Store and retrieve semantic memories using advanced vector search and intelligence-driven insight extraction. Organize knowledge across projects with secure access controls and comprehensive system health monitoring. Optimize your information flow by detecting duplicates and analyzing usage patterns for better decision support.
In-memory vector search API for AI agents. Store documents and query by semantic meaning using TF-IDF vectorization with cosine similarity. Lightweight alternative to Pinecone/Weaviate for small datasets. Tools: data_vector_search. Use this for building simple RAG systems, document matching, or semantic search over small collections (< 10K docs). IMPORTANT: For web-wide search, use web_search_query instead. Returns: {results[], scores[], matchCount}. No API key required —
Permission-aware retrieval for AI systems: policy-enforced access to organizational knowledge.
Long-term memory for AI assistants. Hybrid retrieval, query expansion, auto-topics.
Managed Apache Solr for agents: hybrid BM25+kNN search, server-side embeddings, RAG answers
Local-first shared memory and task coordination for AI coding agents. Go daemon plus headless CLI.
Local Markdown/Obsidian knowledge substrate for MCP agents with governed memory and hybrid search.
Codebase knowledge graph for AI agents — 159 languages, sub-ms queries, 99% fewer tokens.
Governed graph-native agent memory: knowledge extraction, fusion, hybrid RAG, scoped access tokens.
Local-first persistent memory for AI agents — SQLite + local embeddings, MCP-native, no cloud.
Encrypted-first embedded database with vector search and agent memory, exposed as MCP tools
Search engine that combines vector search, full-text search, and filters on object storage. Bring your own API key from the [turbopuffer dashboard](https://turbopuffer.com/dashboard).