Persistent memory for AI agents — 98%+ retrieval recall, 99% token savings, 44 tools
{
"mcpServers": {
"io-github-jubakitiashvili-context-mem": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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Persistent memory for AI agents — 98%+ retrieval recall, 99% token savings, 44 tools
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
License not specified.
Is it maintained?
Last commit 1 days ago. 10 stars.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
Every time you start a new AI session, your assistant has zero memory of what you built yesterday. The architecture decisions, the bugs you fixed, the preferences you stated — all gone. You spend the first 10 minutes re-explaining context.
context-mem runs in the background, captures everything automatically, and retrieves exactly the right context when you need it:
npm i context-mem && npx context-mem init
One command. Works with Claude Code, Cursor, Windsurf, VS Code, Cline, and Roo Code.
Tested on 4 academic benchmarks. All scores are session-level retrieval recall (did the correct session appear in top-k?), not end-to-end QA accuracy.
| Benchmark | Retrieval Recall | Questions | Sessions/conv | Metric |
|---|---|---|---|---|
| LongMemEval | 97.8% R@5 | 500 | ~53 | Session R@5 |
| LoCoMo | 98.1% R@10 | 1,977 | 19-35 | Session R@10 |
| MemBench | 98.0% R@5 | 500 | — | Hybrid top-5 |
| ConvoMem | 97.7% R@10 | 250 | — | Session R@10 |
| Benchmark | Retrieval Recall |
|---|---|
| LongMemEval | 100.0% R@5 (500/500) |
| Benchmark | Context Mem | MemPalace |
|---|---|---|
| LongMemEval R@5 | 97.8% | 96.6% |
| LoCoMo R@10 | 98.1% | 60.3% |
Both systems achieve 100% on LME with optional Haiku reranking. MemPalace comparison uses identical methodology (session-level, same datasets).
benchmarks/ — run them yourself with npm run bench.Every tool output flows through the pipeline: privacy screening (9 secret detectors) → parallel extraction (entities, importance, topics) → **14 co
No automated test available for this server. Check the GitHub README for setup instructions.
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