neruva-mcp 0.41.0

MCP server for Neruva -- reliability infrastructure for production AI agents. Agent stops forgetting / drifting / repeating mistakes. v0.27: qbound KG engine for conflict resolution where docs override LLM priors. 6 KG engines, 7-layer federated recall, counterfactual queries, HD analogy, CBR, belief tracking, code_kg_*. Free tier, no card.

neruva-mcp

MCP server for Neruva — reliability infrastructure for production AI agents. One MCP install. Your agent stops forgetting, stops drifting, stops repeating mistakes — every failure replayable bit-for-bit.

Backed by a 6-engine knowledge graph (incl. qbound for document-vs-LLM-prior conflict resolution), counterfactual queries, HD analogy, episodic CBR, deterministic snapshot/restore.

Drop into Claude Code / Cursor / Codex / Gemini CLI / Goose in one line. Free tier, no card.

For Claude Code users: see neruva.io/claude-code for the 30-second install + first-queries to try.

Benchmarks

Test	Score	What it means
Does the agent learn from mistakes?	+34 points	Same Claude Haiku model, no retraining. With Neruva running the agent climbs from 84% to 93% across 2000 tasks. Without Neruva it stays flat at 59%. Three independent runs.
Memory QA across long histories (LongMemEval)	93.3%	top-4 globally, +22pp vs Zep
Compliance + audit determinism (DFAH)	100% / 88%	first system to do both at once, 2.75× prior best
Latency (p95 cache-hit)	80ms	2.5× faster than Mem0

Full breakdown: neruva.io/benchmarks. The only memory stack hitting world-class scores on memory, reasoning, AND audit determinism — simultaneously.

What's new in 0.27.0 — qbound engine for conflict resolution

New 6th KG engine: qbound (question-conditioned binding) for workloads where document-stated facts must override the LLM's training-time priors.

Use it when your agent reads CRM records, compliance docs, or internal policies that contradict what an LLM "knows" from pretraining:

await session.call_tool(
    "hd_kg_add_facts",
    {
        "kg": "compliance",
        "engine": "qbound",  # <-- new
        "facts": [
            {"subject": "PolicyX", "relation": "current_version", "object": "v2024.3"},
            # ...
        ],
    },
)

Drop-in: same shard format as opb, same query surface. Backward-compat with all existing tools.

What's new in 0.26.0 — 7-layer federated recall

agent_recall_full lands. One question, seven buckets returned in parallel (~150ms p95):

• records — semantic embedding search (existing)
• kg — entity-overlap + cosine across knowledge graphs (existing)
• rules — HD signature cosine across stored rule libraries (new)
• cbr — structural-distance nearest-neighbour across case episode stores (new)
• scm — variable-name match across causal models (new)
• tom — name-resolved chain lookup across theory-of-mind belief stores (new)
• continual — predictive next-token recall against trained K-gram learners (new)

Records + KG work universally. The other five layers light up when you pass opt-in text labels at ingest time (chain_names/prop_name on agent_model_belief_add, token_names on agent_continual_train, var_names on hd_causal_add_worlds, axis_vocab_names on hd_cbr_add_episodes). Empty buckets return a hint field with the exact param to pass next time.

Per-layer prefix scoping (rules_prefix, cbr_prefix, scm_prefix, tom_prefix, continual_prefix, kg_prefix) bounds wall time on tenants with many resources. Existing agent_recall (records + KG only) unchanged.

What's new in 0.25.0 — qa_optimized + recency_first graduated

The LongMemEval 93.3% recipe lifted into the substrate. agent_recall now accepts two opt-in flags (both default off, backwards compatible):

• mode="qa_optimized" — 3× over-fetches the candidate pool and BM25-RRF-fuses (k=60) over the text. Lifts records whose text contains specific entity tokens (brand names, amounts, dates, IDs) that pure semantic embedding under-weights. Worth ~+20pp on memory-QA benchmarks.
• recency_first=true — re-sorts returned records by ts desc after ranking. Use for knowledge-update questions where the most recent statement wins over older contradictory ones.

Pair them for the strongest memory-QA recipe. Auto-router in neruva-record 0.19+ suggests both automatically when the intent classifier hits recall_extended.

What's new in 0.24.0 — Code-graph bare-name resolution

code_kg_module_of and code_kg_class_of now accept short identifiers like bind (no module prefix). When the substrate sees an unqualified name, it falls back to a 1-hop called_by lookup to find the likely qualified target.

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

~80 tools across Records, KG, Causal, Analogy, CBR, Blend, 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": "..." } } } }

Goose (~/.config/goose/config.yaml)

extensions:
  neruva:
    type: stdio
    cmd: npx
    args: ["-y", "@neruva/mcp@latest"]
    env:
      NERUVA_API_KEY: nv_...

For Goose auto-pilot (pattern-C route / reflect / extract via your LLM): pip install neruva-goose.

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 6 engines (hadamard, opb, qbound, multishard, quorum, feature_bundle), 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	Mem0	Neruva
KG engines	0	1	1	6
Counterfactual queries	❌	❌	❌	✅
Provable replay (deterministic snapshot/restore)	❌	❌	❌	✅
Anomaly detection (quorum disagreement)	❌	❌	❌	✅
Federated context (records+KG one call)	❌	partial	partial	✅
Portable container	❌	❌	❌	✅ .neruva
p95 latency	varies	varies	varies	<100ms
Cost per recall vs context-stuffing	varies	varies	varies	dramatically lower

KG engine selector

Pick engine on first hd_kg_add_facts call to a new KG:

engine	best for	storage
hadamard (default)	small KGs (<10k facts), latency-critical	32 KB/shard
opb	large KGs (>10k facts), matrix-power N-hop derivation	256 MB/shard
qbound	conflict-resolution where documents override LLM priors	similar to opb
multishard	very large KGs, sharded across K=16 hadamard buckets	scales linearly
quorum	adversarial/anomaly detection via n-shard quorum	n × hadamard
feature_bundle	typed-feature workloads (color, size, role)	128 features × D

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