In this article, you will discover how to create an agent-native research canvas app that utilizes human-in-the-loop features, using tools like Langraph, CopilotKit, and Tavily. The topics covered include understanding AI agents, visualizing a LangGraph agent using LangGraph Studio, and designing the user interface with CopilotKit. You’ll learn how AI agents can perform research tasks and interact with users for better results. The tutorial provides step-by-step instructions to build an application that collects data, proposes outlines, and generates comprehensive research reports while incorporating user feedback. By the end, you will have a functional application integrating these innovative technologies.
Discover how you can create a powerful agent-native research canvas app using AI technologies like Langraph, CopilotKit, and Tavily. This blog will guide you through the essential steps of building an interactive app that merges human input with AI capabilities.
In this article, we will explore:
– What are AI agents and their functions
– How to visualize a LangGraph AI agent using LangGraph Studio
– Creating a user interface for your LangGraph AI agent with CopilotKit
First, let’s understand what AI agents are. These autonomous software applications can perform various tasks, make decisions, and interact with users while ensuring reliability and trust through human engagement.
You will also learn about CopilotKit, which is a comprehensive framework designed for developing interactive user agents. This tool allows your AI agents to manage applications while providing tailored user interfaces that enhance user experience.
Before diving into coding, ensure you have the necessary tools:
– Basic knowledge of React or Next.js will be beneficial.
– Install Python as it’s essential for building AI agents using LangGraph.
– Get familiar with LangGraph, OpenAI APIs, Tavily AI, and Docker as they will be vital in your development process.
Now, let’s begin building your LangGraph AI agent. Start by cloning the agent-native research canvas app repository. You will find folders for both the agent and the frontend. After setting up the agent directory and installing necessary dependencies, you can create an environment file to store your API keys, which will allow your agent to interact with various services.
Next, visualize your LangGraph AI agent using LangGraph Studio. This tool lets you see the workflow of your agent actively gathering data, generating outlines, and receiving human feedback, which ensures the accuracy of its outputs. Start the agent with a straightforward command, and voilà! You can observe the agent’s processes in real time.
The next step involves building the user interface through CopilotKit, enabling users to interact smoothly with the LangGraph agent. Establish a connection to the CopilotCloud, install the frontend dependencies, and start the application.
To enhance user interaction, set up a shared state between your UI and the LangGraph agent. This feature allows users to see live updates and progress, creating an engaging experience.
By employing the Human-in-the-loop approach, you can ensure that your agent requests user approval for essential decisions, adding a layer of verification. When the AI agent generates a research proposal, users can give feedback, ensuring the output meets their expectations.
In the final steps, implement streaming capabilities to display the AI agent’s responses in real-time. This feature allows users to follow the development of the research report directly on the UI, improving the interactivity of your tool.
Through this tutorial, you’ve gained insights on utilizing advanced tools like LangGraph and CopilotKit to create an engaging, interactive research application. You can find the full source code on GitHub to further explore and expand upon your new skills.
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Tags: AI Agents, LangGraph, CopilotKit, Research Canvas, Interactive Applications, Human-in-the-loop, Software Development, OpenAI.
What is LangGraph?
LangGraph is a tool that helps you create a user interface for your AI agent quickly and easily. You can build chatbots, apps, and more without needing to be a coding expert.
How does CopilotKit work?
CopilotKit is a companion tool that offers smart suggestions while you build your UI. It helps you choose colors, layouts, and features, making the design process smoother and faster.
Do I need coding skills to use these tools?
No, you don’t need any coding skills. LangGraph and CopilotKit are designed for everyone. They provide simple drag-and-drop features so you can design your AI agent without writing code.
Can I customize my UI using these tools?
Yes, you can fully customize your UI! You can change colors, fonts, and even the layout. Both LangGraph and CopilotKit give you flexibility to make your design unique.
How quickly can I build a UI for my AI agent?
You can build a UI in just minutes! With the easy-to-use features of LangGraph and CopilotKit, you can have your AI agent up and running very fast.