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README.md
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README.md
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@ -77,6 +77,23 @@ We're excited to open-source Graphiti, believing its potential reaches far beyon
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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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<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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## Zep vs Graphiti
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| Aspect | Zep | Graphiti |
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|--------|-----|----------|
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| **What they are** | Complete managed platform for AI memory | Open-source graph framework |
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| **User & conversation management** | Built-in users, threads, and message storage | Build your own |
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| **Retrieval & performance** | Pre-configured, production-ready retrieval with sub-200ms performance at scale | Custom implementation required; performance depends on your setup |
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| **Developer tools** | Dashboard with graph visualization, debug logs, API logs; SDKs for Python, TypeScript, and Go | Build your own tools |
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| **Enterprise features** | SLAs, support, security guarantees | Self-managed |
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| **Deployment** | Fully managed or in your cloud | Self-hosted only |
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### When to choose which
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**Choose Zep** if you want a turnkey, enterprise-grade platform with security, performance, and support baked in.
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**Choose Graphiti** if you want a flexible OSS core and you're comfortable building/operating the surrounding system.
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## Why Graphiti?
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## Why Graphiti?
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Traditional RAG approaches often rely on batch processing and static data summarization, making them inefficient for
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Traditional RAG approaches often rely on batch processing and static data summarization, making them inefficient for
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