For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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DocumentationSDK ReferenceGraphiti
DocumentationSDK ReferenceGraphiti
      • Key Concepts
      • Architecture Patterns
      • What is Context Engineering?
      • Retrieval Philosophy
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Key Concepts

Key Concepts

Understanding agent memory, temporal knowledge graphs, and the Context Lake.
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Architecture patterns

Choose the right Zep architecture by deciding across scope, ingestion, and retrieval axes.
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Zep delivers agent memory at enterprise scale. From any source — chat, business data, documents, JSON — Zep builds a temporal knowledge graph (its Context Graph) of the entities, relationships, and facts that matter to a user or subject, then assembles token-efficient context (facts, summaries, and Observations) and serves it from a governed Context Lake with sub-200ms retrieval. This reduces hallucinations and improves accuracy.

ConceptDescriptionDocs
Context GraphZep’s temporal Context Graph — the unit of agent memory. Nodes are entities, edges are facts/relationships; the graph updates dynamically as new data arrives, invalidating outdated facts while preserving history.Docs
Zep’s Context BlockOptimized string containing a user summary and facts from the Context Graph most relevant to the current thread. Also contains dates when facts became valid and invalid. Provide this to your chatbot as context.Docs
Context TypesThe distinct primitives Zep produces from a user graph: facts, entities, episodes, thread summaries, observations, and user summary. Choose the type that fits the agent’s task.Docs
Fact InvalidationWhen new data invalidates a prior fact, the time the fact became invalid is stored on that fact’s edge in the Context Graph.Docs
JSON/text/messageTypes of data that can be ingested into the Context Graph. Can represent business data, documents, chat messages, emails, etc.Docs
Custom Entity/Edge TypesFeature allowing use of Pydantic-like classes to customize creation/retrieval of entities and relations in the Context Graph.Docs
GraphRepresents a standalone Context Graph for storing up-to-date knowledge about an object or system. For storing up-to-date knowledge about a user, a user graph should be used.Docs
User GraphSpecial type of graph for storing personalized context for a user of your application.Docs
UserA user in Zep represents a user of your application, and has its own User Graph and thread history.Docs
ThreadsConversation threads of a user. By default, all messages added to any thread of that user are ingested into that user’s graph.Docs
graph.add & thread.add_messagesMethods for adding data to a graph and user graph respectively.Docs Docs
graph.search & thread.get_user_contextLow level and high level methods respectively for retrieving from the Context Graph.Docs Docs
User Summary InstructionsCustomize how Zep generates entity summaries for users in their Context Graph. Up to 5 custom instructions per user to guide summary generation.Docs
Agentic ToolUse Zep’s context retrieval methods as agentic tools, enabling your agent to query for relevant information from the user’s Context Graph.Docs