🧠 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 recalled by a different agent in a new session, on a different machine. Captioned throughout, so it reads fine muted.

https://github.com/user-attachments/assets/09ab194a-d2a0-4fe5-a7db-69ae8225e39b

_{Player not loading? Download the video to play locally.}

⚡ Quickstart

pip install m3-memory   # or: pipx install m3-memory — pick ONE and stay with it
m3 setup            # detects your agents, wires the MCP server, provisions the local embedder
m3 doctor           # verify: health, memory count, embedder, and which agents got wired

That's the whole install. No cloud account, no API key, no external embedding service.

What it does, in four lines

Save a decision — from any agent, or straight from the shell:

$ m3 memory memory_write --type decision --title "auth-jwt-algorithm" \
    --content "The auth service uses RS256 JWTs. HS256 was rejected because we need asymmetric verification at the edge."
"Created: 84a944fb-ef3e-403b-9240-f53ab3c015f7"

Next week, in a different agent, on a different model — ask in your own words:

$ m3 memory memory_search --query "which signing algorithm did we pick for tokens?" --k 3
Top 1 results:
----------------------------------------
1. [84a944fb-ef3e-403b-9240-f53ab3c015f7] score=0.7501  type: decision  title: auth-jwt-algorithm
Content:
The auth service uses RS256 JWTs. HS256 was rejected because we need asymmetric verification at the edge.
----------------------------------------

The query shares no keywords with the stored text — no "RS256", no "JWT" — and still finds it. That's the hybrid engine: BM25 for exact terms, local BGE-M3 vectors for meaning, MMR for diversity. Your agent calls the same tools over MCP, so it recalls this automatically instead of asking you again.

New here? The 5-Minute Getting Started Guide walks the same path with more context, and Core Tools lists the five you'll use most.

🧩 Beyond the core

The Quickstart above is the whole product for most people: shared memory, wired into your agents, working offline. Everything below is optional surface you can ignore until you want it — each row says what it costs to turn on.

_{Also a drop-in memory backend for LangChain / LangGraph, CrewAI, and PydanticAI — see the framework guides.}

Every path gains automatic contradiction supersession, bitemporal historical queries, local sovereign embedding, and the full 100+ MCP tool set.

⚖️ How M3 Compares

A full, feature-by-feature comparison table — M3 vs Mem0, Letta, Zep, Graphiti, LangChain Memory / LangMem, agentmemory, Chronos, Hindsight, Mastra OM, Memento, and more — with sourced benchmarks and honest "when to choose the other tool" guidance, lives in COMPARISON.md.

Short version: M3 is the local-first, MCP-native option that stays yours and works across every agent — where cloud services (Mem0), full agent runtimes (Letta), and graph-database systems (Zep, Graphiti) each ask you to adopt their infrastructure. See the comparison guide for the row-by-row detail.

🚀 Quick Links & Badges