Knowledge Graph
How CortexDB constructs an entity graph natively from the event log, connecting isolated fragments into connected context.
The Knowledge Graph is an entity graph constructed natively from CortexDB's lossless, event-sourced memory: nodes are entities (people, projects, customers, incidents, …) and edges are the relationships between them. It is not a separate store — it is an emergent view of Facts and Beliefs.
Why connected context matters
Pure vector search treats facts as isolated text fragments and breaks down on relationship questions like "who owns this service?" or "what changed before this incident?" A graph lets an agent traverse relationships and surface implicit workflows and ownership that span multiple isolated conversations.
How CortexDB models graph memory
The entity graph is derived entirely from the event stream. Entities emerge as records are observed, and relationship edges form as predicates are ingested. Edges carry the same bi-temporal validity window as Facts, and traversal respects hierarchical scopes — an agent can't traverse edges it isn't authorized to see.
(person: Priya Rao) ──[works_at, valid 2024-01→ ]──→ (org: Acme Corp)
│
└─[owns, valid 2026-02→ ]──→ (project: Q3 Renewal) ─[has_status, "negotiating"]Graph traversal is an internal channel, not a request parameter
There is no client-supplied graph block on /v1/recall in v0.9.9. Passing a
graph: { seed_entities, max_hops, predicates, as_of } body → 422 "unknown field graph" (verified,
including with the graph store enabled). Graph traversal, when active, is an internal fused retrieval
channel — you don't drive it per-request. The diagram above illustrates what the graph encodes, not a
request you send.
Off by default (self-hosted)
The entity graph is opt-in: enable it with CORTEX_ENTITY_GRAPH=1 (it also needs enrichment/an entity
LLM to populate). config_lint notes the cost (~3× write / ~4× recall). On a default self-hosted instance
the graph channel does not run — recall is BM25 + vector + RRF fusion. See
Self-hosting defaults and Hybrid Retrieval.
What the Knowledge Graph enables
- Connected context — traverse from a customer record to related tickets, decisions, and system logs.
- Relationship discovery — surface ownership graphs and implicit workflows across conversations.
- Contextual grounding — graph signal is fused with vector + BM25 during recall.
FAQ
Does CortexDB use Neo4j internally? No — it implements its own traversal directly over the event-sourced log.
Can agents traverse unauthorized edges? No — traversal respects hierarchical scopes and bi-temporal validity; agents only see edges and entities their policy permits.
See also
- Facts — the triples the graph is built from.
- Hybrid Retrieval — how graph fits with the other channels.