m3

![M3 Memory]

M3 Memory

Persistent, local memory for MCP agents.

"Wait, you remember that?" — Stop re-explaining your project to your AI. Give it a long-term brain that stays 100% on your machine.

🚀 New to M3? Start here with our 5-minute "Human-First" guide.

Works with Claude Code, Gemini CLI, Aider, OpenCode, and any MCP-compatible agent. Quick one-line command to have your agent install chat log sub-system which saves verbatim chat log info, before compaction, with zero lag/latency and 100% retrieval recall. Just tell your AI agent "install m3-memory chat log sub-system" and your agent will automatically install it with all the proper hooks with some minimal customization questions from you (you can accept the default answers).

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📦 Install

curl -fsSL https://raw.githubusercontent.com/skynetcmd/m3-memory/main/install.sh | bash

Installs on macOS or Linux with the single command above. Use this to install on Windows. Use this link to install manually and this to examine the script and what it does.

Claude Code users can also install as a plugin instead — gets you 15 /m3:* slash commands, a memory-curator subagent, and auto-wired hooks:

/plugin marketplace add skynetcmd/m3-memory
/plugin install m3@skynetcmd

Plugin reference · Claude.ai (web/desktop) connector

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Add to your MCP config:

{
  "mcpServers": {
    "memory": { "command": "mcp-memory" }
  }
}

Requires a local embedding model. Ollama maybe the easiest:

ollama pull qwen3-embedding:0.6b && ollama serve

I personally use LM Studio for it's support for Apple's MLX. But, it's your preference.

Qwen3-Embedding-0.6B (1024-dim, Q8 quantized, ~639 MB) is the model M3 Memory is tuned for. But you can set and use other embedders as you wish. For example, you could use nomic-embed-text (768-dim) which also works (with minimal fidelity loss) — set EMBED_MODEL=nomic-embed-text in your environment.

As mentioned, you can use Ollama or LM Studio — load an embedding model and start its server.

Want auto-classification, summarization, and consolidation? Load a small chat model alongside the embedder (e.g. qwen2.5:0.5b via Ollama, or any 0.5–1B instruct GGUF in LM Studio / llama.cpp). M3 auto-selects it; embedding-only features work without it. See docs/QUICKSTART.md → Optional: load a small chat model.

Restart your agent. Done!

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🔮 What happens next (benefits of use)

You're at a coffee shop on your MacBook, asking Claude to debug a deployment issue. It remembers the architecture decisions you made last week, the server configs you stored yesterday, and the troubleshooting steps that worked last time — all from local SQLite, no internet required.

Later, you're at your Windows desktop at home with Gemini CLI, and it picks up exactly where you left off. Same memories, same context, same knowledge graph. You didn't copy files, didn't export anything, didn't push to someone else's cloud. Your PostgreSQL sync handled everything in the background the moment your laptop hit the local network.

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💡 Why this exists