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https://github.com/mem0ai/mem0

Memory for AI Agents; SOTA in AI Agent Memory, beating OpenAI Memory in accuracy by 26% - https://mem0.ai/research
https://github.com/mem0ai/mem0

agent ai aiagent application chatbots chatgpt embeddings llm long-term-memory memory memory-management python rag state-management vector-database

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Memory for AI Agents; SOTA in AI Agent Memory, beating OpenAI Memory in accuracy by 26% - https://mem0.ai/research

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README 

          


  

    Mem0 - The Memory Layer for Personalized AI

  





  

    mem0ai%2Fmem0 | Trendshift

  






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    Mem0 PyPI - Downloads

  

  

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    Y Combinator S24

  






  📄 Building Production-Ready AI Agents with Scalable Long-Term Memory →





  ⚡ +26% Accuracy vs. OpenAI Memory • 🚀 91% Faster • 💰 90% Fewer Tokens




> **🎉 mem0ai v1.0.0 is now available!** This major release includes API modernization, improved vector store support, and enhanced GCP integration. [See migration guide →](MIGRATION_GUIDE_v1.0.md)



##  🔥 Research Highlights

- **+26% Accuracy** over OpenAI Memory on the LOCOMO benchmark

- **91% Faster Responses** than full-context, ensuring low-latency at scale

- **90% Lower Token Usage** than full-context, cutting costs without compromise

- [Read the full paper](https://mem0.ai/research)



# Introduction



[Mem0](https://mem0.ai) ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. It remembers user preferences, adapts to individual needs, and continuously learns over time—ideal for customer support chatbots, AI assistants, and autonomous systems.



### 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



## 🚀 Quickstart Guide 



Choose between our hosted platform or self-hosted package:



### Hosted Platform



Get up and running in minutes with automatic updates, analytics, and enterprise security.



1. Sign up on [Mem0 Platform](https://app.mem0.ai)

2. Embed the memory layer via SDK or API keys



### Self-Hosted (Open Source)



Install the sdk via pip:



```bash

pip install mem0ai

```



Install sdk via npm:

```bash

npm install mem0ai

```



### CLI



Manage memories from your terminal:



```bash

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](https://docs.mem0.ai/platform/cli) for the full command reference.



### Basic Usage



Mem0 requires an LLM to function, with `gpt-4.1-nano-2025-04-14 from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/components/llms/overview).



First step is to instantiate the memory:



```python

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, user_id=user_id, limit=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-4.1-nano-2025-04-14", 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](https://docs.mem0.ai/quickstart) and [API Reference](https://docs.mem0.ai/api-reference).



## 🔗 Integrations & Demos



- **ChatGPT with Memory**: Personalized chat powered by Mem0 ([Live Demo](https://mem0.dev/demo))

- **Browser Extension**: Store memories across ChatGPT, Perplexity, and Claude ([Chrome Extension](https://chromewebstore.google.com/detail/onihkkbipkfeijkadecaafbgagkhglop?utm_source=item-share-cb))

- **Langgraph Support**: Build a customer bot with Langgraph + Mem0 ([Guide](https://docs.mem0.ai/integrations/langgraph))

- **CrewAI Integration**: Tailor CrewAI outputs with Mem0 ([Example](https://docs.mem0.ai/integrations/crewai))



## 📚 Documentation & Support



- Full docs: https://docs.mem0.ai

- Community: [Discord](https://mem0.dev/DiG) · [X (formerly Twitter)](https://x.com/mem0ai)

- Contact: founders@mem0.ai



## Citation



We now have a paper you can cite:



```bibtex

@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](https://github.com/mem0ai/mem0/blob/main/LICENSE) file for details.