🚀 Quickstart Guide
Sign up as an agent
AI agents can mint a working Mem0 API key in under five seconds — no email, no dashboard, no OTP. Four commands end-to-end:
# 1. Install
npm install -g @mem0/cli # or: pip install mem0-cli
# 2. Sign up as an agent (replace `claude-code` with your name)
mem0 init --agent --agent-caller claude-code
# 3. Add a memory
mem0 add "I am using mem0"
# 4. Search
mem0 search "am I using mem0"
The human owner can claim the account later with mem0 init --email <their-email> — same key, memories preserved. Full guide: Sign up as an agent.
| Library | Self-Hosted Server | Cloud Platform |
|---|
| Best for | Testing, prototyping | Teams running on their own infrastructure | Zero-ops production use |
| Setup | pip install mem0ai | docker compose up | Sign up at app.mem0.ai |
| Dashboard | -- | Yes | Yes |
| Auth & API Keys | -- | Yes | Yes |
| Advanced Features | -- | Teasers | All included |
Just testing? Use the library. Building for a team? Self-hosted. Want zero ops? Cloud.
Library (pip / npm)
pip install mem0ai
For enhanced hybrid search with BM25 keyword matching and entity extraction, install with NLP support:
pip install mem0ai[nlp]
python -m spacy download en_core_web_sm
Install sdk via npm:
npm install mem0ai
Self-Hosted Server
Note: Self-hosted auth is on by default. Upgrading from a pre-auth build? Set ADMIN_API_KEY, register an admin through the wizard, or AUTH_DISABLED=true for local dev only. See upgrade notes.
# Recommended: one command — start the stack, create an admin, issue the first API key.
cd server && make bootstrap
# Manual: start the stack and finish setup via the browser wizard.
cd server && docker compose up -d # http://localhost:3000
See the self-hosted docs for configuration.
Cloud Platform
- Sign up on Mem0 Platform
- Embed the memory layer via SDK or API keys
- Using hosted Qdrant vectors? See the Platform migration guide to import them into Mem0 Platform.
CLI
Manage memories from your terminal:
npm install -g @mem0/cli # or: pip install mem0-cli
mem0 init
mem0 add "Prefers dark mode and vim keybindings" --user-id alice
mem0 search "What does Alice prefer?" --user-id alice
See the CLI documentation for the full command reference.
Agent Skills
Teach your AI coding assistant (Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any tool that supports the skills standard) how to build with Mem0. Two categories:
Reference skills — always on (SDK knowledge loaded into the assistant's context):
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
Pipeline skills — run on demand (execute an end-to-end workflow in an existing repo):
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
Use /mem0-integrate to wire Mem0 into an existing repo via a test-first pipeline, then /mem0-test-integration to verify. Use /mem0-oss-to-platform to migrate an existing project from Mem0 OSS to the hosted Platform SDK. See the skills catalog or Vibecoding with Mem0 for the full picture.
Basic Usage
Mem0 requires an LLM to function, with gpt-5-mini from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our Supported LLMs documentation.
Mem0 uses text-embedding-3-small from OpenAI as the default embedding model. For best results with hybrid search (semantic + keyword + entity boosting), we recommend using at least Qwen 600M or a comparable embedding model. See Supported Embeddings for configuration details.
First step is to instantiate the memory:
from openai import OpenAI
from mem0 import Memory
openai_client = OpenAI()
memory = Memory()
def chat_with_memories(message: str, user_id: str = "default_user") -> str:
# Retrieve relevant memories
relevant_memories = memory.search(query=message, filters={"user_id": user_id}, top_k=3)
memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories["results"])
# Generate Assistant response
system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
response = openai_client.chat.completions.create(model="gpt-5-mini", messages=messages)
assistant_response = response.choices[0].message.content
# Create new memories from the conversation
messages.append({"role": "assistant", "content": assistant_response})
memory.add(messages, user_id=user_id)
return assistant_response
def main():
print("Chat with AI (type 'exit' to quit)")
while True:
user_input = input("You: ").strip()
if user_input.lower() == 'exit':
print("Goodbye!")
break
print(f"AI: {chat_with_memories(user_input)}")
if __name__ == "__main__":
main()
For detailed integration steps, see the Quickstart and API Reference.