AI EngineerVideoSource Linked
Citation Needed: Provenance for LLM-Built Knowledge Graphs — Daniel Chalef, Zep AI
LLM synthesis breaks simple source pointers when facts merge, change, or outlive one input. Model provenance inside the context graph so agents can filter trust, debug derivation, and apply deletion policies.
AI Engineer · Jul 23, 2026
Source Summary
Graphiti stores source episodes and links derived entities and facts back to them. When entities merge or new evidence contradicts a fact, those links and temporal changes preserve **fact-level lineage** instead of relying on a later log.
Practical Implication
Build provenance into an agent-memory data structure at ingestion. Propagate source metadata, filter retrieval by trust policy, and use remaining source links to decide whether a derived fact survives **source deletion**.
Agent-Ready Context
Graphiti stores source episodes and links derived entities and facts back to them. When entities merge or new evidence contradicts a fact, those links and temporal changes preserve **fact-level lineage** instead of relying on a later log. Build provenance into an agent-memory data structure at ingestion. Propagate source metadata, filter retrieval by trust policy, and use remaining source links to decide whether a derived fact survives **source deletion**. Verification policy is contextual: some decisions require any verified parent, while others require every parent to be verified. Graph construction is also **expensive in cost and latency**, and one discussed many-edge case uses a separate structure rather than keeping all provenance in the graph.
Context Map
contextdatasecurity#agent-memory#retrieval#context-engineeringUncertainty
Verification policy is contextual: some decisions require any verified parent, while others require every parent to be verified. Graph construction is also **expensive in cost and latency**, and one discussed many-edge case uses a separate structure rather than keeping all provenance in the graph.