<p align="center"> <a href="https://www.getzep.com/"> <img src="https://github.com/user-attachments/assets/119c5682-9654-4257-8922-56b7cb8ffd73" width="150" alt="Zep Logo"> </a> </p> <h1 align="center"> Graphiti </h1> <h2 align="center">Build Temporal Context Graphs for AI Agents</h2> <div align="center">

</div> <div align="center">

</div>

[!NOTE] We're Hiring! Build context graphs that power reliable, personalized, fast production AI agents. Come build with us — we're hiring Engineers and Developer Relations folks. View open roles.

⭐ Help us reach more developers and grow the Graphiti community. Star this repo!

[!TIP] Check out the new MCP server for Graphiti! Give Claude, Cursor, and other MCP clients powerful context graph-based memory with temporal awareness.

Graphiti is a framework for building and querying temporal context graphs for AI agents. Unlike static knowledge graphs, Graphiti's context graphs track how facts change over time, maintain provenance to source data, and support both prescribed and learned ontology — making them purpose-built for agents operating on evolving, real-world data.

Unlike traditional retrieval-augmented generation (RAG) methods, Graphiti continuously integrates user interactions, structured and unstructured enterprise data, and external information into a coherent, queryable graph. The framework supports incremental data updates, efficient retrieval, and precise historical queries without requiring complete graph recomputation, making it suitable for developing interactive, context-aware AI applications.

Use Graphiti to:

Build context graphs that evolve with every interaction — tracking what's true now and what was true before.
Give agents rich, structured context instead of flat document chunks or raw chat history.
Query across time, meaning, and relationships with hybrid retrieval (semantic + keyword + graph traversal).

What is a Context Graph?

A context graph is a temporal graph of entities, relationships, and facts — like "Kendra loves Adidas shoes (as of March 2026)." Unlike traditional knowledge graphs, each fact in a context graph has a validity window: when it became true, and when (if ever) it was superseded. Entities evolve over time with updated summaries. Everything traces back to episodes — the raw data that produced it.

What makes Graphiti unique is its ability to autonomously build context graphs from unstructured and structured data, handling changing relationships while preserving full temporal history.

A context graph contains:

| Component | What it stores | |-----------|---------------| | Entities (nodes) | People, products, policies, concepts — with summaries that evolve over time | | Facts / Relationships (edges) | Triplets (Entity → Relationship → Entity) with temporal validity windows | | Episodes (provenance) | Raw data as ingested — the ground truth stream. Every derived fact traces back here | | Custom Types (ontology) | Developer-defined entity and edge types via Pydantic models |

Graphiti and Zep

Graphiti is the open-source temporal context graph engine at the core of Zep's context infrastructure for AI agents. Zep manages context graphs at scale, providing governed, low-latency context retrieval and assembly for production agent deployments.

Using Graphiti, we've demonstrated Zep is the State of the Art in Agent Memory.

Read our paper: Zep: A Temporal Knowledge Graph Architecture for Agent Memory.

We're excited to open-source Graphiti, believing its potential as a context graph engine reaches far beyond memory applications.

Zep vs Graphiti

| Aspect | Zep | Graphiti | |--------|-----|---------| | What they are | Managed context graph infrastructure for AI agents | Open-source temporal context graph engine | | Context graphs | Manages vast numbers of per-user/entity context graphs with governance | Build and query individual context graphs | | User & conversation management | Built-in users, threads, and message storage | Build your own | | Retrieval & performance | Pre-configured, production-ready retrieval with sub-200ms performance at scale | Custom implementation required; performance depends on your setup | | Developer tools | Dashboard with graph visualization, debug logs, API logs; SDKs for Python, TypeScript, and Go | Build your own tools | | Enterprise features | SLAs, support, security guarantees | Self-managed | | Deployment | Fully managed or in your cloud | Self-hosted only |

When to choose which

Choose Zep if you want a turnkey, enterprise-grade platform with security, performance, and support baked in.

Choose Graphiti if you want a flexible OSS core and you're comfortable building/operating the surrounding system.

Why Graphiti?

Traditional RAG approaches often rely on batch processing and static data summarization, making them inefficient for frequently changing data. Graphiti addresses these challenges by providing:

Temporal Fact Management: Facts have validity windows. When information changes, old facts are invalidated — not deleted. Query what's true now, or what was true at any point in time.
Episodes & Provenance: Every entity and relationship traces back to the episodes (raw data) that produced it. Full lineage from derived fact to source.
Prescribed & Learned Ontology: Define entity and edge types upfront via Pydantic models (prescribed), or let structure emerge from your data (learned). Start simple, evolve as patterns appear.
Incremental Graph Construction: New data integrates immediately without batch recomputation. The graph evolves in real-time as episodes are ingested.
Hybrid Retrieval: Combines semantic embeddings, keyword (BM25), and graph traversal for low-latency, high-precision queries without reliance on LLM summarization.
Scalability: Efficiently manages large datasets with parallel processing, pluggable graph backends, suitable for enterprise workloads.

Graphiti vs. GraphRAG

| Aspect | GraphRAG | Graphiti | |--------|----------|---------| | Primary Use | Static document summarization | Dynamic, evolving context for agents | | Data Handling | Batch-oriented processing | Continuous, incremental updates | | Knowledge Structure | Entity clusters & community summaries | Temporal context graph — entities, facts with validity windows, episodes, communities | | Retrieval Method | Sequential LLM summarization | Hybrid semantic, keyword, and graph-based search | | Adaptability | Low | High | | Temporal Handling | Basic timestamp tracking | Explicit bi-temporal tracking with automatic fact invalidation | | Contradiction Handling | LLM-driven summarization judgments | Automatic fact invalidation with temporal history preserved | | Query Latency | Seconds to tens of seconds | Typically sub-second latency | | Custom Entity Types | No | Yes, customizable via Pydantic models | | Scalability | Moderate | High, optimized for large datasets |

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.

Installation

Requirements:

Python 3.10 or higher
Neo4j 5.26 / FalkorDB 1.1.2 / Kuzu 0.11.2 / Amazon Neptune Database Cluster or Neptune Analytics Graph + Amazon OpenSearch Serverless collection (serves as the full text search backend)
OpenAI API key (Graphiti defaults to OpenAI for LLM inference and embedding)

[!IMPORTANT] Graphiti works best with LLM services that support Structured Output (such as OpenAI and Gemini). Using other services may result in incorrect output schemas and ingestion failures. This is particularly problematic when using smaller models.

Optional:

Google Gemini, Anthropic, or Groq API key (for alternative LLM providers)

[!TIP] The simplest way to install Neo4j is via Neo4j Desktop. It provi

Graphiti

Install / Use

README