Neuromcp
Semantic memory for AI agents with hybrid search, knowledge graph, and consolidation
- Transport
- Not stated
- Package
- —
- Registry id
- io.github.AdelElo13/neuromcp
No install snippet on purpose. A working MCP config is a command, its arguments and an environment block — the last two are where API keys live, so this catalogue never stores them and cannot publish them. Follow the link above for the authors' own instructions.
Any model. Your memory. Stays local.
neuromcp is the first Sovereign Memory layer for AI: an open-source MCP server that gives Claude, GPT, Gemini, and Ollama persistent, searchable memory — stored entirely on your machine. No API keys. No cloud sync. No subscription required to remember who you are.
Sovereign Memory = data that you own outright, lives on hardware you control, and is portable across every model you use. Cloud memory products own your data; Sovereign Memory means you do.
npx neuromcp-init # one command: detects your MCP clients, writes configs, sets up the wiki
Or run the bare server without any setup: npx neuromcp. Something not
working? npx neuromcp-doctor diagnoses the daemon, Ollama, embeddings
and the database in one run.
The built-in memory browser: the most-connected entities in a namespace
and the relations between them, as a force-directed graph you can drag,
zoom and click — served from localhost, never from a cloud. See
Memory browser & Obsidian.
Why neuromcp
The LLM is a commodity. Your memory is the moat. GPT-5, Claude 4, Gemini — they all converge. The model you use next year will differ. The memory of every conversation, decision, and preference you build is yours. neuromcp keeps that layer on your machine and makes it portable across any MCP-compatible client.
Local-first is a design choice, not a limitation. No telemetry. No data leaves your laptop. No vendor has a copy of your conversations. Audit every line of code that touches your memory. SQLite + local embeddings; everything fits on one disk.
One install. Every client. Claude Desktop, Cursor, Windsurf, Codex CLI, Continue, LibreChat, Open WebUI — neuromcp speaks MCP, so it works wherever MCP is supported. Switch models tomorrow; your memory follows.
Real recall, not keyword matching. Hybrid retrieval combines vector search (nomic-embed-text, 768-dim), BM25 full-text, graph links, and a learned usefulness prior. At 500 distractors on LongMemEval, R@5 holds at 93.3%. Your context window gets the right memory, not just the most recent.
LongMemEval-S accuracy
| Run | Score | Sample | Config |
|---|---|---|---|
| v7 (current) | 96.08% (98/102) | n=102 | Opus generator + Opus judge, single-model |
| v6 | 95.10% (97/102) | n=102 | Same as v7, prior hint set |
Repro: OMB_ANSWER_LLM=claude OMB_ANSWER_MODEL=opus OMB_JUDGE_LLM=claude OMB_JUDGE_MODEL=opus uv run omb run --dataset longmemeval -s s -m neuromcp -c "single-session-user,single-session-assistant,multi-session,temporal-reasoning,knowledge-update,single-session-preference" --query-limit 17
Sample size honesty. n=102 (17 per category × 6 categories). Wilson 95% CI for 98/102 ≈ 90.5–98.7%. Full 500q run with the same config is the next milestone before any "top-tier" claim.
Benchmarks (v0.18.0)
Oracle split (clean — easy mode)
| Mode | R@5 | R@10 | Hit Rate |
|---|---|---|---|
| Extracted (hybrid) | 100% | 100% | 100% |
Oracle-split LongMemEval isolates the correct memory in a small corpus. Every local MCP memory system claims ~99% here. It measures "does the ranker work on clean inputs" — nothing more.
Distractor split (v0.18.0, honest)
From the project's README.