docs: update README to enhance clarity and add Zep Memory section (#260)
* docs: update README to enhance clarity and add Zep Memory section * docs: fix formatting in README for clarity on Zep's memory capabilities * docs: add hyperlink to arXiv paper in README for improved accessibility
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README.md
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README.md
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<div align="center">
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<div align="center">
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<img width="350" alt="Graphiti-ts-small" src="https://github.com/user-attachments/assets/bbd02947-e435-4a05-b25a-bbbac36d52c8">
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# Graphiti
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## Temporal Knowledge Graphs for Agentic Applications
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## Temporal Knowledge Graphs for Agentic Applications
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<br />
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<br />
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<p align="center">
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<p align="center">
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<img src="/images/graphiti-graph-intro.gif" alt="Graphiti temporal walkthrough" width="700px">
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<img src="images/graphiti-graph-intro.gif" alt="Graphiti temporal walkthrough" width="700px">
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</p>
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</p>
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<br />
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<br />
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@ -43,6 +43,20 @@ With Graphiti, you can build LLM applications such as:
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Graphiti supports a wide range of applications in sales, customer service, health, finance, and more, enabling long-term
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Graphiti supports a wide range of applications in sales, customer service, health, finance, and more, enabling long-term
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recall and state-based reasoning for both assistants and agents.
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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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Using Graphiti, we've demonstrated Zep is the [State of the Art in Agent Memory](https://blog.getzep.com/state-of-the-art-agent-memory/).
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Read our paper: [Zep: A Temporal Knowledge Graph Architecture for Agent Memory](https://arxiv.org/abs/2501.13956).
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We're excited to open-source Graphiti, believing its potential reaches far beyond memory applications.
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<p align="center">
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<a href="https://arxiv.org/abs/2501.13956"><img src="images/arxiv-screenshot.png" alt="Zep: A Temporal Knowledge Graph Architecture for Agent Memory" width="700px"></a>
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</p>
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## Why Graphiti?
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## Why Graphiti?
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We were intrigued by Microsoft’s GraphRAG, which expanded on RAG text chunking by using a graph to better model a
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We were intrigued by Microsoft’s GraphRAG, which expanded on RAG text chunking by using a graph to better model a
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<img src="/images/graphiti-intro-slides-stock-2.gif" alt="Graphiti structured + unstructured demo" width="700px">
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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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</p>
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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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We're excited to open-source Graphiti, believing its potential reaches far beyond memory applications.
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## Installation
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## Installation
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Requirements:
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Requirements:
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> [!IMPORTANT]
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> [!IMPORTANT]
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> Graphiti uses OpenAI for LLM inference and embedding. Ensure that an `OPENAI_API_KEY` is set in your environment.
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> Graphiti uses OpenAI for LLM inference and embedding. Ensure that an `OPENAI_API_KEY` is set in your environment.
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> Support for Anthropic and Groq LLM inferences is available, too.
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> Support for Anthropic and Groq LLM inferences is available, too. Other LLM providers may be supported via OpenAI compatible APIs.
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```python
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```python
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from graphiti_core import Graphiti
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from graphiti_core import Graphiti
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Graphiti is under active development. We aim to maintain API stability while working on:
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Graphiti is under active development. We aim to maintain API stability while working on:
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- [x] Implementing node and edge CRUD operations
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- [ ] Improving performance and scalability
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- [ ] Achieving good performance with different LLM and embedding models
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- [x] Creating a dedicated embedder interface
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- [ ] Supporting custom graph schemas:
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- [ ] Supporting custom graph schemas:
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- Allow developers to provide their own defined node and edge classes when ingesting episodes
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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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- Enable more flexible knowledge representation tailored to specific use cases
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