Zep Memory
UnclaimedBuild personalized, fast, and reliable AI agents with comprehensive, evolving memory.
Visit WebsiteTL;DR - Zep Memory
- Provides comprehensive, evolving memory for AI agents.
- Unifies chat history, business data, and user interactions into a temporal knowledge graph.
- Delivers fast, token-efficient, and accurate context to LLMs for personalized agents.
Pricing: Free plan available
Best for: Growing teams
4.7/5 across review platforms
Pros & Cons
Pros
- Significantly improves AI agent accuracy and personalization by providing rich, evolving context.
- Reduces inference costs and response latency by delivering token-efficient and relevant context.
- Integrates diverse data sources (chat, business data, user behavior) into a single knowledge graph.
- Designed for real-time applications with extremely low retrieval latency (<200ms).
- Supports custom graph ontologies for domain-specific memory and improved relevance.
Cons
- No explicit free tier mentioned, suggesting it's a paid solution.
- Requires integration and setup to connect various data sources.
- Complexity of managing and defining custom graph ontologies might require some expertise.
Ratings Across the Web
4.7(15 reviews)
Ratings aggregated from independent review platforms. Learn more
Preview
Key Features
End-to-End Context EngineeringTemporal Knowledge Graph Construction with Fact InvalidationUnified Data Ingestion (Chat, Documents, JSON, CRM, App Events)Sub-200ms P95 Retrieval LatencyCustomizable Context Blocks for LLMsPersistent Context with Provenance TrackingCustom Graph Ontologies (Pydantic, Zod)Entity Extraction and Relationship Building
Pricing Plans
Free
Free
- 1,000 episodes a month
- Low rate limits
- Lower priority episode processing
Flex
$25/month
- 20,000 credits included
- Auto-topup at 20% (+20,000)
- 600 requests per minute
- 5 Projects
- 10 custom entity & edge types
- Unlimited memories, retrieval & users
Flex Plus
$475/month
- 300,000 credits included
- Auto-topup at 20% (+100,000)
- 1,000 requests per minute
- 5 Projects
- 20 custom entity & edge types
- Custom extraction instructions
- Webhooks
- API logs (7 days)
- Unlimited memories, retrieval & users
Enterprise
Contact Sales
- SOC 2 Type II certification & HIPAA BAA
- Custom limits & guaranteed rate limits
- Slack support & dedicated account manager
- API & audit logs, SLA guarantees
- Flexible Deployment (Managed Enterprise, BYOK, BYOM, BYOC)
About Zep Memory
By Toolradar Team·
Zep is an AI agent memory platform that provides end-to-end context engineering for building intelligent agents. It addresses the common problem of AI agents failing due to a lack of relevant context by assembling information from chat history, business data, and user behavior into a unified, temporal knowledge graph. This allows agents to have a holistic and continuously updated understanding of users and their interactions.
The platform is designed for developers and organizations building AI agents, including conversational AI, voice agents, and live support systems. It integrates with any agent framework and offers low-latency retrieval, making it suitable for real-time applications. Zep aims to improve agent accuracy, reduce inference costs, and lower response latency by providing agents with rich, personalized, and evolving context, moving beyond simple chat memory or static RAG approaches.
Zep's core functionality involves ingesting diverse data sources, automatically extracting entities and relationships to construct a dynamic knowledge graph, and then assembling relevant, token-efficient context for Large Language Models (LLMs). It also supports custom graph ontologies to tailor memory to specific business domains, ensuring high precision and relevance.
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Zep Memory FAQ
How does Zep handle evolving user information and fact invalidation over time?
Zep utilizes a temporal context graph that updates with every interaction. When facts change, old ones are automatically invalidated, ensuring the agent always has the most current and accurate context.
What types of data sources can Zep ingest to build its unified context graph?
Zep can ingest a variety of data sources including chat messages, JSON business data, CRM records, application events, and documents. It integrates these into a single, evolving context graph.
How does Zep ensure low latency for real-time applications like voice agents?
Zep is optimized for performance, achieving P95 retrieval latency of less than 200ms. This speed is crucial for real-time applications such as voice agents, video agents, and live support, preventing latency issues.
Can I customize the context blocks that Zep provides to my LLM?
Yes, Zep allows for customizable context blocks. You can define templates using a simple API to format the retrieved context, including user profiles, relevant facts, and entities, optimized for your specific LLM needs.
How does Zep compare to other agent memory solutions in terms of accuracy and efficiency?
Zep leads on the LoCoMo benchmark for single-shot retrieval, demonstrating up to 80.32% accuracy at 189ms. It offers configurations to optimize for accuracy, latency, and token efficiency, outperforming traditional chat memory or static RAG approaches.
Does Zep support defining custom graph ontologies for specific business domains?
Yes, Zep allows users to easily define custom graph ontologies to improve the precision and relevance of agent memory. This customization can be done using familiar tools like Pydantic and Zod.
Source: getzep.com