Skip to main content

MCP server for Neruva agent memory + reasoning substrate. v0.22 adds the AUTO-PILOT surface: agent_route_intent_prompt (Layer 2 -- 18-pattern intent classifier) + agent_reflect_prompt (Layer 3 -- self-curates substrate from recent turns). Combined with auto-extract on records_ingest (Layer 1), the agent uses the full substrate without prompting. Plus Records, 5-engine KG, federated agent_remember/recall/context, Pearl do-operator, HD analogy, CBR, ToM, EFE, rule induction, replay, code_kg_* navigation.

Project description

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 0.22.0 — Auto-pilot surface (the moat)

Two new tools complete the auto-pilot that makes the substrate use itself. The agent automatically routes user intents to the right cognitive primitive AND self-curates memory across sessions, without the user telling it which Neruva tool to call.

  • agent_route_intent_prompt — returns the canonical 18-pattern intent classifier (counterfactual / analogy / theory-of-mind / rule induction / causal / planning / recall / comparison / state / composition / decision / mistake + 6 code-graph navigation intents). Pair with NERUVA_AUTO_ROUTE=1 in neruva-record for hands-free routing on every user prompt.
  • agent_reflect_prompt — returns the canonical reflection prompt that extracts durable decisions / facts / mistakes / open questions from recent turns. Pair with NERUVA_AUTO_REFLECT=1 in neruva-record for hands-free self-curation. Next session boots with curated context, not raw transcript.

Both endpoints are pattern-C: substrate emits a prompt, caller LLM runs it in its normal turn, structured result pushed back via existing tools. Substrate stays $0/call. Combined with the existing hd_kg_extraction_prompt (Layer 1 — auto-extract on records_ingest), the three layers form a complete auto-pilot.

See neruva-record v0.11+ for the SDK that wires these into Claude Code's hook system automatically.

What's new in 0.21.0 — code-graph MCP tools

  • 5 new code_kg_* tools for sub-ms structural code queries against KGs built locally via neruva-record-code-index: code_kg_callees, code_kg_callers, code_kg_class_of, code_kg_module_of, code_kg_imports. Thin wrappers over hd_kg_query with "Call this when..." routing nudges.
  • Tool-description routing nudges. All high-leverage tools (records_*, agent_recall/context/remember, hd_kg_query, hd_analogy, hd_causal_query, agent_counterfactual_rollout, agent_model_belief(_add), agent_register_action, agent_plan_efe, agent_induce_rule, agent_extract_schema, agent_hierarchical_decode) lead with "Call this when..." so LLMs route into the right substrate primitive without explicit prompting.

What's new in 0.18.3 — depth-unlimited theory of mind + 125× faster cleanup

  • Theory of mind is now depth-unlimited (v0.5.4 substrate fix). Position-tagged at every chain index via non-commutative permutation binding. Inner-position swaps correctly reject; recursive self- reference (same agent at multiple chain positions) works natively.
  • Cleanup acceleration via FAISS-binary popcount. OPB query stage 2 uses SIMD popcount over sign-quantized atoms with deterministic float32 cosine rerank. Substantially faster on warm queries; replay bit-identical.
  • 551× compression on stored OPB pages (rank-12 SVD). Persistence blobs that were >100 MB now fit in under 1 MB at perfect recall on round-trip.

The 9-level cognitive ladder — no LLM vendor ships rows 3-9

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) agent_model_belief_add · agent_model_belief hallucinates at depth
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 agent_induce_rule fine-tune (many examples)
8 Persistent rule storage 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 a graduated, production-shipped engine. No published memory vendor offers more than rows 1-2. Substrate-augmented small LLMs can match frontier-class agentic capabilities at a fraction of the 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:b::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 BaseChatMessageHistory

Same pattern: neruva-langgraph (BaseCheckpointSaver + BaseStore), neruva-crewai (Storage interface + 3 memory flavors).

Auto-record for Claude Code

pip install neruva-record && neruva-record-install

Every 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 varies <100ms
Cost per recall vs context-stuffing varies varies dramatically lower

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neruva_mcp-0.22.1.tar.gz (30.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

neruva_mcp-0.22.1-py3-none-any.whl (26.5 kB view details)

Uploaded Python 3

File details

Details for the file neruva_mcp-0.22.1.tar.gz.

File metadata

  • Download URL: neruva_mcp-0.22.1.tar.gz
  • Upload date:
  • Size: 30.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.9

File hashes

Hashes for neruva_mcp-0.22.1.tar.gz
Algorithm Hash digest
SHA256 2b4cdb18c4357ba19d756ec56ccc056036964700772a1bad597e0e7abf3d355b
MD5 6420168bf894f728f25e7176a6af9b7c
BLAKE2b-256 b1a28deaa8992c62e4667330cb84501dac823bc43d0cfd2992ebca4398fca1cf

See more details on using hashes here.

File details

Details for the file neruva_mcp-0.22.1-py3-none-any.whl.

File metadata

  • Download URL: neruva_mcp-0.22.1-py3-none-any.whl
  • Upload date:
  • Size: 26.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.9

File hashes

Hashes for neruva_mcp-0.22.1-py3-none-any.whl
Algorithm Hash digest
SHA256 66fae9f6c750dbef3e0c34c23e395daf4f913a068bb41033c584be7131a2f487
MD5 6aa2f0f7483b0108f1f8b07e70208ed5
BLAKE2b-256 689498c57483cc5dd95fc36088e61cd5948b4f06b1f7ff1e5d52bea4fd4c1b85

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page