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Teams evaluating AI models, assistants and generative workflows, particularly RAG and vector search, search and retrieval β free to start, source available on GitHub.
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Catalogue record
github.com
π π π πͺ π§ - Durable hybrid memory for AI agents. Combines vector search, BM25, temporal retrieval, and optional Memgraph knowledge graph via...
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π π π πͺ π§ - Durable hybrid memory for AI agents. Combines vector search, BM25, temporal retrieval, and optional Memgraph knowledge graph via reciprocal rank fusion. 6 MCP tools: health, search, answer, feedback, graphquery, graphaddtriple. Self-hosted with Qdrant backend. 82.5% strict accuracy on LoCoMo benchmark. pip install 'mnemostack[mcp]'
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Teams evaluating AI models, assistants and generative workflows, particularly RAG and vector search, search and retrieval β free to start, source available on GitHub.
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Evidence summary
Why this fit
Teams evaluating AI models, assistants and generative workflows, particularly RAG and vector search, search and retrieval β free to start, source available on GitHub.
Known limitations
Teams that need a vendor SLA before adopting a free tool β check the project's support terms first.
Catalogue confidence
Catalogue data checked Oct 27, 2026.
GitHub-backed record with source-attributed context.
Repository context
GitHub / udjin-labs/mnemostack
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