Show HN: I built a powerful RAG and knowledge graph agent that runs locally
Claw-Coder is a locally running AI agent designed to enhance privacy and security while coding. It addresses the performance limitations of local models by integrating tools like a knowledge graph and Docker execution. Users can test Claw-Coder by installing it through a specific command, although it remains closed source during its testing phase.
- ▪Claw-Coder runs locally on laptops, providing powerful tools without compromising privacy.
- ▪It utilizes a knowledge graph to improve the AI's understanding of code relationships, enhancing performance in coding tasks.
- ▪The agent incorporates Docker execution to validate generated code, increasing its reliability.
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Claw-Coder is an AI agent that runs locally on your laptop and has access to powerful tools instead of configuring claude or codex to use a local model just use claw-coder.Why was claw-coder created? Answer: To solve the problem of privacy and security. When you use an agent that is configured with a cloud model like codex, cursor, Claude etc. You are not just getting the agent but you are giving up your codebase to train an llm which is a bit concerning and this reduces trust in the technology called AI but now another problem comes in performance when you switch to a local model that is not made for that workflow you lose performance, speed, and it becomes really a tradeoff so that's where claw-coder comes in it not only runs on your machine but all the code, rag, knowledge graph etc…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.