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">
    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

Teach your coding assistant how to build with Mem0:

```bash
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.

## Claude Code Plugin

The [OpenMemory plugin](https://github.com/mem0ai/claude-code-plugin) gives Claude Code **persistent memory across sessions, projects, and teams** — automatically.

<Steps>
<Step title="Get your API key">
Sign up at [openmemory.ai](https://openmemory.ai).
</Step>

<Step title="Install the plugin">
```bash
/plugin add mem0ai/claude-code-plugin
```
</Step>

<Step title="Set your environment variable">
```bash
export OPENMEMORY_API_KEY="your-key-here"
```
</Step>

<Step title="Start coding">
The plugin activates automatically. It captures decisions at session end, preserves context during compaction, and retrieves relevant memories at session start.
</Step>
</Steps>

## MCP Server Setup

Connect Cursor, Windsurf, Claude Desktop, or any MCP-compatible client to Mem0.

<Tabs>
<Tab title="OpenMemory Hosted">
Sign up at [app.openmemory.dev](https://app.openmemory.dev), then pick your client:

<CodeGroup>
```bash Claude Desktop
npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key
```

```bash Cursor
npx @openmemory/install --client cursor --env OPENMEMORY_API_KEY=your-key
```

```bash Windsurf
npx @openmemory/install --client windsurf --env OPENMEMORY_API_KEY=your-key
```
</CodeGroup>

For full setup options, see [OpenMemory Quickstart](/openmemory/quickstart).
</Tab>

<Tab title="Mem0 Platform MCP">
Get your API key from [app.mem0.ai](https://app.mem0.ai), then add to your MCP config:

```json
{
  "mcpServers": {
    "mem0": {
      "command": "uvx",
      "args": ["mem0-mcp-server"],
      "env": {
        "MEM0_API_KEY": "m0-...",
        "MEM0_DEFAULT_USER_ID": "your-handle"
      }
    }
  }
}
```

For Docker, Smithery, and advanced options, see [Mem0 MCP Setup](/platform/mem0-mcp).
</Tab>
</Tabs>

## 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. 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.
```

## 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>