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🚀 Introducing CopilotKit for Angular Build agentic Angular apps with: → Drop-in chat or your own UI → Generative UI with A2UI → Shared state → Human-in-the-loop approvals Built on AG-UI and contributed by Angular GDE Rainer Hahnekamp and Murat Sari. Great collaborating on this. Read more:

CopilotKit🪁

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25,118 просмотров • 9 дней назад •via X (Twitter)

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🪁🦜CoAgents x LangGraph Bring your agents into React apps with powerful and fully customizable React hooks. Styled or headless UI. Including Agentic Chat, realtime Shared State, Frontend tools, Generative UI, and Human-in-the-Loop. Used by Fortune 100 teams & thousands of devs. Get started: LangChain

🪁🦜CoAgents x LangGraph Bring your agents into React apps with powerful and fully customizable React hooks. Styled or headless UI. Including Agentic Chat, realtime Shared State, Frontend tools, Generative UI, and Human-in-the-Loop. Used by Fortune 100 teams & thousands of devs. Get started: LangChain

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17,150 просмотров • 1 год назад

Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇

Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇

CopilotKit🪁

31,845 просмотров • 9 месяцев назад

One hook connects any Agent to your frontend Then layer on chat UI, threads, generative UI, voice transcription, tool calls, state sync, HITL, and any AI feature. We make it easy for devs to build fullstack agentic apps. Everything here is built with CopilotKit 👇

One hook connects any Agent to your frontend Then layer on chat UI, threads, generative UI, voice transcription, tool calls, state sync, HITL, and any AI feature. We make it easy for devs to build fullstack agentic apps. Everything here is built with CopilotKit 👇

CopilotKit🪁

20,520 просмотров • 2 месяцев назад

Microsoft 🤝 AG-UI Protocol Microsoft Agent Framework is now AG-UI compatible! 🎉 Connect your MS Agent Framework .NET agents into fullstack applications with Python coming soon 👀 MS Agent Framework takes care of the agentic backend, AG-UI connects your agents to the frontend. Features include: - Chat UI - Generative UI - State synchronization - Human-in-the-loop - Frontend Tool Calls All the building blocks you need to take your agents from the backend and into a fullstack agentic application. Available in React & Angular via CopilotKit, and Rust, Kotlin, Rust, Java and .NET via community SDKs Big shoutout to the Microsoft & CopilotKit teams, who shipped this highly requested integration at breakneck speeds. More coming soon 👀 🙏 Javier Nelson, Joonseok Oh, Daniel Roth, Evan Mattson, Tyler Slaton, Brandon McConnell, Markus Ecker Read more about our collaboration: Get started: Check out the Dojo:

Microsoft 🤝 AG-UI Protocol Microsoft Agent Framework is now AG-UI compatible! 🎉 Connect your MS Agent Framework .NET agents into fullstack applications with Python coming soon 👀 MS Agent Framework takes care of the agentic backend, AG-UI connects your agents to the frontend. Features include: - Chat UI - Generative UI - State synchronization - Human-in-the-loop - Frontend Tool Calls All the building blocks you need to take your agents from the backend and into a fullstack agentic application. Available in React & Angular via CopilotKit, and Rust, Kotlin, Rust, Java and .NET via community SDKs Big shoutout to the Microsoft & CopilotKit teams, who shipped this highly requested integration at breakneck speeds. More coming soon 👀 🙏 Javier Nelson, Joonseok Oh, Daniel Roth, Evan Mattson, Tyler Slaton, Brandon McConnell, Markus Ecker Read more about our collaboration: Get started: Check out the Dojo:

CopilotKit🪁

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Introducing the A2UI Widget Builder ⚙️ An interactive demo and template to build A2UI Widgets and bring them into your application. Powered by CopilotKit & Google's new A2UI spec Includes... - Widget Gallery: premade UI widgets you can use & modify - Material Icons: 100 of the most commonly used icons - UI Components: common components preview, usage and props - Source code and data on any widget/icon/component - Built with CopilotKit- so you get chat UI, generative UI, etc. Try it out: Tutorial of how to get started: Repo:

Introducing the A2UI Widget Builder ⚙️ An interactive demo and template to build A2UI Widgets and bring them into your application. Powered by CopilotKit & Google's new A2UI spec Includes... - Widget Gallery: premade UI widgets you can use & modify - Material Icons: 100 of the most commonly used icons - UI Components: common components preview, usage and props - Source code and data on any widget/icon/component - Built with CopilotKit- so you get chat UI, generative UI, etc. Try it out: Tutorial of how to get started: Repo:

CopilotKit🪁

18,510 просмотров • 7 месяцев назад

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.

Pratham

33,920 просмотров • 6 месяцев назад

Stop leaving your Agent stateless. Give it Threads in one hook. Resumable conversations that capture everything: messages, generative UI, shared state, human-in-the-loop workflows. Built on AG-UI, so it works with any compatible agent framework. docs:

Stop leaving your Agent stateless. Give it Threads in one hook. Resumable conversations that capture everything: messages, generative UI, shared state, human-in-the-loop workflows. Built on AG-UI, so it works with any compatible agent framework. docs:

Atai Barkai

40,879 просмотров • 2 месяцев назад

Introducing CoAgents v0.2🪁🦜🚀 Build full-stack Agent-Native Apps with LangGraph 🤝 CopilotKit (Like Cursor for __) Including: ✨Agentic UI/UX + Frontend 🪢 Shared state (agent app) 👨‍🦰Human-in-the-Loop 🦜Full LangGraph Platform support (Python, JS, Cloud) Get Started: Check out the demo:

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CopilotKit🪁

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Introducing Shadify: Generative UI built on ShadCN Simply describe a UI and watch your LangChain agent compose from @ShadCN on the fly, using AG-UI and CopilotKit🪁. Then export it as React code. Repo below. Try it out here:

Introducing Shadify: Generative UI built on ShadCN Simply describe a UI and watch your LangChain agent compose from @ShadCN on the fly, using AG-UI and CopilotKit🪁. Then export it as React code. Repo below. Try it out here:

Atai Barkai

138,046 просмотров • 4 месяцев назад

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 просмотров • 1 месяц назад

600 SVGs available on Svgl 🥳🚀 ✓ Fully optimized ✓ React, Vue, Angular, Astro, Svelte & Web Components ✓ Add SVGs with the shadcn/ui CLI ✓ Free API ✓ Raycast extension, npm packages, and CLIs built by contributors and 100% free, open source ✨

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Pablo

95,527 просмотров • 4 месяцев назад

Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:

Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:

Atai Barkai

11,997 просмотров • 5 месяцев назад

Introducing Adabraka UI — a Rust-powered desktop UI library built for speed, control, and beauty. Inspired by shadcn and built on gpui by Zed Here’s it in action: a music player and task manager built entirely with Adabraka UI. #RustLang #DesktopDev #OpenSource

Introducing Adabraka UI — a Rust-powered desktop UI library built for speed, control, and beauty. Inspired by shadcn and built on gpui by Zed Here’s it in action: a music player and task manager built entirely with Adabraka UI. #RustLang #DesktopDev #OpenSource

pythonxi

44,514 просмотров • 9 месяцев назад

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Akshay 🚀

86,515 просмотров • 2 месяцев назад

🚀Launching CoAgents Public Beta 🪁: Everything you need to build Agent-Native applications, powered by LangGraph & CopilotKit. CoAgents enables in-app agents with: - Agenetic generative UI ✨ - Shared state (between agent application) - Streaming intermediate agent state - Human-in-the-Loop (Human approval & planning) 👨‍🦰 - Frontend Actions 💪 --- We’ve recently seen the emergence of Agent-Native applications that actually work: powered by vertical AI agents + agentic UX + deep in-app integration. (Apps like the Replit AI Agent, OpenAI Canvas, etc, but for any domain) With CoAgents + LangGraph, you can build this next generation of agent apps, for any vertical, with a clean and easy to use infrastructure (in hours). 💻Get started: 🗞️Blog: 🕹️Demo: 🗓️Webinar (Build an Agent-Native Apps with CoAgents & LangGraph): CC: Atai Barkai, , Harrison Chase

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CopilotKit🪁

51,662 просмотров • 1 год назад

Introducing Open Tag. A better, open-source Claude Tag. Works with any model, any agent harness, and fully custom agents. Supports → Generative UI → Streaming replies → Human in the Loop approvals → Full thread context Slack and MS Teams today. Discord, Google Chat, WhatsApp soon. Request early access:

Introducing Open Tag. A better, open-source Claude Tag. Works with any model, any agent harness, and fully custom agents. Supports → Generative UI → Streaming replies → Human in the Loop approvals → Full thread context Slack and MS Teams today. Discord, Google Chat, WhatsApp soon. Request early access:

Atai Barkai

444,968 просмотров • 1 месяц назад

Regal officially has a sergal on the shop! Our handsomely angular friend comes with their plushy, Stitches, a retexture of the player head item. Video also shows a peek at the new UI organization. More drop down menus = more emotes and outfits. -Kaiyssia

Regal officially has a sergal on the shop! Our handsomely angular friend comes with their plushy, Stitches, a retexture of the player head item. Video also shows a peek at the new UI organization. More drop down menus = more emotes and outfits. -Kaiyssia

RegalB

78,064 просмотров • 1 год назад

🚀 Announcing Ark UI Ark™ The robust headless UI component library, built with State machines 🥳💪✨ 🔸Over 30+ components (ColorPicker, DatePicker, and more...) 🔸Works in React, Solid, and Vue 🔸Bring your own styling solution

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Sage ⚡️

159,213 просмотров • 2 лет назад

Don't just learn how to build AI agents. Instead, learn how to build... ...full-stack AI agents that actually work inside apps. Yet, the main challenge is transitioning them from local setups to user-facing apps. Here are some key factors to consider: - How do you embed a seamless UI? - How do you enable user control when necessary? - How do you unify the agent’s state and actions with the app? CoAgents by CopilotKit🪁 solves this. It is the only framework that offers all the essential Agentic front-end building blocks to set up full-stack agentic apps. And most recently, they have extended full-stack development support to Agents built with CrewAI ✨. This includes both regular Agentic Crews and Flows. Check their GitHub repo it's 100% open-source, I have shared link in the next tweet! With this update, here's what you can do with Agents built using CrewAI: ↳ Chat: Add chat components for interaction. ↳ Human-in-the-loop: Real-time human approvals. ↳ Predictive state updates: Preview upcoming changes. ↳ Agentic UI: Agents generate UI components dynamically. This way, you can give your CrewAI Agents a frontend brain—so they don’t just work behind the scenes, they become an integral part of the user experience. The video below showcases a full-stack CrewAI Agent built in 20 minutes using CoAgents. Impressive, right?

Don't just learn how to build AI agents. Instead, learn how to build... ...full-stack AI agents that actually work inside apps. Yet, the main challenge is transitioning them from local setups to user-facing apps. Here are some key factors to consider: - How do you embed a seamless UI? - How do you enable user control when necessary? - How do you unify the agent’s state and actions with the app? CoAgents by CopilotKit🪁 solves this. It is the only framework that offers all the essential Agentic front-end building blocks to set up full-stack agentic apps. And most recently, they have extended full-stack development support to Agents built with CrewAI ✨. This includes both regular Agentic Crews and Flows. Check their GitHub repo it's 100% open-source, I have shared link in the next tweet! With this update, here's what you can do with Agents built using CrewAI: ↳ Chat: Add chat components for interaction. ↳ Human-in-the-loop: Real-time human approvals. ↳ Predictive state updates: Preview upcoming changes. ↳ Agentic UI: Agents generate UI components dynamically. This way, you can give your CrewAI Agents a frontend brain—so they don’t just work behind the scenes, they become an integral part of the user experience. The video below showcases a full-stack CrewAI Agent built in 20 minutes using CoAgents. Impressive, right?

Akshay 🚀

27,153 просмотров • 1 год назад

Microsoft Teams just got Generative UI But this is just the tip of the iceberg. AG-UI is the protocol connecting any agent to any user on any app, topped with Generative UI. That means your agent can render: - forms - buttons - charts - approval flows Right inside the chat, on the fly. But it's not limited to in-app copilots. What's special is that you can work with any agent using the same system and bring them into any surface, including Slack, WhatsApp, Discord and more. This is exactly why Hermes and OpenClaw took off. As users of these agents, we don't like walls of text, and it's a pain to get when you can have way better UX. CopilotKit🪁 + AG-UI handle that interaction layer. If you want to try out Agentic UI in Microsoft Teams, you can get early access here:

Microsoft Teams just got Generative UI But this is just the tip of the iceberg. AG-UI is the protocol connecting any agent to any user on any app, topped with Generative UI. That means your agent can render: - forms - buttons - charts - approval flows Right inside the chat, on the fly. But it's not limited to in-app copilots. What's special is that you can work with any agent using the same system and bring them into any surface, including Slack, WhatsApp, Discord and more. This is exactly why Hermes and OpenClaw took off. As users of these agents, we don't like walls of text, and it's a pain to get when you can have way better UX. CopilotKit🪁 + AG-UI handle that interaction layer. If you want to try out Agentic UI in Microsoft Teams, you can get early access here:

Atai Barkai

24,595 просмотров • 1 месяц назад