Enables coding agents to query local notes, decisions, docs, and code with hybrid retrieval (BM25 + embeddings + reranking) and get path:line citations. It provides tools like rag_query for full-corpus search and search_knowledge for project-scoped knowledge recall.
Local-first memory and retrieval for private project knowledge. Enables indexing files, searching, and asking questions about project documents using local embeddings and LLM.
Enables AI coding agents to maintain persistent memory across repositories by indexing local files into a semantic graph and providing ranked, verified search results without LLM token costs.
Enables AI coding agents to build and query local repository memory, returning ranked files, context packs, relationship maps, semantic recall, and feedback-tuned search without uploading code.
Provides AI coding agents with persistent, long-term memory through local semantic search and SQLite storage. It enables agents to save and retrieve architectural decisions or project context across different conversation sessions without requiring cloud services.