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Introduction

Mem0 ("mem-zero") is the memory layer for AI agents. It remembers user preferences, decisions, and history across sessions, so your agents stop asking the same questions and stay useful over weeks instead of a single conversation.

How it works

Mem0 sits between your app and your LLM. Two calls do the work.

On add(), Mem0 writes memory:

1. Your conversation messages go to an LLM in a single extraction pass, which pulls out durable facts ("prefers dark mode", "ships to Berlin") and discards small talk.
2. Entities in those facts are extracted, embedded, and linked to entities already stored, so related memories connect to each other.
3. Facts are stored additively. Nothing is overwritten or deleted, so history stays auditable and a wrong extraction cannot silently erase a correct one.

On search(), Mem0 reads memory:

1. Your query runs against three signals in parallel: semantic vector similarity, BM25 keyword matching, and entity overlap.
2. The three scores are fused, and time-aware ranking picks the right dated instance when facts changed over time ("where do I live" vs "where did I live before").
3. You get back a short, ranked list to drop into your prompt, typically under 7K tokens instead of replaying a 25K+ token conversation history.

That is the whole loop: add() after a turn, search() before the next one. Everything else (vector store, embedder, LLM, reranker) is swappable.

Key Features & Use Cases

Core Capabilities:

• Multi-Level Memory: Seamlessly retains User, Session, and Agent state with adaptive personalization
• Developer-Friendly: Intuitive API, cross-platform SDKs, and a fully managed service option

Applications:

• AI Assistants: Consistent, context-rich conversations
• Customer Support: Recall past tickets and user history for tailored help
• Healthcare: Track patient preferences and history for personalized care
• Productivity & Gaming: Adaptive workflows and environments based on user behavior

Benchmarks

Benchmark	Previous	Current	Tokens	Latency p50
LoCoMo	71.4	92.5	7.0K	0.88s
LongMemEval	67.8	94.4	6.8K	1.09s
BEAM (1M)	--	64.1	6.7K	1.00s
BEAM (10M)	--	48.6	6.9K	1.05s

All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops) at a top_200 retrieval budget. Scores reflect Mem0's managed platform, which includes proprietary optimizations not available in the open-source SDK; open-source users should expect directionally similar gains but not identical numbers.

The evaluation framework is open-sourced so anyone can reproduce the numbers, and the full paper covers the methodology. Upgrading from v2? See the migration guide.

🚀 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

1. Sign up on Mem0 Platform
2. Embed the memory layer via SDK or API keys
3. 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.

🔗 Integrations & Demos

• ChatGPT with Memory: Personalized chat powered by Mem0 (Live Demo)
• Browser Extension: Store memories across ChatGPT, Perplexity, and Claude (Chrome Extension)
• Langgraph Support: Build a customer bot with Langgraph + Mem0 (Guide)
• CrewAI Integration: Tailor CrewAI outputs with Mem0 (Example)

📚 Documentation & Support

• Full docs: https://docs.mem0.ai
• Community: Discord · X (formerly Twitter)
• Contact: founders@mem0.ai

Citation

We now have a paper you can cite:

@article{mem0,
  title={Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory},
  author={Chhikara, Prateek and Khant, Dev and Aryan, Saket and Singh, Taranjeet and Yadav, Deshraj},
  journal={arXiv preprint arXiv:2504.19413},
  year={2025}
}

⚖️ License

Apache 2.0 — see the LICENSE file for details.