docs/vibecoding.mdx
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:
<CardGroup cols={2}> <Card title="Get an API Key" icon="key" href="https://app.mem0.ai/login?utm_source=oss&utm_medium=vibecoding"> Sign up for Mem0 Platform and start building </Card> <Card title="Quickstart" icon="rocket" href="/platform/quickstart"> Store your first memory in under 5 minutes </Card> </CardGroup>Mem0 ships two kinds of skills for AI coding assistants. Both work with Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any assistant that supports the skills standard.
Teach your assistant Mem0's SDK surface so it writes correct code in everyday development:
npx skills add https://github.com/mem0ai/mem0 --skill mem0
npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli
npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
mem0 — Python and TypeScript SDKs (Platform + OSS), plus framework integrations (LangChain, CrewAI, OpenAI Agents, LangGraph, LlamaIndex, etc.)mem0-cli — terminal workflows for the mem0 CLI (both Node and Python builds)mem0-vercel-ai-sdk — @mem0/vercel-ai-provider and createMem0Let your assistant execute an end-to-end workflow in an existing repo. Invoked as slash commands:
npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
/mem0-integrate — wire Mem0 into an existing repository using a goal-driven, test-first pipeline. Detects the stack, asks whether to use Platform or OSS, writes failing tests first, and keeps the integration additive and feature-flagged./mem0-test-integration — verify what /mem0-integrate produced. Runs the repo's native test suite and a real end-to-end smoke flow against your API key, then produces a scorecard.See the skills index for the full catalog.
Connect Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode, or any MCP-compatible client to Mem0.
Get your API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=vibecoding" rel="nofollow">app.mem0.ai</a>, 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.
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?", filters={"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." }], { userId: "user1" });
const results = await client.search("What editor?", { filters: { userId: "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?", filters={"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.