{"repo":"skynetcmd/m3-memory","free":true,"listed":false,"github":"https://github.com/skynetcmd/m3-memory","clone":"git clone https://github.com/skynetcmd/m3-memory.git","description":"Local-first Memory Framework for AI Agents · 99.2% LongMemEval-S retrieval @ k=10 · Supports Claude · Antigravity · LangChain · Hermes · Gemini · OpenCode · OpenClaw · MCP-native and plugins · Hybrid search (FTS5 + vector + MMR) · GDPR · FIPS 140-3 ready · 100% local (fully offline) or cloud capable","language":"Python","stars":21,"topics":["agentic-memory","ai-agents","claude-code","gdpr","gemini-cli","local-llm","mcp","rag","vector-search","ai-memory"],"license":"Apache-2.0","category":"ai-agents","readme_excerpt":"🧠 M3 Memory A memory layer that outlives your agents. You switch from Claude Code to Cursor, upgrade your model, start fresh next week — and everything your tools learned about your project is gone. You re-explain the same decisions, the same preferences, the same hard-won context, over and over. M3 fixes that. It's a private, local-first memory your agents share and build on — so your project's knowledge accumulates instead of resetting every time the agent does. One memory store, on your machine, that your tools and agents read from and write to — whether that's Claude Code, Cursor, Gemini CLI, or any MCP-compatible agent. Under the hood, M3 treats agent memory as a distributed-systems infrastructure problem , not a simple retrieval feature — a shared, evolving, bitemporal, contradiction-aware knowledge base that multiple heterogeneous agents and machines read and write, built to stay consistent over months and years. It runs where your data has to stay. A single pip install with no account, no API key, and no outbound calls — at home in a homelab , on a corporate or government network , or fully air-gapped . The embedder runs in-process and local, the store is a file you own, and installation works with no internet at all. On the metric that isolates the memory layer — retrieval accuracy, no answer model or judge involved — M3 reaches 99.2% session-hit-rate @ k=10 and 100% @ k=20 on LongMemEval-S. --- 🎬 Quick video overview One decision saved from a conversation, then re","default_branch":null,"files":null,"tree":[],"storefront":"/r/skynetcmd","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/skynetcmd/m3-memory/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}