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Traditional RAG retrieves static documents based on similarity. Zep builds temporal knowledge graphs that understand entity relationships and track how facts change over time. This enables queries like 'how has the customer's preference evolved?' that static RAG cannot handle. Zep also assembles context from multiple sources (chat, CRM, business data) in one API call.
Zep achieves <200ms P95 retrieval latency through optimized graph traversal, intelligent caching, and single-shot context assembly. Unlike systems that require multiple tool calls or agentic loops, Zep delivers complete assembled context in one API request, eliminating the round-trip delays that slow down other approaches.
Zep's temporal knowledge graph automatically invalidates outdated facts when new information conflicts with existing data. It maintains provenance to source messages and timestamps, allowing agents to reason about when facts were true and how they've changed. This prevents agents from acting on stale information.
Yes. Zep is framework-agnostic with native SDKs for Python, TypeScript, and Go. It integrates with LangChain, LlamaIndex, AutoGen, CrewAI, and custom frameworks through simple API calls. The three-line integration works with any system that can make HTTP requests.
Enterprise customers can choose from Managed (fully hosted), BYOK (bring your own encryption keys), BYOM (bring your own model provider), or BYOC (bring your own cloud/VPC). All enterprise plans include SOC2 Type 2 certification, HIPAA BAA support, guaranteed SLAs, and dedicated account management.
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