Freemium
Leading digital analytics platform for product insights and customer journey analytics
analyticsproduct analyticscustomer insights
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Pricing
Freemium
Use cases
Developers building real-time voice or video agents requiring low-latency context retrieval for natural human-like interactions (verified: 2026-01-30)., Engineering teams integrating chat history and CRM data into a unified knowledge graph for consistent agent memory (verified: 2026-01-30)., Software architects implementing Graph RAG to provide LLMs with token-efficient context from diverse sources like JSON and documents (verified: 2026-01-30).
Strengths
The platform achieves sub-200ms P95 retrieval latency making it suitable for high-performance real-time applications and live support (verified: 2026-01-30)., Users can ingest data from multiple sources including chat history, app events, and documents into a single knowledge graph (verified: 2026-01-30)., The system provides token-efficient context formatting which reduces the amount of data sent to the LLM while maintaining relevance (verified: 2026-01-30).
Limitations
The entry-level Flex plan limits users to 5 projects and 10 custom entity or edge types for their graph (verified: 2026-01-30)., Advanced features such as custom extraction instructions and webhooks are restricted to the Flex Plus tier or higher (verified: 2026-01-30).
Last verified
Jan 30, 2026
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Strengths
- The platform achieves sub-200ms P95 retrieval latency making it suitable for high-performance real-time applications and live support (verified: 2026-01-30).
- Users can ingest data from multiple sources including chat history, app events, and documents into a single knowledge graph (verified: 2026-01-30).
- The system provides token-efficient context formatting which reduces the amount of data sent to the LLM while maintaining relevance (verified: 2026-01-30).
Limitations
- The entry-level Flex plan limits users to 5 projects and 10 custom entity or edge types for their graph (verified: 2026-01-30).
- Advanced features such as custom extraction instructions and webhooks are restricted to the Flex Plus tier or higher (verified: 2026-01-30).
FAQ
How does Zep handle the retrieval of context for real-time AI agent applications?
Zep utilizes a context engineering approach that assembles relevant data from chat history and knowledge graphs. It is designed for real-time performance with a P95 retrieval latency of less than 200ms, ensuring that voice and video agents can access necessary information without significant delays (verified: 2026-01-30).
What are the specific limitations for users on the free tier of the Zep platform?
Users on the free tier receive 1,000 credits per month to explore the platform's capabilities. This tier is intended for playground use and development, but it is subject to specific usage limits that are not present in the paid Flex or Enterprise plans (verified: 2026-01-30).
Can Zep integrate with existing CRM systems and other external data sources for memory?
Yes, Zep is built to ingest data from various sources including CRM systems, application events, and JSON files. It assembles this information into a unified knowledge graph that stays current as the underlying data changes, providing a continuous learning memory for agents (verified: 2026-01-30).
