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Zep
Context engineering platform that builds temporal knowledge graphs from chat and business data for production AI agents.
FreeStarting price
78KMonthly visits
32Popularity
About Zep
Zep is an API and web-based context engineering and agent memory platform designed for AI development teams and enterprises building production AI agents and assistants. It constructs temporal knowledge graphs that track entities, relationships, and facts over time, enabling fact invalidation to prevent stale context from influencing responses. The platform unifies context from multiple sources—including chat history, JSON business data, documents, CRM systems, and application events—into token-efficient context blocks optimized for LLM prompts. Zep supports real-time agents with graph RAG retrieval that combines semantic, keyword, and graph-based search, achieving sub-200ms (P95) context retrieval and assembly. It offers Context Templates and custom ontologies for domain-specific filtering and reproducible evaluations. The platform integrates with major LLM providers such as OpenAI, Azure OpenAI, Google Gemini, and Anthropic, and provides an MCP server for compatibility with clients like Claude and Cursor. Zep is notable for its temporal knowledge graph approach, powered by the open-source Graphiti engine with Neo4j support, and publishes benchmark results demonstrating strong performance in agent memory evaluations.
Key features
- Temporal knowledge graph memory with fact invalidation
- Unified context assembly from chat, documents, CRM, and app events
- Graph RAG retrieval combining semantic, keyword, and graph-based search
- Sub-200ms (P95) context retrieval and assembly for real-time agents
- Context Templates and custom ontologies for domain-specific filtering
- MCP server for integration with Claude, Cursor, and other MCP-compatible clients
- Support for multiple LLM providers including OpenAI, Azure OpenAI, Google Gemini, and Anthropic
- Published benchmark results for agent memory evaluations
- Graphiti engine with Neo4j support
- Token-efficient context block formatting for LLM prompts
Use cases
- Building production AI agents and assistants
- Training AI models with structured temporal context
- Enhancing chatbots with long-term memory and context
Pros
- Temporal knowledge graphs track entities, relationships, and facts over time with automated fact invalidation to ensure up-to-date context
- Sub-200ms (P95) context retrieval and assembly, maintaining performance regardless of graph size or count
- Unified context ingestion from diverse sources including chat history, business data, documents, CRM systems, and application events
- Provenance-preserving memory with audit trails that trace facts back to their source episodes for transparency
- Enterprise-scale memory infrastructure with access control, retention policies, and audit capabilities built into the substrate
Cons
- Complexity in setup and management due to the temporal knowledge graph architecture and enterprise-scale features
- Potential latency in initial graph construction when ingesting large volumes of historical data
- Dependency on proprietary Context Graph Engine may limit flexibility for custom implementations
Frequently asked questions about Zep
What does Zep do?
Zep provides persistent memory for AI agents through temporal context graphs that track entities, relationships, and facts over time, enabling fact invalidation and token-efficient context retrieval.
Who is Zep designed for?
Zep is designed for AI development teams and enterprises building production AI agents and assistants that require scalable, governed memory infrastructure.
How does Zep handle memory updates and contradictions?
Zep invalidates old facts when new information contradicts existing data, ensuring agents reason with the latest decisions, traits, and behaviors while preserving historical context.
What integrations does Zep support?
Zep integrates with major LLM providers such as OpenAI, Azure OpenAI, Google Gemini, and Anthropic, and offers an MCP server for compatibility with clients like Claude and Cursor.
How do I get started with Zep?
Zep provides a quickstart guide with code examples in Python, TypeScript, and Go, allowing users to add memory to their agents in minutes with minimal setup.
Does Zep support enterprise-scale deployments?
Yes, Zep is built for enterprise-scale memory infrastructure, offering features like access control, retention policies, provenance tracking, and audit capabilities across millions of context graphs.
Zep traffic & engagement
getzep.com · third-party estimates from Similarweb
Data as of Sep 2026Full AI website ranking →
Monthly visits · Sep 202678K+13.9% MoM12-month high 116K (Jun 2026)
Rank among AI websites#2,207▲ 136 vs last monthof 8,652 tools on aitoolnavio™
Top marketUnited States37.4%Top 5 countries bring 68% of visits
Average visit1m 23sLonger than 68% of AI sites
Pages per visit3.12More than 79% of AI sites
Bounce rate41%Leave after one page · lower is better
Similarweb global rank#400,269#275,803 in United States
Traffic trend
Last 4 months · grows by one month every month
- Jun 2026116K
- Jul 202687K−24.4%
- Aug 202668K−22%
- Sep 202678K+13.9%
Top countries
Share of visits · estimated visits
Other countries: 31.6%
Visits sent by AI assistants
How people reach this site from other AI tools
639 a month · 0.8% of all visits
Compared with similar tools
Automation & Agents · Sep 2026
Traffic figures are third-party estimates, good for scale and trends; they can differ from a company's own numbers. Updated monthly.
