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mem0

Integrate persistent memory into AI applications using the Mem0 Platform SDK.

Updated Jun 29, 2026
Search Tags:#typescript#python#personalization#ai memory#vector search#llm integration#mem0
One-click install
npx skills add https://github.com/nhatnguyen1122/Agent-Memory-Eval --skill mem0-nhatnguyen1122
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mem0
Source: https://github.com/nhatnguyen1122/Agent-Memory-Eval/tree/main/mem0/integrations/mem0-plugin/skills/mem0
Command: npx skills add https://github.com/nhatnguyen1122/Agent-Memory-Eval --skill mem0-nhatnguyen1122

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? AI applications lose all user context between sessions, forcing developers to build their own memory storage, extraction, and retrieval infrastructure. This Skill provides working code patterns and API references for adding managed, persistent memory to Python and TypeScript AI apps via the Mem0 Platform. ## Core Features & Use Cases - Memory CRUD Operations: Add, search, update, and delete user memories with scoped filters (user_id, agent_id, app_id, run_id) using the v3 API. - Framework Integrations: Ready-to-use patterns for LangChain, CrewAI, OpenAI Agents SDK, LangGraph, LlamaIndex, AutoGen, and Vercel AI SDK. - Live Documentation Search: Query docs.mem0.ai on demand with the included search script for the latest API details. - Use Case: A developer building a support chatbot asks Claude to add memory so the bot recalls each customer's past issues; the Skill generates the retrieve-generate-store loop with correct v3 filter syntax. ## Quick Start Ask Claude to set up mem0 in your project and add memory to your chatbot using your MEM0_API_KEY.

Frequently Asked Questions about mem0

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

FAQPage Schema
How do I add memory to my AI chatbot with mem0?▼

Install mem0ai, initialize MemoryClient with your API key, then follow the retrieve-generate-store loop: call client.search() before generating a response and client.add() after, both scoped with user_id.

How to integrate mem0 with LangChain or CrewAI?▼

For LangChain, retrieve memories with mem0.search() and inject them as context into your prompt chain. CrewAI has native support via memory_config with provider set to mem0 and a user_id in the config.

Why does mem0 search return empty results after adding memories?▼

Mem0 v3 processes add() asynchronously, returning an event ID immediately, so wait 2-3 seconds before searching. Also verify the user_id matches exactly and is passed inside the filters object.

What is the difference between mem0 MemoryClient and Memory?▼

MemoryClient connects to the managed Mem0 Platform API at api.mem0.ai and requires an API key. Memory is the open-source self-hosted class that runs locally with your own vector store, embedder, and LLM configuration.

Does mem0 support TypeScript and Node.js applications?▼

Yes, the mem0ai npm package provides an async MemoryClient for TypeScript with camelCase parameters like userId and topK. Filter keys remain snake_case, and all methods return Promises.

Why does combining user_id and agent_id in an AND filter return nothing?▼

Mem0 stores entities separately, so an AND filter across user_id and agent_id yields empty results. Use OR logic or query each scope separately to retrieve memories across entities.

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