neruva-mcp
Reliability for AI agents — and for multi-agent operations. The Python MCP server for Neruva. Your agents stop repeating mistakes, stop spinning in circles, remember across every session, and get more reliable the longer they run. Every run is replayable bit-for-bit for audit. Bring your own LLM. Free tier, no card.
neruva.io · get a key at app.neruva.io
It just works — that's the point
Neruva is push, not pull. It runs silently in the background — no tools to learn, no glue code. Before each action it surfaces the past mistake (and the fix that worked) so the agent does it right; it breaks loops before they burn your budget; it records everything for replay. The agent never spends a token deciding to "use memory." The automatic operation is the moat — pull-based memory tools can't match it because they require the agent to stop and ask.
What you get
- Stops repeating mistakes — past mistake + working fix surfaced automatically; a known-destructive repeat is blocked so the agent self-corrects.
- Stops spinning — detects loops/stalls on a failing approach and breaks them.
- Remembers across sessions and projects — persistent memory that builds itself from what your agents do.
- Provable + replayable — deterministic from a seed; reproduce any run bit-for-bit for audit. Export the whole memory as one portable
.neruvafile. - Gets more reliable over time — learns from your agents' own history, no retraining, no weight changes.
- Built for multi-agent operations — keeps shared memory consistent and catches an agent that's wrong or lying before it poisons the others. In a swarm, one unreliable agent compounds at every handoff; Neruva is the reliability layer underneath.
Install
pip install neruva-mcp
This installs the Neruva MCP server. Point your MCP host at it with your key:
{
"mcpServers": {
"neruva": {
"command": "neruva-mcp",
"env": { "NERUVA_API_KEY": "nv_..." }
}
}
}
For the silent auto-pilot hook (background mistake-recall, loop-break, recording) in Claude Code, install the companion: pip install neruva-record && neruva-record-install. Get a free key at app.neruva.io (no card).
The tool surface
A small, focused set: typed Records (ingest/query/timeline/get/update/delete, GDPR forget, compact, export/import), federated recall (agent_recall), the memory knowledge graph (hd_kg_*), snapshot/restore for replay, multi-agent consensus, and op stats. Bring your own LLM — the substrate stays deterministic and $0/call server-side.
Proof (cited honestly on neruva.io/benchmarks)
| Test | Result | Plain meaning |
|---|---|---|
| Learns from mistakes | +34 pts | Same model, no retraining: 84%→93% over 2000 tasks vs flat 59% without. |
| Long-history memory (LongMemEval) | 93.3% | Top-tier on the standard agent-memory benchmark. |
| Replay determinism + accuracy (DFAH) | 100% / 88% | First to hit both at once. |
| Recall latency (p95, cache hit) | ~80ms | Answers from memory in well under a tenth of a second. |
Config
Set NERUVA_API_KEY. NERUVA_URL defaults to https://api.neruva.io.
License
MIT
1 maintainer
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