Zep AI

• Persistent long term memory for AI agents
• Temporal knowledge graphs for evolving information
• Automatic memory extraction and enrichment
• Semantic and full text hybrid search
• Context retrieval from conversations and business data
• Structured and unstructured data ingestion
• Memory MCP Server for AI assistants and agents
• Python, TypeScript, JavaScript, and Go SDK support
• Scalable memory retrieval for production AI applications
• Enterprise security, compliance, BYOK, and BYOC deployment options

Zep is an AI agent memory and context engineering platform designed to help developers build more reliable, personalized, and context-aware AI applications. Instead of treating every interaction as an isolated conversation, Zep provides infrastructure for storing information about users, businesses, documents, conversations, events, and previous agent activity, then retrieving useful context when an agent needs it.

The platform is primarily built for developers and teams creating AI agents, conversational applications, customer-facing assistants, research systems, and other software that needs persistent contextual information. It can be integrated into applications through its SDKs and APIs, allowing developers to add memory without building an entire memory and retrieval pipeline themselves.

Zep organizes incoming information into temporal Context Graphs. These graphs represent entities, relationships, and facts while preserving information about how those facts change over time. This allows an agent to distinguish current information from older information rather than treating every historical fact as equally relevant.

Developers can add conversation messages and other application data to Zep, then retrieve a Context Block containing relevant information for the next model interaction. The resulting context can be inserted into an LLM prompt, giving the agent access to useful history without requiring the complete conversation or database contents to be included every time.

Zep also supports a broader Context Lake architecture that can unify chat, business information, documents, and events across large numbers of users, agents, or organizational subjects. Governance capabilities can control access to context and support retention and auditing requirements.

For developers, Zep can be used with Python, TypeScript, and Go, and it provides integrations for several agent frameworks. Its Memory MCP Server also allows compatible AI clients to access governed user memory.

Zep offers a free plan with 10,000 monthly credits. Paid Flex starts at $125 per month, while Flex Plus starts at $375 per month, with Enterprise pricing available for organizations requiring custom capacity and deployment options.

Frequently Asked Questions

What is Zep used for?

Zep is used to give AI agents persistent memory and relevant context across conversations and tasks. Developers can use it to build personalized assistants, customer agents, research applications, and other context-aware AI systems.

How does Zep work?

Zep receives conversations and other application data, organizes that information into a temporal Context Graph, and retrieves relevant context when an agent needs it. Developers can then provide the returned context to their chosen language model.

Is Zep free to use?

Yes, Zep offers a free plan with 10,000 credits per month for prototyping and development. The free tier has usage and feature limits, while larger applications can move to paid plans. :contentReference[oaicite:8]{index=8}

How much does Zep cost?

Zep’s first paid self-serve plan is Flex at $125 per month with 50,000 included credits. Flex Plus costs $375 per month and includes 200,000 credits, while Enterprise pricing is customized. :contentReference[oaicite:9]{index=9}

What makes Zep different from basic AI memory tools?

Zep uses temporal Context Graphs to represent facts, relationships, entities, and changes over time rather than relying only on simple conversation history or similarity retrieval. Its broader context infrastructure also supports business data, documents, events, governance, and enterprise-scale memory.

Can Zep remember information between AI conversations?

Yes, Zep can persist relevant information from previous interactions and make that information available to an agent during later conversations. Developers control what data is added and how retrieved context is incorporated into the AI application’s workflow.

What programming languages does Zep support?

Zep provides SDK support for Python, TypeScript, and Go. Developers can use these SDKs to add messages, ingest application data, retrieve context, and integrate memory into AI applications. :contentReference[oaicite:10]{index=10}

Can Zep connect AI agents to shared memory?

Yes, Zep’s Memory MCP Server can connect compatible AI clients to governed user memory. This allows supported clients and applications to work with the same underlying memory while maintaining identity-based access controls. :contentReference[oaicite:11]{index=11}

Share Your Experience: