semantica/integrations/openclaw

Semantica × OpenClaw Integration

Connect OpenClaw — the open-source personal AI agent — to Semantica's full knowledge-graph and decision-intelligence stack.

Two integration paths are available:

Path	When to use
MCP (recommended)	OpenClaw Gateway is running; zero extra code needed
REST / native tool	Embedding Semantica directly in a SOUL.md agent config

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Path 1 — MCP Server (recommended)

1. Start the Semantica MCP server

python -m semantica.mcp_server

2. Add to mcporter.json

{
  "mcpServers": {
    "semantica": {
      "command": "python",
      "args": ["-m", "semantica.mcp_server"],
      "transport": "stdio"
    }
  }
}

3. Restart the OpenClaw Gateway

openclaw gateway restart

All 12 Semantica tools are now available to any OpenClaw agent:

Tool	What it does
extract_entities	Named entity recognition from text
extract_relations	Relation / triplet extraction from text
record_decision	Record a decision with causal links
query_decisions	Search recorded decisions
find_precedents	Find past decisions similar to a query
get_causal_chain	Trace cause-effect chains from a node
add_entity	Add a node to the knowledge graph
add_relationship	Add an edge between two nodes
run_reasoning	Forward-chain rules over facts
get_graph_analytics	Centrality, communities, topology stats
export_graph	Export graph (JSON, RDF, GraphML, …)
get_graph_summary	High-level graph overview

3 resources are also exposed: semantica://graph/summary, semantica://decisions/list, semantica://schema/info.

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Path 2 — Native Tool (REST)

Use OpenClawKGTool when you prefer a direct Python integration without the MCP gateway.

Install

pip install semantica[openclaw]   # pulls in 'requests'

Quick start

from integrations.openclaw import OpenClawKGTool

tool = OpenClawKGTool(base_url="http://localhost:8000")

# Extract knowledge from text
entities = tool.extract_entities("OpenClaw is an open-source AI agent built in Python.")
relations = tool.extract_relations("Alice manages the OpenClaw project at Hawksight.")

# Record and query decisions
tool.record_decision("Deploy model v2 to production", context="latency improved by 40%")
precedents = tool.find_precedents("roll back production deployment")

# Graph analytics
summary = tool.get_graph_summary()
analytics = tool.get_graph_analytics()

# Export
ttl = tool.export_graph(fmt="ttl")

Generate mcporter.json programmatically

from integrations.openclaw import OpenClawMCPConfig

cfg = OpenClawMCPConfig()
print(cfg.to_json())   # → paste into mcporter.json

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SOUL.md agent snippet

Add Semantica to any OpenClaw agent by referencing the tool in your SOUL.md:

## Tools

- name: semantica_kg
  description: >
    Semantica knowledge-graph tool. Supports entity extraction, decision
    recording, graph querying, causal chain analysis, reasoning, and
    multi-format export.
  endpoint: http://localhost:8000
  auth: none

## Instructions

You have access to `semantica_kg`. Use it to:
- Extract entities and relations from any text the user provides.
- Record important decisions and retrieve precedents before recommending actions.
- Run graph analytics and export results when the user asks for a summary.

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Requirements

• Python 3.8+
• pip install semantica (core)
• pip install semantica[openclaw] (adds requests for the REST path)
• OpenClaw ≥ latest — openclaw.ai