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teflon07/memkeeper

by Various

Local-first, deterministic memory store for AI agents: SQLite-backed BM25 with optional local ONNX semantic search and cross-encoder rerank, graph projection, and an MCP adapter. N

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teflon07/memkeeper

Added 3 Sept 2026

#ai-agents#embeddings#knowledge-graph#llm#local-first#mcp#memory#model-context-protocol
Catalogue record, not a reviewed recommendation. This record can include automatically collected descriptions and setup details. Confirm important claims with the original source. Explore source-reviewed tools.

Overview

Local-first, deterministic memory store for AI agents. Uses SQLite-backed BM25 with optional local ONNX semantic search and cross-encoder rerank, plus graph projection and an MCP adapter. Runs without any required network or LLM.

Best for

Best for
Developers building local-first AI agents that need deterministic memory without cloud dependencies.

Use cases

  • Give AI agents persistent local memory without external APIs
  • Run hybrid retrieval combining BM25 and offline semantic reranking
  • Expose agent memory to MCP-compatible clients

How to use

Tools exposed

  • memkeeper-core
  • memkeeper-store
  • memkeeper-embed
  • memkeeper-protocol
  • memkeeper-cli

Clients listed in this record (not verified here)

Claude Code, Cursor, ChatGPT

Notes

2 stars on GitHub. Last updated 2026-08-24. Licensed Apache-2.0.

Pros

  • Fully offline with no required network or LLM
  • Deterministic behavior for reproducible agent memory
  • Rust implementation for performance and safe concurrency

Cons

  • Very early stage with only 2 GitHub stars
  • Requires Rust toolchain to build or integrate
  • ONNX semantic search needs local model setup
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