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  • License MIT

MCP server for Neruva agent memory: typed Records (decisions/mistakes/tool_calls/llm_turns, auto-embedded), 5-engine knowledge graph (Hadamard / OPB / multi-shard / quorum / feature-bundle) with managed or BYO-LLM extraction, federated agent_remember/recall/context with question-type dispatch, Pearl's do-operator causal queries, HD analogy, concept blending with provenance, CBR episode store, provable replay via agent_snapshot/restore, quorum anomaly detection, fact invalidation, portable .neruva container, sub-100ms p95. Drop-in for Claude Code / Cursor / Codex / Gemini CLI. LangChain / LangGraph / CrewAI adapters available.

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    Readme

    @neruva/mcp

    MCP server for Neruva — agent memory substrate with knowledge graph, causal reasoning, and federated context assembly. Drop into Claude Code / Cursor / Codex / Gemini CLI in one line.

    What's new in 0.16

    Capability Tool(s)
    Auto-managed entity extraction (Sonnet, server-side) agent_remember(extract="managed")
    Federated agent memory (records + KG, one call) agent_remember · agent_recall · agent_context
    Cross-session graph RAG agent_recall(namespaces=[...])
    Question-type dispatch (temporal / multi-hop / single-hop / adversarial) agent_context(question_type="auto")
    Pearl's do-operator on agent memory agent_causal_query
    Provable replay (snapshot + restore) agent_snapshot · agent_restore
    Anomaly detection on quorum KGs agent_check_consistency
    Fact invalidation (Zep-style temporal resolution) hd_kg_replace_fact
    Canonical extraction prompt (BYO-LLM) hd_kg_extraction_prompt
    5 KG engines: hadamard / opb / multi-shard / quorum / feature-bundle hd_kg_add_fact(engine=...)
    Concept blending (provenance-preserving merge) hd_blend_query
    Case-based episode retrieval hd_cbr_*

    ~70 tools across Records, KG, Causal, Analogy, CBR, Blend, Vector memory, federated agent_*, self-introspection.

    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 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 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