combines the advantages of rich Google results and precise official docs).
While the framework also has an experimental feature for validating code against a Knowledge Graph (check_ai_script_hallucinations), the primary focus right now is on this principle of grounding the AI in trusted, version-synced knowledge.
The goal is to create a blueprint for building reliable AI assistants for any fast-moving project. By ensuring the AI's knowledge comes from the right source, we can dramatically reduce debugging time and accelerate the development of complex agents.
The project is open-source, and I would love to get your feedback, especially from anyone else who has struggled with keeping AI assistants in sync with library updates.
My solution is to ground the AI assistant in the executable truth of the official, version-controlled documentation. That's why I created the LangGraph-Dev-Navigator.
Here's how it works:
A Local Source of Truth: The project uses a git submodule to clone the official langgraph repository locally. This means the documentation the AI uses is the exact same one that corresponds to the code on my machine. If I need to work with langgraph==0.0.56, I just check out that tag in the submodule, and the AI's entire knowledge base is instantly aligned.
RAG on Verified Docs: The assistant is equipped with tools (perform_rag_query, search_code_examples) that only perform Retrieval-Augmented Generation (RAG) on this local, version-aligned documentation. It doesn't poll the open internet or rely on its static internal knowledge. This ensures the context for code generation is always current and correct for my environment (future goal is combines the advantages of rich Google results and precise official docs).
While the framework also has an experimental feature for validating code against a Knowledge Graph (check_ai_script_hallucinations), the primary focus right now is on this principle of grounding the AI in trusted, version-synced knowledge.
The goal is to create a blueprint for building reliable AI assistants for any fast-moving project. By ensuring the AI's knowledge comes from the right source, we can dramatically reduce debugging time and accelerate the development of complex agents.
The project is open-source, and I would love to get your feedback, especially from anyone else who has struggled with keeping AI assistants in sync with library updates.
The repo is here: https://github.com/botingw/langgraph-dev-navigator