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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<div align="center">
<img width="350" alt="Graphiti-ts-small" src="https://github.com/user-attachments/assets/bbd02947-e435-4a05-b25a-bbbac36d52c8">
# Graphiti
## Temporal Knowledge Graphs for Agentic Applications
@ -23,7 +23,7 @@ a fusion of time, full-text, semantic, and graph algorithm approaches.
<br />
<p align="center">
<img src="/images/graphiti-graph-intro.gif" alt="Graphiti temporal walkthrough" width="700px">
<img src="images/graphiti-graph-intro.gif" alt="Graphiti temporal walkthrough" width="700px">
</p>
<br />
@ -43,6 +43,20 @@ 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 and Zep Memory
Graphiti powers the core of [Zep's memory layer](https://www.getzep.com) for LLM-powered Assistants and Agents.
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/).
Read our paper: [Zep: A Temporal Knowledge Graph Architecture for Agent Memory](https://arxiv.org/abs/2501.13956).
We're excited to open-source Graphiti, believing its potential reaches far beyond memory applications.
<p align="center">
<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>
</p>
## Why Graphiti?
We were intrigued by Microsofts GraphRAG, which expanded on RAG text chunking by using a graph to better model a
@ -67,12 +81,6 @@ scale:
<img src="/images/graphiti-intro-slides-stock-2.gif" alt="Graphiti structured + unstructured demo" width="700px">
</p>
## Graphiti and Zep Memory
Graphiti powers the core of [Zep's memory layer](https://www.getzep.com) for LLM-powered Assistants and Agents.
We're excited to open-source Graphiti, believing its potential reaches far beyond memory applications.
## Installation
Requirements:
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> [!IMPORTANT]
> Graphiti uses OpenAI for LLM inference and embedding. Ensure that an `OPENAI_API_KEY` is set in your environment.
> Support for Anthropic and Groq LLM inferences is available, too.
> Support for Anthropic and Groq LLM inferences is available, too. Other LLM providers may be supported via OpenAI compatible APIs.
```python
from graphiti_core import Graphiti
@ -194,13 +202,9 @@ as such this feature is off by default.
Graphiti is under active development. We aim to maintain API stability while working on:
- [x] Implementing node and edge CRUD operations
- [ ] Improving performance and scalability
- [ ] Achieving good performance with different LLM and embedding models
- [x] Creating a dedicated embedder interface
- [ ] Supporting custom graph schemas:
- Allow developers to provide their own defined node and edge classes when ingesting episodes
- Enable more flexible knowledge representation tailored to specific use cases
- Allow developers to provide their own defined node and edge classes when ingesting episodes
- Enable more flexible knowledge representation tailored to specific use cases
- [x] Enhancing retrieval capabilities with more robust and configurable options
- [ ] Expanding test coverage to ensure reliability and catch edge cases

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