Zep: Long-Term Memory for AI Agents
Zep is a foundational memory layer designed to equip AI agents with the knowledge to complete tasks effectively. It focuses on providing personalized and accurate AI interactions by leveraging a knowledge graph that learns from user and business data.
Key Features:
- Knowledge Graph Fusion: Intelligently fuses chat messages and business data into a knowledge graph.
- Temporal Reasoning: Updates memory as facts change, marking superseded information as invalid and retaining temporal context.
- Instant Memory Retrieval: Provides relevant results from memory in milliseconds, scaling to millions of users.
- Granular Memory Controls: Offers custom rating frameworks and controls for tailoring memory extraction and relevance.
- Structured Output: Extracts strongly-typed data from chat history, including datetimes, floats, emails, and RegEx patterns.
- Dialogue Classifier: Classifies conversation state to understand user intent and emotion, enabling semantic routing and event triggering.
- Framework Agnostic: Compatible with Python, TypeScript, and Go, and integrates with frameworks like LangChain and AutoGen.
- Enterprise-Ready: SOC 2 Type II compliant and offers privacy controls for CCPA and GDPR.
Use Cases:
- Building personalized AI agents that can remember user preferences and past interactions.
- Creating accurate AI agents that can reason with changing user state and business data.
- Enabling agents to fill forms, populate API calls, understand user intent, segment users, and trigger events.
- Developing consistent and correct LLM applications with structured output and dialogue classification.