docs/vibecoding.mdx
Vibecoding means building software by describing what you want to an AI coding assistant and letting it write the code. The catch is that assistants guess at unfamiliar libraries, and a wrong guess about Mem0 costs you a debugging session.
This page fixes that three ways: skills that teach your assistant the Mem0 SDKs, an MCP connection so it can read and write memories itself, and a starter prompt you can paste into any tool.
<Note> Every page in these docs has a button to copy it as Markdown or send it straight to ChatGPT or Claude, so you can hand your assistant any page it needs. We follow the [llms.txt](https://docs.mem0.ai/llms.txt) standard. </Note>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
npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
/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./mem0-oss-to-platform: migrate an existing project from Mem0 OSS to the hosted Platform SDK. Audits where Mem0 is used, writes a reviewable migration plan, then executes it on approval.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, which gives AI agents long-term memory
that persists across sessions, tools, and runs.
## 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 gives AI agents long-term memory, either managed (Mem0 Platform) or
self-hosted (Open Source). It stores, retrieves, and manages memories so
agents remember preferences, learn from past runs, and personalize over
time. 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: { 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?", 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.