A temporal knowledge graph records when each fact was true and where it came from. Standard vs temporal, what bi-temporal means, and why AI agents need it.
A temporal knowledge graph is a knowledge graph in which every fact carries time — when it became true, when it stopped being true, and where it came from — so the graph represents not just what is known, but how that knowledge changed over time. Where a standard knowledge graph stores entities and relationships as if they're permanently true, a temporal knowledge graph tracks state as it evolves, which is exactly what AI agents need to reason about users and the world without contradicting themselves.
| Standard knowledge graph | Temporal knowledge graph | |
|---|---|---|
| Represents | Entities + relationships | Entities + relationships + time |
| Handles change | Overwrites or duplicates facts | Invalidates old facts, keeps history |
| Can answer “what was true then?” | No | Yes |
| Provenance | Often none | Each fact traces to its source |
| Built for | Static domains | Evolving state (users, agents, business) |
Facts about people and businesses change constantly. A user upgrades a plan, changes a preference, closes an account; a deal moves stages; a policy is updated. A graph that can't represent when a fact was true will eventually serve an agent two contradictory facts and let it pick — the single most common cause of agent hallucination. A temporal knowledge graph removes that ambiguity: it knows the current truth and the historical truth, and can answer either.
The strongest temporal knowledge graphs are bi-temporal — they track two timelines for every fact:
With both, an agent can ask “what is true now?”, “what was true on this date?”, and “where did this fact come from?” — and get a correct, auditable answer to each. When new information contradicts an existing fact, the old fact is marked invalid (not deleted), so history is preserved.
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For agent memory, the graph is constructed continuously from the agent's inputs — chat messages, business data (JSON), and documents. The system extracts entities, relationships, and facts; assigns each a validity window and provenance; and updates the graph as new information arrives, invalidating superseded facts. At query time it retrieves the relevant, current (or as-of-date) slice — not the whole history.
This is what Graphiti does. Graphiti is the open-source temporal knowledge graph library purpose-built for agent memory (20,000+ GitHub stars). It constructs bi-temporal context graphs from any source and powers Zep's agent-memory platform. When scaled with Zep, Graphiti runs on top of the Context Graph Engine — the runtime that serves millions of these graphs with sub-200ms p95 retrieval. Zep is the Context Lake for AI agents: the platform that manages, governs, and serves agent memory at scale on temporal knowledge graphs.
A vector store retrieves text chunks by similarity and has no concept of time or relationships — it can return a stale fact and a current one with equal confidence. A temporal knowledge graph models the relationships and the timeline, so it returns what's true now (or as-of a date) with provenance. For agent memory, the temporal graph is the right structure; vector search is a useful component inside it, not a substitute.
Related: What is agent memory? · Graphiti (open source) · Context Graph Engine · Agent memory vs RAG
A knowledge graph that records when each fact was true and where it came from — so it can tell you what's true now, what was true before, and why.
A standard knowledge graph treats facts as timeless. A temporal one tracks how facts change, invalidating old facts while preserving history — essential for agents reasoning over evolving state.
It tracks two timelines per fact: when the fact was true in the world (valid time) and when the system learned it (ingestion time / provenance).
Graphiti is the leading open-source temporal knowledge graph built specifically for agent memory; it powers Zep's Context Lake at enterprise scale.
