mnemostack
Self-hosted hybrid memory & retrieval for AI apps.
mnemostack is a durable retrieval layer over your own Qdrant (and optional Memgraph): semantic, keyword (BM25), temporal, and graph recall, fused with Reciprocal Rank Fusion and refined by an 8-stage ranking pipeline — with payload filters for multi-tenant isolation, optional LLM answer synthesis (confidence + citations), and an ingest path that enriches and projects structured fields. One recall(query) call, usable as a Python library, an HTTP service, or an MCP server.
Flagship use case — durable memory for AI agents. Long-running agents hit the same wall: context gets compacted, sessions restart, useful decisions disappear, and the next run pays the re-orientation tax again. mnemostack gives them a persistent memory layer to query when the context window is not enough — durable, searchable, scoped, and explainable, not just embedded and hoped for.
The same engine backs other retrieval-heavy work: RAG over mixed corpora, multi-tenant or per-user knowledge stores, and time-aware search backends — anywhere pure vector similarity falls short on its own.
