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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? -...

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Atai Barkai

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11,997 views • 4 months ago •via X (Twitter)

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CopilotKit🪁

30,557 views • 8 months ago

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.
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17,150 views • 1 year ago

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Atai Barkai

23,776 views • 5 days ago

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.
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Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

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450,887 views • 18 days ago

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Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

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46,109 views • 1 year ago

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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:

CopilotKit🪁

18,510 views • 6 months ago

AI agents can finally talk to your frontend! The new AG-UI protocol lets your agents stream messages, sync state, call tools, and receive user input, live in the UI. It fills a critical gap in the agent ecosystem: ▸ MCP – agents to tools ▸ A2A – agents to agents ▸ AG-UI – agents to users, via frontends Connects to LangChain, CrewAI, and LlamaIndex (and more). 100% open-source.
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AI agents can finally talk to your frontend! The new AG-UI protocol lets your agents stream messages, sync state, call tools, and receive user input, live in the UI. It fills a critical gap in the agent ecosystem: ▸ MCP – agents to tools ▸ A2A – agents to agents ▸ AG-UI – agents to users, via frontends Connects to LangChain, CrewAI, and LlamaIndex (and more). 100% open-source.

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15,514 views • 1 year ago

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 👇
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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🪁

20,456 views • 1 month ago

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🚀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

CopilotKit🪁

51,637 views • 1 year ago

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CopilotKit🪁

22,080 views • 1 year ago

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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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:

CopilotKit🪁

32,118 views • 1 year ago

Introducing 21st Agent Elements Diffed edits. Plan approvals. Clarifying questions. Sub-agents. Tool cards. The pieces every "agent UI" lib skips. 25 shadcn /ui components built for the new generation of agents that use your computer. Open-source. Link in thread 🧵
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serafim

41,784 views • 2 months ago

generative ui is no longer a skill issue. 1. npx skills add tambo-ai/tambo --skill generative-ui 2. /generative-ui an agent that manages tasks what will you build?
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generative ui is no longer a skill issue. 1. npx skills add tambo-ai/tambo --skill generative-ui 2. /generative-ui an agent that manages tasks what will you build?

Michael Magán

67,525 views • 3 months ago

build an agent that speaks your UI. your charts. your forms. your seat maps. multi-turn, streaming, interactive. introducing tambo 1.0, the open-source generative UI toolkit for react.
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261,308 views • 4 months ago

New AI Agentic course! Learn to use LangGraph to build single and multi-agent LLM applications in AI Agents in LangGraph. This short course, taught by LangChain LangChain founder Harrison Chase Harrison Chase and Tavily founder @weiss_rotem, shows how to integrate agentic search to enhance an agent's knowledge with query-focused answers in predictable formats. Also learn to implement agentic memory to save state for reasoning and debugging, and see how human-in-the-loop input can guide agents at key junctures. You'll build an agent from scratch, then reconstruct it with LangGraph to thoroughly understand the framework. Finally, you'll build a sophisticated essay-writing agent that incorporates all the learnings from the course. Sign up here!
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151,484 views • 2 years ago