LEANN

@Berkeley SkyLab, we’re the first to bring semantic search to Claude Code with a fully local index in a novel, lightweight structure — check it out at LEANN(https://github.com/yichuan-w/LEANN). Unlike Claude-context, which uploads all data to the cloud, or Serena, which is heavy and limited to keyword search, our solution installs in just 1 minute and instantly enhances Claude Code’s capabilities.Unlike Claude-context, which uploads everything to the cloud, and Serena, which is heavy and limited to keyword search, our method sets up in just 1 minute and instantly boosts Claude Code’s quality.

I think there’s huge potential for a fully local “Cursor-like” stack — no cloud, no API keys, just everything running on your machine. The setup could be: • Cursor CLI for agentic/dev stuff (example:https://x.com/cursor_ai/status/1953559384531050724) • A local memory layer compatible with the CLI — something like LEANN (97% smaller index, zero cloud cost, full privacy, https://github.com/yichuan-w/LEANN) or Milvus (though Milvus often ends up cloud/token-based) • Your inference engine, e.g. Ollama, which is great for running OSS GPT models locally With this, you’d have an offline, private, and blazing-fast personal dev+AI environment. LEANN in particular is built exactly for this kind of setup — tiny footprint, semantic search over your entire local world, and Claude Code/ Cursor –compatible out of the box, the ollama for generation. I

Thank you for the pointer to LEANN! I've been experimenting with RAGs and missed this one. I am particularly excited about using RAG as the knowledge layer for LLM agents/pipelines/execution engines to make it feasible for LLMs to work with large codebases. It seems like the current solution is already worth a try. It really makes it easier that your RAG solution already has Claude Code integration![1] Has anyone tried the above challenge (RAG + some LLM for working with large codebases)? I'm very curious how it goes (thinking it may require some careful system-prompting to push agent to make heavy use of RAG index/graph/KB, but that is fine). I think I'll give it a try later (using cloud frontier model for LLM though, for now...) [1]: https://github.com/yichuan-w/LEANN/blob/main/packages/leann-...

This is fantastic work. The focus on a local, sandboxed execution layer is a huge piece of the puzzle for a private AI workspace. The `coderunner` tool looks incredibly useful. A complementary challenge is the knowledge layer: making the AI aware of your personal data (emails, notes, files) via RAG. As soon as you try this on a large scale, storage becomes a massive bottleneck. A vector database for years of emails can easily exceed 50GB. (Full disclosure: I'm part of the team at Berkeley that tackled this). We built LEANN, a vector index that cuts storage by ~97% by not storing the embeddings at all. It makes indexing your entire digital life locally actually feasible. Combining a local execution engine like this with a hyper-efficient knowledge index like LEANN feels like the real path to a true "local Jarvis." Code: https://github.com/