AG-UI is a brand new open standard protocol designed to bridge the gap between powerful AI agents and real-time user-facing applications. It offers a lightweight, event-based protocol that standardizes the way AI agents connect to front-end interfaces, whether it's a web app, mobile app, or any custom UI.

AG-UI makes agent-human interaction seamless, modular, and consistent. It is specifically tailored for developers building interactive AI workflows that require real-time updates and shared state, as well as human-in-the-loop collaboration.

The AG-UI Dojo is a demo playground that showcases the core building blocks of AG-UI, highlighting features like streaming updates, chat interfaces, and shared states. These demos are implemented in just 50-200 lines of code, making it easy for developers to understand, customize, and reuse these components in their own projects.

AG-UI is designed to be simple, modular, and practical, allowing you to integrate it into your real-world AI applications with minimal setup. It offers an open protocol for connecting AI agents to front-ends, compatible with tools like Copilot Kit, and provides efficient communication patterns, streaming support, and best practices for chat state updates and user-agent coordination.

By using AG-UI, you can turn fragmented, backend-heavy workflows into collaborative, human-in-the-loop experiences with minimal code. It is a powerful tool that can revolutionize the way you integrate your AI agents with your front-end applications, enabling seamless and consistent interactions.

The AG-UI Dojo is a demo playground that highlights the core building blocks of the AG-UI protocol, a lightweight event-based protocol designed to standardize the connection between AI agents and front-end interfaces. This dojo provides a hands-on exploration of various features, including streaming updates, chat interfaces, and shared states, all implemented in just 50 to 200 lines of code.

The goal of the AG-UI Dojo is to keep things simple, modular, and practical, making it easy for developers to understand, customize, and reuse these components in their own projects. By showcasing the capabilities of AG-UI, the dojo demonstrates how this protocol can be used to create real-world AI applications with minimal setup, allowing for seamless connectivity between AI agents and front-end applications.

The dojo features several interactive demos, each focusing on a specific feature of the AG-UI protocol:

1. Genetic Chat: This demo showcases the ability to chat with a co-bot and call front-end tools, such as changing the background color.
2. Human-in-the-Loop: This demo allows you to plan a task together with the AI and direct the co-pilot to take the necessary steps.
3. Agentic Generative UI: This demo showcases the ability to assign long-running tasks to the co-pilot and observe its performance.
4. Tool-based Generative UI: This demo demonstrates the use of tools within the front-end, such as generating a haiku about a specific topic.
5. Shared State: This demo features a recipe co-bot that reads and updates collaboratively, showcasing predictive state and no-chat functionality.
6. External Demo: This demo showcases the integration of co-agents in research workflows, allowing you to assign different agents for various tasks.

These interactive demos provide a comprehensive understanding of the capabilities of the AG-UI protocol, highlighting its potential to enhance the user experience and collaboration between AI agents and front-end applications.

In addition to the dojo, the AG-UI documentation also provides guidance on how to build an HTTP endpoint compatible with the AG-UI protocol, enabling seamless integration with agent frameworks like Copilot Kit. This step-by-step guide covers the setup of a Python project, the installation of dependencies, and the implementation of a simple "Hello World" response, progressing to more advanced features such as parsing structured agent input data and implementing service-sent events for real-time event training.

By exploring the AG-UI Dojo and the accompanying documentation, developers can gain a deeper understanding of how the AG-UI protocol can be leveraged to create interactive, collaborative, and efficient AI-powered applications with minimal effort.

To get started with AG-UI, you'll need to have the following prerequisites:

• Node.js and npm or yarn installed
• An OpenAI API key

First, open your command prompt and set your OpenAI API key:

# Windows
set OPENAI_API_KEY=your_api_key_here

# macOS/Linux
export OPENAI_API_KEY=your_api_key_here

Next, clone the AG-UI repository:

git clone https://github.com/Anthropic-AI/ag-ui.git

Navigate to the ag-ui directory and then the dojo folder:

cd ag-ui
cd dojo

Install the necessary dependencies:

npm install

Finally, start the AG-UI playground:

npm start

This will open the AG-UI playground in your default web browser, where you can explore the various demos and see how AG-UI can connect your AI agent to the front-end.

The playground showcases different features of AG-UI, such as:

• Genetic chat: Interact with a co-bot and make changes to the front-end.
• Human-in-the-loop: Plan tasks collaboratively with the AI agent.
• Agentic generative UI: Assign long-running tasks to the co-pilot.
• Tool-based generative UI: Use tools within the front-end, like generating a haiku.
• Shared state: Collaborate on a recipe co-bot.

You can also refer to the "Build with AG-UI" documentation to learn how to integrate AG-UI into your own project, including setting up a Python-based HTTP endpoint compatible with the AG-UI protocol.

AG-UI is a lightweight, event-based protocol that standardizes the connection between AI agents and front-end interfaces. It enables seamless integration between powerful AI agents and real-time, user-facing applications, transforming fragmented, backend-heavy workflows into collaborative, human-in-the-loop experiences with minimal code.

The AG-UI Dojo is a demo playground that showcases the core building blocks of AG-UI, highlighting features like streaming updates, chat interfaces, and shared states. These demos are implemented in just 50-200 lines of code, making it easy for developers to understand, customize, and reuse these components in their own projects.

AG-UI is designed for developers building interactive AI workflows that require real-time updates and shared state. It offers a simple, open protocol for connecting AI agents to front-ends, compatible with tools like Copilot Kit, while providing efficient communication patterns, streaming support, and best practices for chat state updates and user-agent coordination.

To get started with AG-UI, you'll need to set up your development environment with Node.js, npm or yarn, and an OpenAI API key. Once you've cloned the AG-UI repository and installed the necessary dependencies, you can explore the Dojo and see the various interactive demos in action, such as the Genetic Chat, Human-in-the-Loop, and Toolbased Generative UI.

The AG-UI documentation also provides a step-by-step guide on building an HTTP endpoint compatible with the AG-UI protocol, enabling seamless integration with agent frameworks like Copilot Kit. This guide covers everything from setting up a Python project with Poetry to implementing service-sent events for real-time event training.

By leveraging AG-UI, developers can create AI-powered applications with ease, bridging the gap between powerful AI agents and user-facing interfaces. This standardized protocol offers a modular and practical approach to building collaborative, human-in-the-loop experiences, empowering developers to bring their AI-driven ideas to life with minimal setup and maximum impact.

Agui is a powerful open-standard protocol that bridges the gap between AI agents and front-end applications, enabling seamless integration and real-time collaboration. By providing a lightweight, event-based communication layer, Agui simplifies the development of interactive AI workflows and human-in-the-loop experiences.

The Agui Dojo showcases the versatility of the protocol, demonstrating features like streaming updates, chat interfaces, and shared states. These modular components make it easy for developers to understand, customize, and reuse in their own projects.

Agui's compatibility with popular AI frameworks, such as Langchain, Maestra, and Anthropic's Agent2, further enhances its utility. Developers can leverage Agui to connect their AI agents to a wide range of front-end applications, from web apps to mobile interfaces.

The documentation provided by the Agui team offers a comprehensive guide on building HTTP endpoints compatible with the Agui protocol, making it accessible for developers of all skill levels. By following the step-by-step instructions, developers can seamlessly integrate their AI agents with modern front-end technologies.

In summary, Agui is a game-changing protocol that simplifies the integration of AI agents into real-time, user-facing applications. Its modular design, open-source nature, and extensive documentation make it a valuable tool for developers seeking to create innovative, collaborative AI-powered experiences.