Context engineering platform that builds temporal knowledge graphs from conversations and business data, delivering personalized context to AI agents with <200ms retrieval latency.
A context engineering platform that builds knowledge graphs from conversations and business data, helping AI agents remember relationships and track how facts change over time.
Zep is a context engineering platform that fundamentally reimagines how AI agents access and process information. Instead of simple vector-based memory stores, Zep builds temporal knowledge graphs that capture entities, relationships, and how facts evolve over time from both conversations and structured business data.
The platform's core innovation lies in its unified approach to context assembly. Zep ingests chat messages, JSON business data, documents, and user interactions, automatically extracting entities and relationships while maintaining temporal awareness. When facts change, the system invalidates outdated information and preserves provenance, ensuring agents always work with current, accurate context.
This temporal knowledge graph approach enables sophisticated queries that simple memory systems cannot handle. Agents can ask 'What did the user say about Project X last month?' or 'How has the customer's preference for remote work evolved?' These queries require understanding relationships between entities across time—capabilities that flat vector retrieval simply cannot provide.
Zep's architecture is built for real-time applications with <200ms P95 retrieval latency, making it suitable for voice agents, live customer support, and interactive applications. The platform delivers 80.32% accuracy on the LoCoMo benchmark with single-shot retrieval, eliminating the need for slow agentic loops that plague other memory systems.
Competitive differentiation becomes clear when compared to alternatives: Mem0 provides basic flat memory facts suitable for simple personalization, while LangChain memory is limited to conversation history. Letta offers long-running agent memory but lacks business data integration. Zep uniquely combines Graph RAG, agent memory, and automated context assembly from multiple data sources into a single API.
The platform offers flexible deployment options from a free tier (1,000 credits monthly) through Flex plans ($25-$475/month with credit-based pricing) to Enterprise deployments with SOC2 Type 2 and HIPAA compliance. Enterprise customers can choose from Managed, BYOK (Bring Your Own Key), BYOM (Bring Your Own Model), or BYOC (Bring Your Own Cloud) deployment models.
Zep's open-source foundation includes Graphiti, their temporal context graph library, providing transparency and extensibility for developers who need customization. The platform supports Python, TypeScript, and Go SDKs with framework-agnostic integration, allowing teams to implement sophisticated memory capabilities in three lines of code.
Real-world applications span enterprise sales agents that track prospect interactions across months of conversations, customer support systems that understand full customer history including past decisions and technical issues, and educational platforms that adapt to student learning patterns over time. The system excels where understanding context evolution and entity relationships matter more than simple fact retrieval.
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Zep delivers sophisticated context engineering capabilities that go far beyond simple conversation memory. Users praise the temporal knowledge graph approach for capturing entity relationships and fact evolution over time. The <200ms retrieval latency and framework-agnostic integration make it suitable for real-time applications. Enterprise features including SOC2 and HIPAA compliance address security requirements. Some users note the credit-based pricing can become expensive at scale, and the graph-based architecture requires more setup than simple memory stores.
Builds evolving knowledge graphs from conversations and business data, tracking how entities and relationships change over time. Automatically invalidates outdated facts while preserving provenance, ensuring agents access current, accurate information.
Use Case:
Customer support agent understands that a user's payment method was updated last week, invalidating previous 'expired card' status while maintaining history of the resolution process.
Automatically ingests and correlates data from chat history, CRM systems, JSON business data, and documents into a single context graph. Retrieves and formats relevant information for LLM consumption in one API call.
Use Case:
Sales agent accessing prospect's conversation history, CRM data, and product interaction logs to provide personalized recommendations based on complete customer journey.
Delivers assembled context with <200ms P95 latency using optimized graph traversal and caching. Multiple configuration options balance accuracy, speed, and token efficiency for different use cases.
Use Case:
Voice agent providing immediate, personalized responses during live customer calls without noticeable delays, accessing complete customer context in real-time.
Combines relationship-aware retrieval with traditional RAG, understanding connections between entities to surface relevant context. Supports custom entity types and relationship models for domain-specific knowledge.
Use Case:
Healthcare agent understanding patient's medication history, doctor relationships, and treatment outcomes to provide contextually appropriate health guidance.
Pre-formatted context blocks optimized for different LLM prompting strategies. Allows fine-tuned control over how entities, relationships, and facts are presented to agents.
Use Case:
E-commerce agent receiving customer context formatted with purchase history, browsing patterns, and preference summaries tailored for product recommendation workflows.
SOC2 Type 2 certified with HIPAA BAA support, multiple deployment models including BYOK, BYOM, and BYOC. Audit logs, guaranteed SLAs, and data residency controls for regulated industries.
Use Case:
Healthcare organization deploying AI patient assistants with full HIPAA compliance, encrypted data processing, and audit trails for regulatory requirements.
Free
month
$25.00/month
month
$475.00/month
month
Custom
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