mem0/docs/vibecoding.mdx

---
title: "Vibecoding with Mem0"
sidebarTitle: "Vibecoding"
description: "Agent skills, starter prompts, and setup for building with Mem0 using AI coding tools."
icon: "wand-magic-sparkles"
---

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:

- [llms.txt](https://docs.mem0.ai/llms.txt)

<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>

## Agent Skills

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.

### Reference skills — always on

Teach your assistant Mem0's SDK surface so it writes correct code in everyday development:

```bash
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 `createMem0`

### Pipeline skills — run on demand

Let your assistant execute an end-to-end workflow in an existing repo. Invoked as slash commands:

```bash
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](https://github.com/mem0ai/mem0/tree/main/skills) for the full catalog.

## MCP Server Setup

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:

```bash
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](/platform/mem0-mcp).

## Universal Starter Prompt

Copy this into any AI tool to start building with Mem0:

```text
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. Storage: vector embeddings.

**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.
```

## Go Deeper

<CardGroup cols={2}>
  <Card title="Platform Quickstart" icon="cloud" href="/platform/quickstart">
    Get started with the managed API
  </Card>
  <Card title="Open Source" icon="code-branch" href="/open-source/overview">
    Self-host with full control
  </Card>
  <Card title="Cookbooks" icon="book" href="/cookbooks/overview">
    Production-ready tutorials and examples
  </Card>
  <Card title="API Reference" icon="code" href="/api-reference">
    Explore every REST endpoint
  </Card>
</CardGroup>