agentrun-mem0ai 0.0.2

Long-term memory for AI Agents with Aliyun TableStore support

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📄 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 →

🔥 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

Introduction

Mem0 ("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
2. Embed the memory layer via SDK or API keys

Self-Hosted (Open Source)

Install the sdk via pip:

pip install mem0ai

Install sdk via npm:

npm install mem0ai

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.

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, 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 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 · Twitter
• Contact: [e-mail]

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}
}

📦 PyPI 自动发布

使用自动化脚本快速发布到 PyPI：

1. 配置 PyPI Token

复制模板文件并填入你的 API Token：

cp .pypirc.example .pypirc
# 编辑 .pypirc 文件，填入你的 PyPI 和 TestPyPI 的 API Token

2. 安装发布工具

pip install --upgrade build twine

3. 运行发布脚本

./publish.sh

脚本会自动完成以下步骤：

• 检查并使用项目目录下的 .pypirc 配置
• 清理旧的构建文件
• 构建新的发行包
• 检查包的完整性
• 可选：先上传到测试环境验证
• 上传到正式 PyPI

注意：.pypirc 文件包含敏感信息，已被 .gitignore 忽略，不会提交到版本控制

⚖️ License

Apache 2.0 — see the LICENSE file for details.