Graph-MIND
Your AI forgets everything when the session ends. This one remembers, and it never calls an LLM to do it.
88.8% on LongMemEval. Zero model tokens spent saving memory. Runs on your own PC.
Decide something in Claude Code on Monday. Ask Codex on Wednesday. It knows.
Install
Windows: download Graph-MIND-Setup.cmd and double-click it, or in PowerShell:
irm https://raw.githubusercontent.com/goyohan0611-png/graph-mind/main/scripts/install.ps1 | iex
macOS / Linux:
curl -LsSf https://raw.githubusercontent.com/goyohan0611-png/graph-mind/main/scripts/install.sh | sh
No Python needed; nothing else to set up. Restart your AI apps afterwards. Already have Python?
pip install graph-mind-memory && graph-mind-install. More: docs/install.md.
What it does
- Remembers every conversation with Claude Code, Codex and Claude Desktop, word for word, on your own PC. Saving is a database write: no model call, no tokens.
- Any AI picks up where another left off. When a question depends on the past, the model calls Graph-MIND over MCP and gets back the few thousand tokens that answer it. Korean and English.
- One memory across your PCs. Tell the AI "let my other PCs use this memory", paste the code it gives you to the AI on the other PC. Office network or Tailscale, over TLS, every line encrypted before it leaves a PC.
- Yours. No account, no cloud of its own, no telemetry. Secrets are masked before anything is stored, and "delete the password I typed earlier" deletes it everywhere. docs/privacy.md
- Teams, if you want them. Share a project's decisions and progress with teammates' AIs, never your personal memory; files stay in Slack and GitHub. docs/teams.md
Does it work?
Same 40 questions, same answer model, same official judge, pre-registered before anyone looked:
| accuracy | model tokens spent saving memory | |
|---|---|---|
| Graph-MIND | 85.0% | 0 |
| MemPalace 3.10.0 | 57.5% | 0 |
| Mem0 2.2.1 (open source) | 52.5% | ~640,000 per question |
On all 500 LongMemEval questions: 88.8%. Every question, answer and verdict is in
research/runs/; method, failures and how to rerun it:
docs/benchmarks.md.
More
MCP tools · See your memory in 3D
(graph-mind-view) · Changelog · Security ·
Development: pip install -r requirements.txt, then python -m unittest discover -s tests
Contributions are welcome under the CLA. License: AGPL-3.0; a commercial license is available for products that cannot publish their changes.
1 maintainer
Metadata
1 maintainer
Release files for graph-mind-memory 0.8.0
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