MCP Fact Graph

Positioning: mcp_fact_graph is an internal component of the MakeMind knowledge stack exposed through the mcp_knowledge facade. Application code should import package:mcp_knowledge/mcp_knowledge.dart — the symbols declared here are re-exported from there. Direct package:mcp_fact_graph/ imports remain valid for advanced or integration scenarios but are discouraged in product code.

A temporal knowledge graph for evidence-based fact management with candidates, summaries, and skill-execution claims. The persistence and query backbone of the MakeMind knowledge stack.

Architecture

A 4-layer model:

L0 Evidence — raw fragments and evidence ingested from sources.
L1 FactGraph — entities, candidates, facts, relations, fact clusters, classifications, fact policies, automations, runs, artifacts.
L2 ContextOps — context bundles, summaries, claims, response validation.
L3 SkillOps — patterns, skills, rubrics, evaluation runs.

Cross-cutting domain entities reduce LLM calls (extraction rules / validators, classifier memory, disambiguation decisions, LLM call log, idempotency records).

Contract Layer

Implements mcp_bundle standard ports through capability-named adapters under src/adapters/. Persistence is unified via UnifiedStoragePort. Hosts wire the adapters they need; the legacy per-domain ports are gone.

Quick Start

import 'package:mcp_fact_graph/mcp_fact_graph.dart';

final graph = FactGraphRuntime(
  storage: InMemoryUnifiedStorage(),
);

await graph.ingestEvidence(myEvidence);
final candidates = await graph.candidatesFor(entityId);
final bundle = await graph.contextBundleFor(entityId);

Support

License

MIT — see LICENSE.