AG-UI vs CopilotKit
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
An open protocol that standardizes how AI agents connect to user-facing apps.
Open source · MIT
A TypeScript toolkit for building chat and generative UI on top of AI agents in web, mobile, and chat apps.
The short answer
Choose AG-UI if you need…
- Developers adding an AI agent to a web or in-app user interface
- Teams using a supported agent framework such as LangChain, CrewAI, or Google ADK who want a standard frontend connection
- Builders who want real-time streaming chat and shared state between agent and UI
Choose CopilotKit if you need…
- Developers adding an AI assistant or agent interface to a React or Next.js app
- Teams who want one agent backend to serve a web app, a mobile app, and Slack or Teams
- Projects that need agents to ask users for confirmation or edits before continuing
- Developers building chat interfaces that render custom UI components from tool calls
Side-by-side
| AG-UI | CopilotKit | |
|---|---|---|
| Type | Framework | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 17k | 38.5k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | TypeScript | TypeScript |
| Last code update | 5 days ago | 4 days ago |
What each one is
AG-UI
AG-UI is an open, lightweight, event-based protocol that defines how AI agent backends send events to a user interface and accept simple inputs in return. The README says it works over any event transport such as SSE or WebSockets, and it ships with a reference HTTP implementation and a default connector. Developers use it to bring agents into frontend applications, such as chat, shared state, and human-in-the-loop workflows.
CopilotKit
CopilotKit is an SDK for building applications where AI agents interact with users through a interface. It provides pieces such as a chat UI, shared state between agents and components, generative UI that agents can render at runtime, and human-in-the-loop prompts that pause an agent for user input. The README says the same agent can be served to React, Angular, Vue, React Native, and Slack or Microsoft Teams. The project is maintained by the company behind the AG-UI Protocol.
Best for
AG-UI
- Developers adding an AI agent to a web or in-app user interface
- Teams using a supported agent framework such as LangChain, CrewAI, or Google ADK who want a standard frontend connection
- Builders who want real-time streaming chat and shared state between agent and UI
CopilotKit
- Developers adding an AI assistant or agent interface to a React or Next.js app
- Teams who want one agent backend to serve a web app, a mobile app, and Slack or Teams
- Projects that need agents to ask users for confirmation or edits before continuing
- Developers building chat interfaces that render custom UI components from tool calls
Strengths
AG-UI
- Built on a small set of about 16 standard event types, per the README
- Transport-flexible: the README says it works with SSE, WebSockets, webhooks, and others
- Has a broad list of supported frameworks and an MIT license
CopilotKit
- Covers several frontends (React, Angular, Vue, React Native) and chat channels from one agent backend, per the README
- Built on the AG-UI Protocol, which the README says is adopted by several large framework vendors
- Permissive MIT license with an active repository and a large star count
Not ideal for
AG-UI
- People who only want a ready-made chat product without writing code, since the README describes a protocol and developer tooling
- Projects that need tool access for agents alone, since the README positions MCP for that role rather than AG-UI
CopilotKit
- Non-developers looking for a ready-made app; the README targets people writing code
- Projects that do not use JavaScript or TypeScript frontends, since the listed frameworks are JS-based
- Teams who want a fully self-contained product without integrating an agent framework of their own
Trade-offs
AG-UI
- The README is truncated and does not give full API details, so integration effort is hard to judge from it alone
- Several listed integrations are marked In Progress or rely on partner documentation hosted elsewhere
CopilotKit
- The README is partly a marketing overview; several features (Channels for Discord/WhatsApp, Vue quickstart) are listed as coming soon or only partly supported
- Some advanced features (rich threads, memories, analytics) belong to a separate hosted or self-hosted product called CopilotKit Intelligence, which may have its own plans
- Setup depends on your agent framework and frontend stack, so the learning curve is non-trivial
Getting started
AG-UI
Create a new AG-UI application
npx create-ag-ui-app my-agent-appCopilotKit
Set up with your coding agent (run from your project root)
npx --yes copilotkit@latest onboard startInstall agent skills for your coding agent
npx copilotkit@latest skills installFrequently asked questions
What is the difference between AG-UI and CopilotKit?
AG-UI: An open protocol that standardizes how AI agents connect to user-facing apps. CopilotKit: A TypeScript toolkit for building chat and generative UI on top of AI agents in web, mobile, and chat apps. AG-UI suits developers adding an ai agent to a web or in-app user interface, while CopilotKit suits developers adding an ai assistant or agent interface to a react or next.js app.
Are AG-UI and CopilotKit free?
AG-UI: Free and open source. CopilotKit: Free and open source.
Is AG-UI or CopilotKit more popular?
CopilotKit has more GitHub stars (38.5k) than AG-UI (17k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to AG-UI and CopilotKit?
Other options people compare them with include assistant-ui, LangGraph, Tambo, PageIndex, OpenAI Agents SDK.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.