Zep provides agent memory capabilities for teams building and operating AI applications. It supports the MCP ecosystem.
Zep provides agent memory capabilities for teams building and operating AI applications. It supports the MCP ecosystem.
Zep is a agent memory product intended for teams building or operating AI-enabled software. The vendor site describes its offering in terms that include: The vendor page could not be reliably extracted during this automated run. This profile is based on the public homepage and pricing route fetched on August 25, 2026; buyers should confirm details that affect purchasing or production architecture directly with the vendor.
For builders, the practical value is reducing the custom work needed to connect, inspect, evaluate, or operate AI applications. Typical workflows include prototyping an assistant, adding the product to an existing application, reviewing behavior with a technical or business team, and creating a repeatable production process. It is best evaluated with a representative workload: connect one real application, define success criteria, measure setup effort and output quality, then review security, data retention, access controls, and export options. Zep advertises or participates in the Model Context Protocol ecosystem in a server role. That can make it easier to expose capabilities to MCP hosts or consume MCP tools without a proprietary integration for every client.
Pricing evidence from the fetched pricing page was not reliably extractable. Free allowances, usage limits, model charges, seats, support, and enterprise controls can materially change total cost. A sensible pilot should therefore test both functional fit and expected monthly volume. Strong use cases include agent memory pilots, internal AI applications, and production systems where teams want a maintained product instead of assembling every operational component themselves.
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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.
$0/month
$125/month
$375/month
Custom
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