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These docs are designed to be easily consumable by LLMs. Each page has a button that lets you copy the page as Markdown or paste directly into ChatGPT, Claude, or any AI coding tool. We follow the llms.txt standard:

Get an API Key

Sign up for Mem0 Platform and start building

Quickstart

Store your first memory in under 5 minutes

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Agent Skills

Teach your coding assistant how to build with Mem0: 
npx skills add https://github.com/mem0ai/mem0 --skill mem0
Works with Claude Code, Cursor, Windsurf, and any assistant that supports skills. Once installed, your assistant understands Mem0’s full API, framework integrations, and common patterns.

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MCP Server Setup

Connect Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode, or any MCP-compatible client to Mem0. Get your API key from app.mem0.ai, then add Mem0 MCP with a single command: 
npx mcp-add \
  --name mem0-mcp \
  --type http \
  --url "https://mcp.mem0.ai/mcp" \
  --clients "claude,claude code,cursor,windsurf,vscode,opencode"
For per-client setup and advanced options, see Mem0 MCP Setup.

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Universal Starter Prompt

Copy this into any AI tool to start building with Mem0: 
I want to start building with Mem0 — a self-improving memory layer for LLM
applications that gives agents persistent context across sessions.

## Mem0 Resources

**Documentation:**
- Main docs: https://docs.mem0.ai
- Platform Quickstart: https://docs.mem0.ai/platform/quickstart
- OSS Python Quickstart: https://docs.mem0.ai/open-source/python-quickstart
- OSS Node.js Quickstart: https://docs.mem0.ai/open-source/node-quickstart
- API Reference: https://docs.mem0.ai/api-reference
- Full LLM-friendly docs: https://docs.mem0.ai/llms.txt

**Code & Examples:**
- Core repo: https://github.com/mem0ai/mem0
- Python SDK: pip install mem0ai
- TypeScript SDK: npm install mem0ai
- Cookbooks: https://docs.mem0.ai/cookbooks/overview

**What Mem0 Does:**
Mem0 is a memory layer for AI apps — managed (Mem0 Platform) or self-hosted
(Open Source). It stores, retrieves, and manages user memories so agents
remember preferences, learn from interactions, and personalize over time.
Sub-50ms retrieval. Dual storage: vector embeddings + graph databases.

**Architecture Overview:**
- Memory is scoped by user_id, agent_id, or run_id
- Core operations: add, search, update, delete
- Memory types: factual (preferences, facts), episodic (past interactions),
  semantic (concept relationships), working (session state)
- Integration pattern: retrieve relevant memories → generate response → store
  new memories

**Quick Usage (Python Platform):**
  from mem0 import MemoryClient
  client = MemoryClient(api_key="m0-xxx")
  client.add("I prefer dark mode and use VS Code.", user_id="user1")
  results = client.search("What editor do they use?", user_id="user1")

**Quick Usage (JavaScript Platform):**
  import MemoryClient from 'mem0ai';
  const client = new MemoryClient({ apiKey: 'm0-xxx' });
  await client.add([{ role: "user", content: "I prefer dark mode." }], { user_id: "user1" });
  const results = await client.search("What editor?", { user_id: "user1" });

**Quick Usage (Python Open Source):**
  from mem0 import Memory
  m = Memory()
  m.add("I prefer dark mode and use VS Code.", user_id="user1")
  results = m.search("What editor do they use?", user_id="user1")

Help me integrate Mem0 into my project. Start by asking what I'm building,
what language/framework I'm using, and whether I want managed or self-hosted.

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Go Deeper

Platform Quickstart

Get started with the managed API

Open Source

Self-host with full control

Cookbooks

Production-ready tutorials and examples

API Reference

Explore every REST endpoint