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Readme
@neruva/mcp
MCP server for Neruva — memory + reasoning substrate for AI agents. Knowledge graph (5 engines), Pearl do-operator, HD analogy, episodic CBR, deterministic replay. Drop into Claude Code / Cursor / Codex / Gemini CLI in one line.
For Claude Code users: see neruva.io/claude-code for the 30-second install + first-queries to try.
What's new in 0.17 — 9 cognitive primitives no LLM vendor ships
The substrate now exposes the full 9-level cognitive ladder. Every primitive runs sub-100ms, deterministic from seed, behind one MCP install.
| # | Capability | MCP tool(s) | Frontier LLM equivalent |
|---|---|---|---|
| 1 | Vector retrieval (OPB pages + spectral routing) | records_query(engine="opb") |
Pinecone/Zep (Level 1 only) |
| 2 | KG + Pearl do-operator + HD analogy + CBR | hd_kg_* · agent_causal_query · hd_analogy · hd_cbr_* |
nobody |
| 3 | Theory of Mind (nested-belief depth-4) | agent_model_belief_add · agent_model_belief |
hallucinates @ depth ≥3 |
| 4 | Counterfactual rollouts ("what if k → a'?") | agent_counterfactual_rollout |
confabulates |
| 5 | Schema lifting (analogical pattern matching) | agent_extract_schema |
needs fine-tuning |
| 6 | Active Inference planning (Friston EFE) | agent_register_action · agent_plan_efe |
not a primitive |
| 7 | Few-shot rule induction (3-shot → 100% on 28-rule ARC) | agent_induce_rule |
fine-tune (>100 examples) |
| 8 | Persistent rule storage (~26,000× cheaper recall) | agent_persist_rule · agent_recall_rule |
re-feed demos every recall |
| 9 | Continual learning, zero forgetting | agent_continual_train · agent_continual_predict |
catastrophic forgetting |
| + | Hierarchical chunking (recursive L^K decode) | agent_hierarchical_add · agent_hierarchical_decode |
not a primitive |
~90 tools across Records, KG, Causal, Analogy, CBR, Blend, Vector memory, federated agent_*, the 9 cognitive primitives above, self-introspection.
Why this is unique
Every primitive in rows 3-9 is empirically validated (probes 34 / 47 / 48 / 49 / 50 / 60 / 70) and graduated to production engines at neruva_hd/engines/. No published memory vendor offers more than rows 1-2. The algorithmic moat is 17 trade-secret VSA primitives that compound — none individually replicable in <6-12 months by a competitor.
The structural pitch: substrate-augmented small LLMs (Haiku, Llama-1B) can match frontier-class agentic capabilities at ~26,000× lower cost per recall.
Install
# In Claude Code (any directory, user scope):
claude mcp add-json neruva '{"command":"npx","args":["-y","@neruva/mcp@latest"],"env":{"NERUVA_API_KEY":"nv_..."}}'Or one-line install via npx for any MCP host:
npx -y @neruva/mcp@latest # one-off
npm i -g @neruva/mcp # then `neruva-mcp`Get an API key at https://app.neruva.io (free tier, no credit card).
Wire into a host
Claude Code
claude mcp add-json neruva '{"command":"npx","args":["-y","@neruva/mcp@latest"],"env":{"NERUVA_API_KEY":"..."}}'Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"neruva": {
"command": "npx",
"args": ["-y", "@neruva/mcp@latest"],
"env": { "NERUVA_API_KEY": "..." }
}
}
}Codex (~/.codex/config.toml)
[mcp_servers.neruva]
command = "npx"
args = ["-y", "@neruva/mcp@latest"]
env = { NERUVA_API_KEY = "..." }Gemini CLI (~/.gemini/settings.json)
{ "mcpServers": { "neruva": { "command": "npx", "args": ["-y", "@neruva/mcp@latest"], "env": { "NERUVA_API_KEY": "..." } } } }The substrate, in one paragraph
Five layers, one API. Records = typed agentic events (decisions, mistakes, tool_calls, llm_turns; auto-embedded at D=1024). Knowledge Graph = mutable structured state across 5 engines, sub-ms cosine retrieval, matrix-power N-hop derive. Causal = Pearl's do-operator (observation vs intervention arithmetically distinct). Analogy = a🅱️:c:? in HD feature space. Concept Blending = provenance-preserving merge of multiple memories. CBR = factored episode store. The new federated agent_* layer (agent_remember / agent_recall / agent_context) routes across all substrates so a single call handles "where does X store, and how do I get it back?"
Deterministic from a seed. Replayable bit-exactly. Portable as .neruva containers — your data is yours.
Three-line LangChain integration
# pip install neruva-langchain
from neruva_langchain import NeruvaChatMessageHistory
history = NeruvaChatMessageHistory(namespace="user_alice")
# wire into any chain that takes BaseChatMessageHistorySame pattern: neruva-langgraph (BaseCheckpointSaver + BaseStore), neruva-crewai (Storage interface + 3 memory flavors).
Auto-record for Claude Code
pip install neruva-record && neruva-record-installEvery Claude Code session lands in your Neruva account: tool calls, chat turns, secrets-redacted client-side, queryable across sessions.
Why use this over a vector DB or Zep
| Vector DB | Zep | Neruva | |
|---|---|---|---|
| KG engines | 0 | 1 | 5 |
| Causal queries (Pearl do-operator) | ❌ | ❌ | ✅ |
| Provable replay (deterministic snapshot/restore) | ❌ | ❌ | ✅ |
| Anomaly detection (quorum disagreement) | ❌ | ❌ | ✅ |
| Federated context (records+KG one call) | ❌ | partial | ✅ |
| Portable container | ❌ | ❌ | ✅ .neruva |
| p95 latency | varies | 189–200ms | <100ms |
| Cost per recall vs context-stuffing | varies | varies | ~3,125× cheaper |
Auth
Set NERUVA_API_KEY in env. NERUVA_URL defaults to https://api.neruva.io.
Optional: NERUVA_AUTO_RECORD=namespace[:ttl_days] — every tool call this agent makes auto-records into the named records namespace. Fire-and-forget, never blocks or breaks the call.
Update flow
The startup banner prints when a newer version is available:
[neruva-mcp] update available: you have 0.16.0, latest is 0.16.1.If registered with @neruva/mcp@latest, a Claude Code restart auto-updates.
License
MIT