mem0

Name: mem0
Availability: InStock
Author: Mem0

Store, retrieve, and manage user memories across AI applications.

62.9k|7.3k|Updated Jun 20, 2023

Search Tags:#multi-tenant #langchain #sdk #memory #vector-search #mem0 #ai-memory

Ready to run

npx skills add https://github.com/mem0ai/mem0 --skill mem0-mem0ai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mem0
Source: https://github.com/mem0ai/mem0/tree/main/skills/mem0
Command: npx skills add https://github.com/mem0ai/mem0 --skill mem0-mem0ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Mem0 provides a managed memory layer to store, retrieve, and manage user memories across AI applications.

Core Features & Use Cases

Automatic memory extraction and deduplication, with multi-tenant scoping (user/agent/app/run)
Hybrid retrieval with semantic, BM25, and entity matching, plus optional reranking
Examples: add user memories, search with filters, and multi-framework integrations (LangChain, CrewAI, OpenAI Agents)

Quick Start

Install mem0 client, initialize MemoryClient, add and search memories in a sample workflow using Python or TypeScript.

Frequently Asked Questions about mem0

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I persist memories across multiple AI applications?▼

To persist memories across AI applications, use a managed memory layer to store and retrieve user context. It supports per-user, per-session, and per-agent scoping to maintain memory continuity across different platforms.

How do I add and search user memories using the mem0 Python SDK?▼

You can add and search user memories by installing the mem0 client and initializing the MemoryClient in Python or TypeScript. The SDK handles automatic memory extraction and token-efficient retrieval for your workflow.

Does mem0 work with LangChain and CrewAI frameworks?▼

Yes, mem0 integrates with LangChain, CrewAI, and OpenAI Agents. It provides multi-framework integrations to inject memory management capabilities directly into your existing AI agent workflows.

What is the best way to handle multi-tenant memory scoping for AI agents?▼

The best way to handle multi-tenant memory scoping is to use a managed memory layer that supports per-user, per-session, and per-agent memories. This isolates context and ensures data privacy across different tenants.

How does hybrid retrieval work for AI memory management?▼

Hybrid retrieval for AI memory management combines semantic search, BM25, and entity matching, with optional reranking. This approach ensures token-efficient retrieval of relevant stored memories.

Why does my AI memory system return duplicate memories?▼

Your AI memory system returns duplicates because it lacks automatic memory deduplication. A managed memory layer handles extraction, deduplication, and graph/entity linking to prevent redundant stored context.