
MCP-Mem0
A robust server for managing long-term agent memory using Mem0, providing efficient storage and retrieval of agent memories with a lightweight Python-based implementation.
README
MCP-Mem0: Your Gateway to Long-Term Agent Memory 🚀
Welcome to the MCP-Mem0 repository! This project provides a robust server for managing long-term agent memory using Mem0. It also serves as a helpful template for anyone looking to build their own MCP server with Python.
Table of Contents
Features ✨
- Long-Term Memory Management: Efficiently store and retrieve agent memories.
- Python-Based: Built with Python, making it easy to customize and extend.
- Template Structure: A great starting point for your own MCP server development.
- Lightweight: Minimal resource requirements for easy deployment.
Getting Started 🏁
To get started with MCP-Mem0, you will need to download the latest release. Visit the Releases section to find the latest version. Download the file and execute it to set up your server.
Installation ⚙️
Follow these steps to install MCP-Mem0:
-
Clone the Repository:
git clone https://github.com/yellnuts/mcp-mem0.git cd mcp-mem0
-
Install Dependencies: Ensure you have Python 3.6 or higher installed. Use pip to install the required packages:
pip install -r requirements.txt
-
Run the Server: After installing the dependencies, you can start the server with:
python server.py
-
Access the API: Open your web browser and navigate to
http://localhost:5000
to access the server.
Usage 📚
Once the server is running, you can interact with it using HTTP requests. Below are some example endpoints you can use:
-
Create Memory:
POST /memory
Body:
{ "agent_id": "unique_agent_id", "memory_data": "Your memory data here" }
-
Retrieve Memory:
GET /memory/{agent_id}
-
Delete Memory:
DELETE /memory/{agent_id}
For more detailed API documentation, refer to the API.md
file in the repository.
Contributing 🤝
We welcome contributions to MCP-Mem0! Here’s how you can help:
- Fork the Repository: Click the "Fork" button at the top right corner of the page.
- Create a Branch:
git checkout -b feature/YourFeature
- Make Changes: Implement your feature or fix.
- Commit Your Changes:
git commit -m "Add your message here"
- Push to the Branch:
git push origin feature/YourFeature
- Open a Pull Request: Go to the original repository and click on "New Pull Request".
License 📄
This project is licensed under the MIT License. See the LICENSE
file for more details.
Contact 📬
For any inquiries or support, please contact the maintainer:
- Name: [Your Name]
- Email: [your.email@example.com]
- GitHub: your-github-profile
Thank you for checking out MCP-Mem0! We hope you find it useful. For the latest updates and releases, don’t forget to check the Releases section again.
Advanced Configuration 🔧
MCP-Mem0 allows for advanced configurations to suit your specific needs. You can adjust settings in the config.json
file located in the root directory. Here are some of the key configurations you can modify:
- Memory Expiry: Set how long memories should be retained.
- Logging Level: Adjust the verbosity of server logs.
- Port Configuration: Change the port number if needed.
Example Configuration
Here’s an example of what your config.json
might look like:
{
"memory_expiry": "30 days",
"logging_level": "info",
"port": 5000
}
Troubleshooting 🛠️
If you encounter issues while using MCP-Mem0, consider the following common problems:
- Server Not Starting: Ensure that all dependencies are installed correctly.
- API Errors: Check the request format and ensure the server is running.
- Memory Not Saving: Verify that the
agent_id
is unique and correctly formatted.
Roadmap 🗺️
We have exciting plans for future updates! Here are some features we aim to implement:
- User Authentication: Secure your memory management with user accounts.
- Data Visualization: Graphical representation of memory data.
- Multi-Agent Support: Handle multiple agents simultaneously.
Stay tuned for these features and more!
Community 💬
Join our community to share your experiences, ask questions, and get support:
- Discord: Join our Discord Server
- Forum: Visit our Forum
We encourage you to engage with other users and contribute to discussions.
Final Thoughts 💭
Thank you for exploring MCP-Mem0! We believe this tool will be a valuable asset for anyone working with agent memory management. Your feedback is essential, so feel free to reach out with suggestions or improvements.
For the latest updates, don’t forget to visit the Releases section again. Happy coding!
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.