Graphiti

We’re moving Graphiti to Trial as this open-source temporal knowledge graph engine from Zep has demonstrated its production viability for addressing the LLM memory problem. While flat vector stores in RAG pipelines fail to track how facts change over time, Graphiti ingests data as discrete episodes and maintains bi-temporal validity windows on graph edges, so outdated facts are invalidated rather than overwritten. Unlike batch-oriented GraphRAG, it updates the graph incrementally and delivers sub-second retrieval via hybrid retrieval combining semantic search, BM25 and graph traversal, without query-time LLM calls. Two factors drove this move: peer-reviewed benchmarks reporting 18.5% accuracy improvements and 90% latency reductions, and the release of a first-class MCP server enabling Model Context Protocol–compliant agents to attach persistent temporal memory with minimal integration effort. Strong community adoption further signals production readiness. We’re using Graphiti to build context-aware agents with stateful, temporally aware knowledge graphs and recommend evaluating it for agentic applications. Neo4j is the primary backend, with FalkorDB as a lighter alternative. Teams should also account for per-write LLM extraction costs and pin dependencies given its pre-1.0 release status.

Graphiti 构建了动态且时间感知的知识图谱，能够捕捉不断演化的事实和关系。我们的团队使用 GraphRAG 来挖掘数据之间的关系，从而提升检索与响应的准确性。在数据集不断变化的情况下，Graphiti 在图的边上维护了时间元数据，用以记录关系的生命周期。它能够将结构化和非结构化数据以离散的Episodes 形式进行摄取，并支持基于时间、全文检索、语义和图算法的查询融合。 对于基于 LLM 的应用程序——无论是 RAG 模式还是 Agentic 方法——Graphiti 都能够支持长期记忆和基于状态的推理。