ChunkHound – Advanced Code RAG

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Hi everyone, I wanted to share ChunkHound with the community in the hope someone else finds it as useful as I do. ChunkHound is a modern RAG solution for your codebase via MCP. I started this project because I wanted good code RAG for use with Claude Code, that works offline, and that's capable of handling large codebases. Specifically, I built it to handle my work on GoatDB ( https://goatdb.dev/ ) and my projects at work. LLMs like Claude and GPT don’t know your codebase - they only know what they were trained on. Every time they help you code, they need to search your files to understand your project’s specific patterns and terminology. ChunkHound solves that by equipping your agent with advanced semantic search over the entire codebase, which enable it to handle complex real world projects efficiently. This latest release introduces an implementation of the cAST algorithm and a two-hop semantic search with a reranker which together greatly increase the efficiency and capacity for handling large, complex, codebases. I would really appreciate any kind of feedback. Thank you!

Visit product View on Hacker NewsAug 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, mcp · Missing: mac, agents, macos

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started, efficiently · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce, real world · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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