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neruva-goose

Goose (Block Inc) extension for the Neruva agent memory + reasoning substrate.

Two integration paths -- use one or both:

Path 1: @neruva/mcp as a Goose MCP extension (zero Python)

Drop the Neruva MCP server straight into Goose's extension config. You get every Neruva substrate tool (records, KG, causal, analogy, CBR, ToM, EFE planning, code graph, replay) inside Goose with no glue code.

~/.config/goose/config.yaml:

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

Restart Goose. Run goose mcp list -- you should see ~80 Neruva tools.

Path 2: neruva-goose Python package (auto-pilot)

For pattern-C auto-pilot (route intent / reflect / extract running through your LLM, with substrate emitting only the prompts), install this Python package and wire its hooks into your Goose extension.

pip install neruva-goose
from neruva_goose import NeruvaAutoPilot, NeruvaAutoPilotHooks

ap = NeruvaAutoPilot(
    api_key="nv_...",
    namespace="my_session",
    llm_callable=lambda prompt: my_llm.complete(prompt),
)
hooks = NeruvaAutoPilotHooks(ap, reflect_every=10)

# Pre-message: classify intent so you can pick the right Neruva tool
intent = hooks.before_user_message(user_text)
print(intent)
# -> {"primary_intent": "counterfactual", "confidence": 0.87,
#     "suggested_tool": "agent_counterfactual_rollout", ...}

# Post-tool: auto-extract facts to the auto-KG, fire reflection every 10
hooks.after_tool_call("agent_recall", str(tool_output))

# Optional: buffer assistant turns
hooks.after_assistant_message(assistant_text)

# End of session: force a final reflection
hooks.after_session(force=True)

The three layers:

Layer Hook Substrate prompt What happens
1 after_tool_call inline extraction prompt Triples extracted from tool output, pushed to <ns>-auto-kg.
2 before_user_message /v1/agent/route_intent_prompt Returns top intent + suggested Neruva tool.
3 after_session / every N tool calls /v1/agent/reflect_prompt Decisions / facts / mistakes / open_questions written to substrate.

Pattern-C, $0/call

The substrate never runs your LLM. It hands back canonical prompts; your code runs them; structured results come back via the existing client. That keeps the substrate deterministic and free at the call boundary, and means your LLM provider, model choice, and rate limits are entirely yours.

License

MIT. Same as neruva-mcp, neruva-record, neruva-langchain, neruva-langgraph, neruva-crewai.

Metadata

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