docs: update README for real-time capabilities and GraphRAG comparison (#299)
* update intro and add graphrag * small nits
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Daniel Chalef
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README.md
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README.md
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<h1 align="center">
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Graphiti
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</h1>
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<h2 align="center"> Temporal Knowledge Graphs for Agentic Applications</h2>
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<h2 align="center"> Build Real-Time Knowledge Graphs for AI Agents</h2>
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<br />
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[](https://discord.com/invite/W8Kw6bsgXQ)
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@ -19,9 +19,13 @@ Graphiti
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:star: _Help us reach more developers and grow the Graphiti community. Star this repo!_
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<br />
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Graphiti builds dynamic, temporally aware Knowledge Graphs that represent complex, evolving relationships between
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entities over time. Graphiti ingests both unstructured and structured data, and the resulting graph may be queried using
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a fusion of time, full-text, semantic, and graph algorithm approaches, effectively serving as a powerful memory layer for AI applications.
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Graphiti is a framework for building and querying temporally-aware knowledge graphs, specifically tailored for AI agents operating in dynamic environments. 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.
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Use Graphiti to:
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- Integrate and maintain dynamic user interactions and business data.
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- Facilitate state-based reasoning and task automation for agents.
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- Query complex, evolving data with semantic, keyword, and graph-based search methods.
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<br />
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@ -31,23 +35,14 @@ a fusion of time, full-text, semantic, and graph algorithm approaches, effective
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<br />
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Graphiti helps you create and query Knowledge Graphs that evolve over time. A knowledge graph is a network of
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interconnected facts, such as _“Kendra loves Adidas shoes.”_ Each fact is a “triplet” represented by two entities, or
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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
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nodes (_”Kendra”_, _“Adidas shoes”_), and their relationship, or edge (_”loves”_). Knowledge Graphs have been explored
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extensively for information retrieval. What makes Graphiti unique is its ability to autonomously build a knowledge graph
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while handling changing relationships and maintaining historical context.
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With Graphiti, you can build LLM applications such as:
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- Assistants that learn from user interactions, fusing personal knowledge with dynamic data from business systems like
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CRMs and billing platforms through robust conversation history management.
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- Agents that autonomously execute complex tasks, reasoning with state changes from multiple dynamic sources through persistent memory.
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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.
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## Graphiti and Zep Memory
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Graphiti powers the core of [Zep's memory layer](https://www.getzep.com) for LLM-powered Assistants and Agents.
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Graphiti powers the core of [Zep's memory layer](https://www.getzep.com) for AI Agents.
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Using Graphiti, we've demonstrated Zep is
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the [State of the Art in Agent Memory](https://blog.getzep.com/state-of-the-art-agent-memory/).
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@ -62,28 +57,35 @@ We're excited to open-source Graphiti, believing its potential reaches far beyon
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## Why Graphiti?
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We were intrigued by Microsoft's GraphRAG, which expanded on RAG (Retrieval-Augmented Generation) text chunking by using a graph to better model a
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document corpus and making this representation available via semantic and graph search techniques. However, GraphRAG did
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not address our core problem: It's primarily designed for static documents and doesn't inherently handle temporal
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aspects of data.
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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:
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Graphiti is designed from the ground up to handle constantly changing information, hybrid semantic and graph search, and
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scale:
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- **Temporal Awareness:** Tracks changes in facts and relationships over time, enabling point-in-time queries. Graph
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edges include temporal metadata to record relationship lifecycles, creating a comprehensive context window extension.
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- **Episodic Processing:** Ingests data as discrete episodes, maintaining data provenance and allowing incremental
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entity and relationship extraction, ideal for chat state management.
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- **Hybrid Search:** Combines semantic and BM25 full-text search, with the ability to rerank results by distance from a
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central node e.g. "Kendra".
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- **Scalable:** Designed for processing large datasets, with parallelization of LLM calls for bulk processing while
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preserving the chronology of events and enabling efficient knowledge retrieval.
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- **Supports Varied Sources:** Can ingest both unstructured text and structured JSON data.
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- **Real-Time Incremental Updates:** Immediate integration of new data episodes without batch recomputation.
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- **Bi-Temporal Data Model:** Explicit tracking of event occurrence and ingestion times, allowing accurate point-in-time queries.
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- **Efficient Hybrid Retrieval:** Combines semantic embeddings, keyword (BM25), and graph traversal to achieve low-latency queries without reliance on LLM summarization.
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- **Custom Entity Definitions:** Flexible ontology creation and support for developer-defined entities through straightforward Pydantic models.
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- **Scalability:** Efficiently manages large datasets with parallel processing, suitable for enterprise environments.
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<p align="center">
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<img src="/images/graphiti-intro-slides-stock-2.gif" alt="Graphiti structured + unstructured demo" width="700px">
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</p>
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## Graphiti vs. GraphRAG
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| Aspect | GraphRAG | Graphiti |
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| -------------------------- | ------------------------------------- | ------------------------------------------------ |
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| **Primary Use** | Static document summarization | Dynamic data management |
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| **Data Handling** | Batch-oriented processing | Continuous, incremental updates |
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| **Knowledge Structure** | Entity clusters & community summaries | Episodic data, semantic entities, communities |
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| **Retrieval Method** | Sequential LLM summarization | Hybrid semantic, keyword, and graph-based search |
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| **Adaptability** | Low | High |
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| **Temporal Handling** | Basic timestamp tracking | Explicit bi-temporal tracking |
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| **Contradiction Handling** | LLM-driven summarization judgments | Temporal edge invalidation |
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| **Query Latency** | Seconds to tens of seconds | Typically sub-second latency |
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| **Custom Entity Types** | No | Yes, customizable |
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| **Scalability** | Moderate | High, optimized for large datasets |
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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.
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## Installation
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Requirements:
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@ -258,6 +260,7 @@ Graphiti is under active development. We aim to maintain API stability while wor
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- Allow developers to provide their own defined node and edge classes when ingesting episodes
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- Enable more flexible knowledge representation tailored to specific use cases
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- [x] Enhancing retrieval capabilities with more robust and configurable options
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- [ ] Graphiti MCP Server
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- [ ] Expanding test coverage to ensure reliability and catch edge cases
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## Contributing
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