What is Graphiti

Graphiti helps overcome static RAG’s limitations with dynamic data. It’s a real-time, temporally-aware knowledge graph engine that incrementally processes incoming data, instantly updating entities, relationships, and communities without batch recomputation. Graphiti isn’t just another retrieval tool — it’s an ever-present source of context for agents, continuously available and updated.

Graphiti’s real-time incremental architecture is built for frequent updates. It continuously ingests new data episodes (events or messages), extracting and immediately resolving entities and relationships against existing nodes.

A key feature is Graphiti’s bi-temporal model, which tracks when an event occurred and when it was ingested. Every graph edge (or relationship) includes explicit validity intervals (t_valid, t_invalid). Graphiti uses semantic, keyword, and graph search to determine whether new knowledge conflicts with existing knowledge. When conflicts arise, Graphiti intelligently uses the temporal metadata to update or invalidate, but not discard, outdated information, preserving historical accuracy without large-scale recomputation.

Fast Query Speeds: Instant Retrieval Without LLM Calls

Graphiti is built for speed. Zep’s own Graphiti implementation achieves extremely low-latency retrieval, returning results at a P95 latency of 300ms. This is enabled by a hybrid search approach that combines semantic embeddings, keyword (BM25) search, and direct graph traversal — avoiding any LLM calls during retrieval.

Graphiti represents a meaningful departure from traditional RAG methods, specifically because it was built from the ground up as a memory infrastructure for dynamic agentic systems. Graphiti offers incremental, real-time updates through its temporally aware knowledge graph. This design means engineers no longer need to recompute entire graphs when data changes. Instead, Graphiti incrementally integrates updates, resolves conflicts based on temporal metadata, and maintains an accurate historical state.

By removing the bottleneck of LLM-driven summarization at query time, Graphiti achieves practical latency levels that engineers require for interactive real-world applications. Its hybrid indexing system — combining semantic embeddings, keyword search, and graph traversal — allows rapid retrieval in near-constant time, independent of graph scale. With intuitive tools like custom entity types implemented through familiar structures such as Pydantic models, Graphiti addresses a significant capability gap in agent development, equipping engineers with a robust, performant, and genuinely dynamic memory layer.