🚀 mcp-rag-agent - Build Smart, Context-Aware AI Agents

The mcp-rag-agent is a powerful application designed to help you build reliable AI agents. It integrates the LangGraph with the Model Context Protocol (MCP), offering advanced features like semantic search and grounded responses. With this system, you can create context-aware agents that understand and respond to user queries effectively.

To get started with mcp-rag-agent, follow the simple steps below to download and set up the software on your device.

• Semantic Search: Leverage MongoDB Atlas Vector Search for quick and accurate results.
• Grounded Responses: Use COSTAR prompting to ensure your AI delivers relevant information.
• Automated Evaluation: Built-in RAGAS-based evaluation helps create dependable AI agents.
• User-Friendly Interface: Easy-to-navigate design simplifies the experience for everyone.

1. Visit the Releases Page: Click the link below to access our GitHub Releases page where you can download the program.

   Download Latest Release

2. Choose the Right Version: On the Releases page, find the latest version of mcp-rag-agent. Ensure you select the correct version for your operating system.

3. Download the Installer: Click on the download link to save the file to your computer.

4. Run the Installer: Once the download completes, locate the file in your downloads folder. Double-click the file to start the installation process. Follow the on-screen instructions to install the application.

Before you start, make sure your system meets the following requirements:

• Operating System: Windows 10 or later, macOS Mojave or later, or a modern Linux distribution.
• RAM: At least 4 GB of RAM.
• Storage: Minimum 200 MB of free space for installation.
• Internet Connection: Required for downloading updates and accessing some features.

After installation, follow these steps to use the application:

1. Launch the Application: Open mcp-rag-agent from your applications folder or desktop shortcut.

2. Create a New Project: Click on "New Project" to start building your AI agent. Give your project a name and set parameters.

3. Set Up Data Sources: Import data from various sources to help your AI learn. You can configure settings based on your needs.

4. Train Your Model: Use the built-in training features to help your AI understand and respond accurately.

5. Deploy Your Agent: Once training is complete, you can deploy your agent and start interacting with it.

If you have questions or need help, consider reaching out to our community. You can find discussions, tips, and troubleshooting advice in the repository’s Issues section.

For more detailed instructions and guidelines, visit our documentation page. It includes tutorials, FAQs, and best practices for optimizing your experience.

We welcome contributions from everyone. If you want to help improve mcp-rag-agent, please follow these steps:

1. Fork the Repository: Create your copy of the project to make changes.
2. Make Changes: Implement new features or fix issues in your copy.
3. Submit a Pull Request: Share your modifications so we can collaborate and enhance the project together.

• GitHub Repository: Explore the source code and track issues at mcp-rag-agent.
• Project Roadmap: Check our plans for future features and updates in the Roadmap.

Thank you for choosing mcp-rag-agent. We appreciate your interest and hope this tool helps you build effective AI solutions. For feedback or suggestions, feel free to raise an issue on GitHub.

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