Fixed, thank you

The DB is stored locally, and any embedding, reranker and LLM will work. It's up to you if you self host these or bring them externally from one SaaS or the other

`chunkhound search <query>`, `chunkhound search --regex <query>` and `chunkhound research <query>` are the main cli entry points that you can already use today

Completely free, MIT licensed. You can fully self host it if you have the hardware to run Qwen3-embedding and reranker models

Just add to your prompt something like "use code research", but yes there's a PR in the works that fixes that and optimizes the MCP tools interface - https://github.com/chunkhound/chunkhound/pull/150

ChunkHound does it a bit differently, since at true enterprise scale it's very slow and costly to pass all code chunks through an LLM during indexing time. Instead, ChunkHound implements a customized "deep research" algorithm that's been optimized for code exploration so it can answer, on demand, any deep technical question about the indexed codebase. This research agent can be powered by a lower tier LLM (think Haiku, Codex low, etc) that's already included in your subscription.

Thank you, yes the docs are overdue for a refresh. It's in the works

Presumably it could update its own docs

Exactly. There's an autodoc feature coming up in the next version

It also supports OpenAI compatible embeddings server if you have a local one lying around

Because you still need to save it, back it up, and share it with others who will likely want to do the same

Right. But how do you scale it?