Build a local RAG and knowledge graph agent for code analysis

Deploy a privacy-preserving AI coding agent on your laptop with RAG, knowledge graphs, and tool access

Updated: 5/24/2026
Difficulty
hard
Time
2-4 hours
Use Case
Analyze large codebases locally without uploading to cloud, with AI understanding of code relationships and ability to validate generated code
Popularity
0 views

About this automation

Set up a local AI agent that combines RAG (retrieval-augmented generation) with knowledge graphs to understand code relationships, augmented with tools like search, Docker execution, and vision capabilities to validate code without privacy exposure.

How to implement

1

Install Claw-Coder via brew tap gabriel-c70/claw && brew install claw-coder

2

Configure a local LLM (8B-13B model) as the agent backbone

3

Set up a vector store for RAG to handle large codebases without context window overflow

4

Build a knowledge graph mapping code entities and relationships in your codebase

5

Expose tools: search_tool for up-to-date info, Docker execution for code validation

6

Add vision LLM for HTML/CSS rendering verification

7

Test on real codebases and iterate based on feedback