Langchain: NEW Agent UI + Deploy Multi-Agents With MCPs, Memory, Tools & Reasoning! (Opensource)

Key Concepts

• Copilot Kit: An open-source framework for embedding AI co-pilots into applications.
• MCP (Model Context Protocol): An open standard for secure two-way communication between AI and application data/tools.
• MCP Client: An application that can communicate with MCP servers.
• MCP Server: A server that exposes tools and data to AI agents via the MCP protocol.
• Langchain, Langraph, CrewAI: Frameworks for building AI agents and workflows.
• ComposeIO: A platform providing pre-built MCP servers.
• Copilot CLI: Command-line interface for deploying Copilot Kit applications.
• Next.js: A React framework for building web applications.
• Multi-Agent Canvas: A user interface for interacting with multiple AI agents.

Copilot Kit and MCP Integration

The video introduces a significant update to Copilot Kit, focusing on the integration of MCP client-side tooling. This integration simplifies the process of connecting applications to external tools and services using the Model Context Protocol (MCP). MCP, developed by Anthropic, enables secure, two-way communication between AI models and application data or tools. This allows AI to interact with real-time context and take actions within the application.

Demonstration: React App as an MCP Client

The video demonstrates how to turn a React app into an MCP client using Copilot Kit. The presenter, Nathan, shows how a single command can enable communication with any MCP server, allowing it to be used as a tool call within the app.

Example Scenario: Managing a launch campaign.

• Problem: Tasks need to be sent to Asana, tweets scheduled, but the tools don't communicate with each other, and there's no standard MCP client.
• Solution: Copilot Kit allows quick integration with MCP servers.
• Process:
  1. Send a blog post list to Asana via an MCP server. The AI assistant provides a link to Asana.
  2. Create tweet drafts for the campaign using another MCP server. The AI generates tweet drafts that can be reviewed and filled out.

Adding MCP to an Application: Step-by-Step

The video outlines the steps to integrate MCP servers into an application using Copilot Kit:

1. Select an MCP Server: Choose an MCP server to work with (e.g., from ComposeIO or a custom-built server).
2. Installation: Follow the installation process for the chosen MCP server, which may vary depending on the tool (e.g., Gmail MCP).
3. Copilot CLI: Use the Copilot CLI to deploy the MCP server within a Next.js application.
4. Create a Next.js App (if needed): Use the command mpx create next app in the terminal to create a new Next.js project.
5. Initialize the Project: Navigate to the project directory (cd my-app).
6. Add MCP to the App: Run the provided command (specific to Copilot Kit) to integrate MCP functionality.
7. Deployment: Choose to deploy with Copilot Cloud or use a local host.
8. API Key: Enter an OpenAI API key.

Multi-Agent Canvas Demo

The video showcases a demo of using a Copilot Kit MCP client with multiple Langchain or Langraph agents.

Example Scenario: Planning a business trip.

• Goal: Find good food in Miami with AI assistance.
• Process:
  1. The research assistant searches the internet and suggests restaurants.
  2. A human-in-the-loop factor allows confirming or denying suggestions.
  3. The selected restaurant is added to a map.
  4. The itinerary is sent to Slack using the Slack MCP server.
  5. The Slack MCP server URL is added to the application.
  6. The Slack channel ID is specified.
  7. The itinerary is posted to the Slack channel.

Cloud Demo and Code Interpreter

The video briefly demonstrates a cloud-based demo of Copilot Kit with MCP integration.

• MCP Servers List: Add servers via SSC (ComposeIO) or standard IO.
• Code Interpreter Example: Provide faulty code to the code interpreter MCP server, which then solves the code.

Key Arguments and Perspectives

• Copilot Kit streamlines the process of building multi-modal co-pilots.
• MCP integration enhances AI functionality within applications.
• Copilot Kit provides an agent UI and tools (MCPs, plugins) to enhance agent capabilities (Langchain, CrewAI).
• The open-source nature of Copilot Kit benefits the AI community.

Synthesis/Conclusion

The Copilot Kit update with MCP integration offers a powerful and streamlined framework for building AI co-pilots that can interact with external tools and data. By leveraging the MCP standard, developers can create context-aware AI agents that seamlessly integrate into their applications, enabling real-time actions and enhanced user experiences. The video highlights the ease of integration and the potential for various applications, from task management to trip planning, making Copilot Kit a valuable tool for developers looking to incorporate AI into their workflows.