Overview
Temporal Knowledge Graphs for Agentic Applications
Graphiti helps you create and query Knowledge Graphs that evolve over time. A knowledge graph is a network of interconnected facts, such as “Kendra loves Adidas shoes.” Each fact is a “triplet” represented by two entities, or nodes (”Kendra”, “Adidas shoes”), and their relationship, or edge (”loves”).
Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to autonomously build a knowledge graph while handling changing relationships and maintaining historical context.
Graphiti builds dynamic, temporally-aware knowledge graphs that represent complex, evolving relationships between entities over time. It ingests both unstructured and structured data, and the resulting graph may be queried using a fusion of time, full-text, semantic, and graph algorithm approaches.
With Graphiti, you can build LLM applications such as:
Graphiti supports a wide range of applications in sales, customer service, health, finance, and more, enabling long-term recall and state-based reasoning for both assistants and agents.
Graphiti powers the core of Zep’s context layer for LLM-powered Assistants and Agents.
We’re excited to open-source Graphiti, believing its potential reaches far beyond context applications.
We were intrigued by Microsoft’s GraphRAG, which expanded on RAG text chunking by using a graph to better model a document corpus and making this representation available via semantic and graph search techniques. However, GraphRAG did not address our core problem: It’s primarily designed for static documents and doesn’t inherently handle temporal aspects of data.
Graphiti is designed from the ground up to handle constantly changing information, hybrid semantic and graph search, and scale:
Graphiti is specifically designed to address the challenges of dynamic and frequently updated datasets, making it particularly suitable for applications requiring real-time interaction and precise historical queries.
