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mem0ai avatar

mem0ai/mem0

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58,698 stars·6,748 forks·Python·Apache-2.0·7 viewsmem0.ai↗

Mem0

Mem0 is an agent-agnostic memory layer designed to provide intelligent agents with long-term persistence and cross-session state management. By acting as a centralized service, it allows diverse AI agents to recall user preferences, past interactions, and historical context, ensuring continuity across multiple workflows and independent agent systems.

The platform distinguishes itself through a multi-signal retrieval engine that combines semantic vectors, keyword matching, and entity-linked metadata to surface the most relevant information. It employs an adaptive memory engine that automatically extracts, compresses, and updates data, while applying temporal decay logic to prioritize recent information and reduce noise. To support enterprise requirements, the system provides hierarchical multi-tenancy, enforcing strict data isolation and access control boundaries between different organizations, projects, and user groups.

Beyond its core storage capabilities, the project offers a comprehensive suite of tools for managing the information lifecycle, including asynchronous event orchestration, webhook integration, and schema-based data structuring. It supports both self-hosted and cloud-based deployments, allowing developers to maintain full control over their infrastructure and data privacy.

The project provides a Python-based initialization process and a command-line interface for managing memory records and configuring agent environments. Detailed documentation and integration guides are available to assist with implementation across various technology stacks.

Features

  • Agent Memory Engines - A persistent storage engine that provides intelligent agents with long-term context, user history, and cross-session state management.
  • Agent Memory Libraries - Provides a centralized library and platform for adding, searching, and retrieving agent memories.
  • Agent Memory Management - Initializes agent memory storage and performs data operations via command line.
  • Agent Memory Orchestration - Provides a centralized memory layer to coordinate user history and session state across agent interactions.

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Memory Persistence - Provides AI agents with long-term storage to recall user preferences and historical context.
  • Knowledge Management Systems - Manages stored memory records through natural language or structured data operations.
  • Vector Databases - Stores unstructured user information as high-dimensional embeddings to enable semantic similarity searches across long-term interaction histories.
  • Multi-Tenancy Security - A secure platform that isolates memory records across different organizational boundaries while maintaining granular access control for enterprise deployments.
  • Multi-Level Memory Management - Coordinates persistent user, session, and agent state across interactions for personalized experiences.
  • Persistent Context Management - Adds and retrieves relevant history from interactions to provide personalized experiences across sessions.
  • Retrieval Engines - Combines semantic vectors, keyword matching, and entity-linked metadata to rank and fetch the most relevant context for agents.
  • Semantic Search APIs - Retrieves context using semantic search and metadata filters to find specific information based on user queries or historical interaction patterns.
  • Semantic Search Engines - Improves retrieval accuracy using AI-powered reranking and keyword expansion.
  • Agent Memory Architectures - Integrates a persistent storage layer that allows agents to retrieve context before reasoning and save refined insights.
  • Contextual Retrieval - Ranks information using semantic, keyword, and entity-linked signals to find the most relevant context.
  • Memory Engines - Automatically extracts and updates relevant information from interactions to maintain an efficient knowledge base.
  • Memory Infrastructure - Implements production-grade features like data isolation and latency controls for high-volume agent deployments.
  • Persistent Memory Integrations - Connects a dedicated storage layer to agent platforms to manage the ingestion, retrieval, and consistency of user state.
  • Infrastructure Deployment Tools - Enables hosting of memory engines on private infrastructure for full data control.
  • Multi-Tenancy Access Controls - Enforces strict data isolation and access control boundaries between different organizations, projects, and individual user groups.
  • Pipeline Automation Tools - Automates repository integration, stack detection, and testing through pipeline workflows.
  • Memory Persistence Patterns - Applies standard techniques like conversation buffers, summarized history, and vector-based retrieval to manage context persistence.
  • Model Configuration Interfaces - Optimizes retrieval performance by configuring language models and embedding providers.
  • State Synchronization - Synchronizes user preferences and past actions across multiple agents to maintain continuity.
  • Agent Frameworks - Universal memory layer for persistent agent state.
  • AI Agents and Automation - AI memory system for long-term context and personalized interactions.
  • AI and Agents - An intelligent memory layer for AI agents enabling personalized interactions.
  • Artificial Intelligence - Memory framework for enhancing LLM application capabilities.
  • Inference and Serving - Memory layer for personalized AI applications.
  • Inference Engines - Memory layer for personalized AI interactions.
  • Memory And Caching - Memory layer for context-aware AI applications.
  • Memory Management - Universal memory layer for AI agents.
  • Retrieval Augmented Generation - Intelligent memory layer for personalizing AI agent interactions.
  • Data Ingestion and Parsing - Framework for creating AI applications on custom data.
  • Python Projects - Listed in the “Python Projects” section of the Awesome For Beginners awesome list.
  • Data Ingestion APIs - Saves new information from interactions into a persistent knowledge base to build a long-term history for users and agents.
  • Protocol Client Integrations - Links compatible clients to development environments for memory-aware interactions.
  • Software Development Kits - Provides a Python interface to manage user data through add and search operations.
  • Agent Skill Definitions - Configures coding skills for AI assistants to ensure consistent SDK and CLI usage.
  • Memory Relevance Controls - Maintains focus by tagging data with types, applying temporal decay, and using confidence scores to prevent noise.
  • Environment Configuration Managers - Manages API keys, authentication secrets, and database connections for secure deployment.
  • Multi-Tenancy - Manages isolated data environments for different users while maintaining secure access.
  • Access Management APIs - Defines organizational boundaries and project access controls to ensure data isolation and secure collaboration across different teams and user groups.
  • Agentic Workflows - Enables automated systems to retrieve relevant background information for improved decision-making.
  • Memory Compression - Reduces stored data size to optimize retrieval speed while preserving context accuracy.
  • Natural Language Processing Libraries - Enables hybrid search and entity extraction through integrated natural language processing tools.
  • State Orchestration - Manages and synchronizes user preferences and interaction history across multiple agent workflows.
  • Data Update APIs - Modifies existing content and metadata to ensure that stored information remains accurate, relevant, and up to date over time.
  • CLI Configuration Tools - Authenticates local development environments and manages CLI configuration files.
  • Event Orchestration - Uses background task queues and webhook triggers to process memory updates and synchronize state changes with external systems.
  • Memory Strategy Design - Defines identities, storage types, and retrieval policies to ensure consistent and relevant state management for intelligent agents.

    • Star history

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    Frequently asked questions

    What does mem0ai/mem0 do?

    Mem0 is an agent-agnostic memory layer designed to provide intelligent agents with long-term persistence and cross-session state management. By acting as a centralized service, it allows diverse AI agents to recall user preferences, past interactions, and historical context, ensuring continuity across multiple workflows and independent agent systems.

    What are the main features of mem0ai/mem0?

    The main features of mem0ai/mem0 are: Agent Memory Engines, Agent Memory Libraries, Agent Memory Management, Agent Memory Orchestration, Memory Persistence, Knowledge Management Systems, Vector Databases, Multi-Tenancy Security.

    What are some open-source alternatives to mem0ai/mem0?

    Open-source alternatives to mem0ai/mem0 include: letta-ai/letta — Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across… flowiseai/flowise — Flowise is a low-code platform designed for building and deploying complex language model workflows through a visual,… topoteretes/cognee — Cognee is an agentic memory management platform designed to provide autonomous agents with long-term semantic recall… mastra-ai/mastra — Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and… crewaiinc/crewai — CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex,… open-webui/open-webui — Open WebUI is a self-hosted, web-based platform designed for interacting with local and remote artificial intelligence…

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