getzep/zep
Zep
Zep is a long-term memory layer and persistent storage system for large language model applications. It functions as a memory service and vector database orchestrator that manages chat history, user preferences, and context retrieval to reduce hallucinations in AI agents.
The system maintains a temporal knowledge graph that stores interaction data as dated facts to track how user preferences and environments evolve over time. It combines these knowledge graphs with a store for persisting unstructured message data at the user and session levels.
The platform provides capabilities for AI context optimization through semantic and graph-based search, as well as AI privacy management tools for executing data deletion requests and compliance tasks across user records.
Features
- Long-term Memory Stores - Provides a persistent storage system for retaining user and session context across multiple AI interactions.
- Temporal Knowledge Graphs - Maintains a graph of dated facts to track how user preferences and environments evolve over time.
- AI Context Optimization - Fetches concise and relevant information for LLMs to improve response accuracy and reduce hallucinations.
- Graph-Based Context Retrieval - Searches for pertinent facts using combined semantic and graph methods to maintain precise context for AI generation.
- Knowledge Graphs - Organizes user interaction data as a graph of dated facts to provide persistent memory for AI agents.
- Persistent Chat Histories - Saves messages and unstructured data at the user or session level for long-term conversational context.
- Stateful LLM Application Servers - Provides a stateful server architecture that manages conversation threads, user preferences, and long-term memory.
- Hybrid Vector-Graph Databases - Combines vector embeddings with knowledge graphs to support both semantic and relational queries for high-precision context.
- Agent Interaction Memory Stores - Builds a temporal knowledge graph from documents and chats to maintain a persistent memory of users and contexts.
- Semantic Retrieval Engines - Uses mathematical embeddings to find relevant context by calculating the distance between queries and stored documents.
- Vector Database Orchestrators - Implements a management layer for indexing and retrieving semantic context and unstructured data to reduce LLM hallucinations.
- Prompt-Optimized Context Retrieval - Retrieves concise and governed data from a central source to improve AI response accuracy and reduce hallucinations.
- Document Versioning - Tracks historical changes to source materials to ensure retrieved context reflects the most current knowledge base state.
- User and Session Hierarchies - Organizes chat histories into user and session hierarchies to enable efficient retrieval and privacy-compliant data deletion.
- AI Session Privacy Management - Manages the deletion and pruning of conversational AI history to meet regulatory privacy requirements.
- Privacy Compliance Tools - Provides utilities for executing data deletion requests and privacy compliance tasks across user and session records.
- Agentic AI - Listed in the “Agentic AI” section of the The Incredible Pytorch awesome list.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
