Unlock the Power of AI CoAgents: Building Intelligent Apps with Copilot

Table of Contents

• What is CoAgents?¶
• Build AI CoAgents or Apps Powered by Copilot¶
• How do I build my own Copilot?¶
• Can Copilot generate code?¶

CoAgent presents a robust toolkit designed to seamlessly integrate LanGraph agents into React applications, paving the way for the development of Agent-Native applications. This article will explore the capabilities of this toolkit and demonstrate how you can leverage it to construct AI CoAgents, or intelligent applications empowered by Copilot.

What is CoAgents?¶

As previously mentioned, CoAgent serves as a comprehensive toolkit that facilitates the integration of AI agents into applications, enabling the creation of innovative Agent-Native experiences. Let’s delve deeper into the key features that define CoAgents and its potential.

Shared State¶

One of the standout features of CoAgents is its Shared State capability. This powerful feature allows for real-time synchronization and interaction between your application and the integrated AI agent with minimal code. Essentially, with just a single line of code, your application can effortlessly access insights from the agent, and conversely, the agent can readily retrieve insights from the application. This synchronized environment fosters a dynamic exchange of information, enhancing the responsiveness and intelligence of your application. This bidirectional flow of information in real-time is crucial for creating truly interactive and adaptive applications.

Realtime Frontend Agents¶

CoAgents also provides Realtime Frontend Agents, which are designed to execute actions on both the frontend and backend of your application. These agents are context-aware, meaning they can dynamically adapt their behavior based on user input and the current state of the application. Leveraging generative UI principles, CoAgents automatically disseminates tool calls as needed, ensuring a seamless and intuitive user experience. This adaptability allows for the creation of user interfaces that are not only dynamic but also highly responsive to user needs and application conditions. The real-time nature of these agents ensures immediate reaction and adaptation, making applications feel more alive and intelligent.

Human-in-the-Loop¶

The Human-in-the-Loop feature of CoAgents is crucial for applications requiring oversight and control. It allows for the seamless integration of human intervention into AI workflows. By specifying breakpoints within the agent’s process, developers can introduce stages where human input or approval is required. This is particularly important in scenarios where safety, accuracy, or ethical considerations are paramount. This feature ensures that AI-driven processes are not entirely autonomous but can be guided and validated by human expertise, enhancing both the reliability and trustworthiness of AI applications. It strikes a balance between automation and human control, leading to more robust and responsible AI systems.

Stream Intermediate Agent State¶

Stream Intermediate Agent State offers a unique window into the inner workings of AI agents. This feature visualizes the agent’s thought processes in real-time, providing transparency and a more engaging user experience. By allowing users to observe how the agent is reasoning and making decisions, it fosters trust and understanding. This transparency is not only beneficial for user experience but also crucial for developers to monitor agent performance and fine-tune its behavior. Understanding the agent’s thought process helps to align user expectations with the agent’s capabilities, leading to greater user satisfaction and effective application usage.

Agentic Generative UI¶

CoAgent’s Agentic Generative UI capabilities represent a significant step forward in user interface design. It enables the creation of dynamic, AI-generated interfaces that adapt on-the-fly to user needs and agent outputs. These interfaces are not static but evolve based on the interaction and context, providing users with relevant information and controls at the right time. Furthermore, this feature enhances visibility into the agent’s state, building user trust by demonstrating the agent’s activity and reasoning directly within the UI. This dynamic and adaptive UI approach results in more intuitive, personalized, and efficient applications, fundamentally changing how users interact with software.

If these features resonate with your business needs, exploring CoAgents further might be highly beneficial. Let’s now examine the practical steps involved in building AI CoAgents or Copilot-powered applications.

Build AI CoAgents or Apps Powered by Copilot¶

To begin constructing AI CoAgents or applications powered by Copilot, follow these structured steps:

1. Install CopilotKit
2. Configure Remote Backend Endpoint
3. Add LangGraph agent

Let’s delve into each of these steps in detail to guide you through the process.

1] Install CopilotKit¶

Before diving into CoAgents, the first crucial step is to install CopilotKit on your development environment. This process assumes you have already set up Node.js and npm (Node Package Manager) on your system. While creating a React application folder is generally recommended for React development, it is optional for this initial installation step. CopilotKit leverages open-source LLM (Large Language Model) models, and for this guide, we will be utilizing the OpenAI API key. Let’s proceed with the installation without further delay.

• Open Windows Terminal (or your preferred terminal on other operating systems) and navigate to the location where you intend to create your React application project using the cd (change directory) command.

cd C:\React\myapplication

• Next, execute the following command to install the necessary CopilotKit packages:

npm install @copilotkit/react-core @copilotkit/react-ui @copilotkit/runtime

This command will install three essential packages: @copilotkit/react-core for core React functionalities, @copilotkit/react-ui for UI components, and @copilotkit/runtime for the runtime environment.

• After installing the CopilotKit packages, you need to install the OpenAI library. Run the following command:

npm install openai

This command installs the OpenAI Node.js library, which will be used to interact with the OpenAI API.

• Within your project directory, locate the .env file in the root directory. If it doesn’t exist, create one. Open the .env file and add the following line, replacing your_api_key_here with your actual OpenAI API key:

OPENAI_API_KEY=your_api_key_here

It is highly recommended to consult the official CopilotKit documentation at docs.copilotkit.ai/quickstart to understand how to properly configure the Endpoint and the CopilotKit provider within your project after making these initial configuration changes. This documentation provides comprehensive guidance for setting up your environment and ensuring everything is correctly integrated.

2] Configure Remote Backend Endpoint¶

To integrate services built with Python or other Node.js alternatives, it’s necessary to connect your Copilot application to a Remote Backend endpoint. This allows for communication and data exchange between your frontend application and backend services. Let’s begin by installing the required Copilot dependencies using pip in the Windows Terminal. Execute the following command:

pip install copilotkit fastapi uvicorn --extra-index-url https://copilotkit.gateway.scarf.sh/simple/

This command installs copilotkit, fastapi (a modern, fast web framework for building APIs with Python), and uvicorn (an ASGI server implementation) using pip, the Python package installer. The --extra-index-url flag specifies a custom package index URL, which might be necessary for accessing CopilotKit packages.

Next, we will set up a FastAPI server. To do this, execute the following commands in your Windows Terminal:

mkdir my_copilotkit_remote_endpoint

cd my_copilotkit_remote_endpoint

echo. > server.py

The first command mkdir my_copilotkit_remote_endpoint creates a new directory named my_copilotkit_remote_endpoint. The second command cd my_copilotkit_remote_endpoint navigates into this newly created directory. The third command echo. > server.py attempts to create an empty file named server.py. If the echo command doesn’t work as expected (which can sometimes happen in Windows environments), you can manually create the server.py file using Visual Studio Code or any text editor.

Open the server.py file in Visual Studio Code (or your preferred code editor) and paste the following Python code into it:

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"Hello": "World"}

This code snippet sets up a basic FastAPI application. It imports the FastAPI class, creates an instance of it named app, and defines a simple GET endpoint at the root path (“/”). When this endpoint is accessed, it returns a JSON response {"Hello": "World"}.

Related: loading

Return to your Windows Terminal and install fastapi and uvicorn again, to ensure they are installed within the correct environment if needed.

pip install fastapi uvicorn

For more comprehensive guidance on configuring the Remote Backend endpoint and further customization options, refer to the official CopilotKit documentation at docs.copilotkit.ai/guides/backend-actions/remote-backend-endpoint#set-up-a-fastapi-server. This documentation provides detailed instructions and best practices for setting up and managing your backend services.

3] Add LangGraph agent¶

The next crucial step is to integrate a LangGraph agent into your server.py file. First, you need to locate the CopilotKitSDK instance within your Python Remote Endpoint, which is typically found in your server.py file. Then, adjust the CopilotKitSDK instance to accommodate LangGraph agents. This involves adding specific lines of code to configure and incorporate your LangGraph agent. You will need to add the following code block within your CopilotKitSDK setup:

agents=[
    LangGraphAgent(
        name="basic_agent",
        description="Agent that answers questions about the weather",
        graph=the_langraph_graph, # Replace with your actual LangGraph graph object
        # copilotkit_config={ # if you use Google Gemini, uncomment this code (and import `copilotkit_messages_to_langchain`, see above)
        # "convert_messages": copilotkit_messages_to_langchain(use_function_call=True)
        # }
    ),
],

Ensure that this agents configuration is correctly placed within your CopilotKitSDK initialization block in your server.py file. You will need to replace the_langraph_graph with your actual LangGraph graph object, which defines the logic and workflow of your agent. The commented-out copilotkit_config section is relevant if you are using Google Gemini and need to handle message conversions; otherwise, it can be left commented out.

For a deeper understanding and more detailed instructions on integrating LangGraph agents with Coagent, it is highly recommended to consult the guide available at docs.copilotkit.ai/coagents/getting-started. This resource provides comprehensive information and examples to help you effectively implement LangGraph agents in your Coagent applications.

How do I build my own Copilot?¶

Creating your own Copilot is a straightforward process using Microsoft’s Copilot Studio. To begin, navigate to the Copilot Studio home page, typically found at /copilotstudio.microsoft.com. In the left navigation menu, select Create, and then choose the Copilots page. Alternatively, on the Copilots page, you can directly select + New copilot.

Once you initiate the creation process, you will be presented with a chat interface. Here, you can describe your desired copilot using natural language. The system will guide you with questions to refine your copilot’s purpose and functionality. If you prefer a more direct configuration, you can select Skip to configure and fill out a form with the necessary details. Finally, to finalize the creation process, select Create. This will set up your custom Copilot, ready for further customization and deployment.

Can Copilot generate code?¶

Yes, Copilot is indeed capable of generating code. It is a powerful tool designed to assist developers in writing code more efficiently. However, it’s crucial to acknowledge that Copilot’s code generation is not always flawless. There can be instances of glitches, bugs, or code that is not perfectly aligned with the intended functionality.

Therefore, it is strongly advised against directly integrating code generated by any AI chatbot, including Copilot, without thorough manual inspection and testing. Always treat AI-generated code as a starting point or suggestion rather than a finished product. Carefully review the code, test it rigorously, and make any necessary adjustments to ensure it meets your project’s requirements and standards.

You can effectively utilize Microsoft’s Copilot through various platforms. It is integrated into Microsoft Edge, available as a built-in application, and can also be used within Visual Studio by installing the GitHub Copilot extension. These integrations provide convenient access to Copilot’s code generation and AI assistance features within your preferred development environments.

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We encourage you to explore the potential of AI CoAgents and Copilot in your projects. Share your experiences and thoughts in the comments below!