CopilotKit🪁 @CopilotKit
The Frontend Stack for Agents & Generative UI. Open-Source (MIT) Creators of the AG-UI Protocol. copilotkit.ai San Francisco Joined May 2021-
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Join our German 🇩🇪 Online Event about Agentic UI with @angular and @CopilotKit
AG-UI, A2UI & MCP Apps: Wie verbinden sich AI Agents und Benutzeroberflächen? Manfred Steyer zeigt im Live Event, wie diese Standards mit Angular & CopilotKit für interaktive AI-Anwendungen eingesetzt werden. 23.10. | 13 Uhr f.mtr.cool/z8rawmbgj8
What does modern Angular architecture look like in the age of AI? This week at @JS_Days in Berlin, we explored two sides of the equation: 🏗️ Architectures both humans and AI agents can understand and follow. 🤖 Agentic Angular apps powered by @CopilotKit and AG-UI.
Claude Managed Agents can power many different UIs. Our new cookbook pairs Claude Managed Agents with AG-UI, @CopilotKit's open Agent-User Interaction Protocol.
Clone it, customize it, and make it yours. Try OpenMuse: github.com/CopilotKit/ope…
OpenMuse is live on Product Hunt 🚀 It's the best open source template of Meta Muse: - #1 Repo of the Day - 5k+ GitHub ⭐️ - 700+ forks Please support and Upvote here: producthunt.com/products/copil…
🎉 Introducing 𝙾𝚙𝚎𝚗𝙼𝚞𝚜𝚎 An open source, self-hostable personal assistant that works with any agent harness. Includes: - Computer use: browser, terminal & files - Connectors for your personal apps - Ideas, goals & progress tracking - Built for Mobile and Web Repo →
Clone it, customize it, and make it yours. Try OpenMuse: github.com/CopilotKit/ope…
CopilotKit just released CLiF (Continual Learning from Full-Stack Trajectories), a platform that teaches AI agents from what users actually do in the product, not just from what the agent did. The core idea is the full-stack trajectory: the agent trace (runs, tool calls, state changes) and the user's product actions (screen, clicks, server requests) captured as one ordered timeline over AG-UI. CLiF analyzes those trajectories, surfaces Insights like repeated corrections and friction users quietly work around, and proposes skills as plain SKILL.md files. A human approves every skill. Published skills reach the agent without a redeploy, and can ship with an MCP server so external agents like Claude follow them too. The same trajectories also become production-based evals, fine-tuning datasets for smaller open models, and raw exports for any observability stack. Fully self-hostable on Kubernetes or AWS ECS/Fargate...... Full analysis: marktechpost.com/2026/10/15/cop… CLiF: copilotkit.ai/clif?utm_sourc… Technical details: copilotkit.ai/blog/introduci… @CopilotKit
Want to give your @LangChain agent a UI in minutes? Just copy one prompt: docs.copilotkit.ai/langgraph-type…
London's FIRST @LangChain Interrupt💂 It's always great to represent here, especially since AG-UI emerged out of partnership with LangChain back in the day. Come find us at the next one! Thank you @hwchase17 and team 🦜🪁
Give your Google ADK agent Automatic Learning👇
CLiF 🤝 Google ADK Here's a practical how-to guide for adding Automatic Learning to your ADK Agent. (Optimized for coding assistants) CLiF turns real user work into: > Product insights > Approvable skills > A self-improving system Get started: copilotkit.ai/blog/google-ad…
This is the best OS version of OpenAI's Intelligent UI And it's been around for months... Repo (⭐3K): github.com/CopilotKit/Ope…
Introducing 𝙾𝚙𝚎𝚗 𝙸𝚗𝚝𝚎𝚕𝚕𝚒𝚐𝚎𝚗𝚝 𝚄𝙸 A template for bringing interactive, generative UI to your own product. Works with ANY agent. Supports: → Your design system → Your components → Your real data and actions Repo: github.com/CopilotKit/Ope… Available on Web,
Continual Learning works for ANY agent Every agent and user step can be securely captured and turned into Full-stack Trajectories, which can then be used to generate: 1. Evals from real usage 2. Skills for agents & MCP servers 3. Datasets for fine-tuning models Powered by AG-UI & CopilotKit Intelligence. The more your users use your product, the better it gets. Now in early access: go.copilotkit.ai/get-clif
INTRODUCING: CopilotKit CLiF A platform for Continual Learning from Full-stack Trajectories. Teach your agents how work gets done by turning the work users & employees do into: → Exportable datasets (of trajectories) → Evals (bring into platform of choice) → Skills →
Securely capture product trajectories👇
New in AG-UI: Capture everything your users do Their clicks, screens, requests... Then use CLiF to continually improve your product by generating skills & evals. 👉 Get early access to this: go.copilotkit.ai/get-clif
Excited for this @omnaraai integration!
nearly done with a new Omnara integration for @CopilotKit's AG-UI been cool seeing what’s already possible with AG-UI looking forward to seeing what people build with @omnaraai in the mix
Agents are repeating software’s dumbest mistake! The real moat in 2027 won’t be anything to do with the agent. Instead, it’ll be by capturing fullstack trajectories. Imagine two teams ship the same agent, on the same model, on the same day. Your competitor ships it and moves on. But you ship agents that learn from every session: where users stepped in, what they did by hand, what they keep asking for. Every week it picks up a few new skills, evals and datasets. Six months later, your aren't really competing on the same product anymore. Because the advantage isn't in the model but rather it's in your users' data. CLiF is a new learning platform from @CopilotKit that sees every trajectory: → The trace: every step the agent took → The product: everything the users did The AG-UI protocol already sits between the agent and the UI, so it sees both sides. And then turns real sessions into exportable fullstack trajectories, evals, skills and fine-tuned models. The visual below explains how it works and I worked with the CopilotKit team to put this together. You can self-host it, so the moat stays yours. Access it here: go.copilotkit.ai/get-clif My suggestion if you’re building user-facing agents: start collecting this data now. CLiF already implements the entire continual learning infrastructure you need to achieve that. More details below.
INTRODUCING: CopilotKit CLiF A platform for Continual Learning from Full-stack Trajectories. Teach your agents how work gets done by turning the work users & employees do into: → Exportable datasets (of trajectories) → Evals (bring into platform of choice) → Skills →
🎉 Announcing CLiF Securely capture entire product + agent trajectories from real usage. Then use that data to create: - Exportable fullstack trajectories - Evals - Skills - Fine-tuned models Get early access → go.copilotkit.ai/get-clif Powered by AG-UI & CopilotKit Intelligence
INTRODUCING: CopilotKit CLiF A platform for Continual Learning from Full-stack Trajectories. Teach your agents how work gets done by turning the work users & employees do into: → Exportable datasets (of trajectories) → Evals (bring into platform of choice) → Skills →
Man am I stoked for this release, we're making your agents smarter as your users consume them even if they don't directly interact with the chat window. We let your agents learn about painful user journeys and improve themselves to make them smoother 🔥
INTRODUCING: CopilotKit CLiF A platform for Continual Learning from Full-stack Trajectories. Teach your agents how work gets done by turning the work users & employees do into: → Exportable datasets (of trajectories) → Evals (bring into platform of choice) → Skills →
@LangChain @FactoryAI @p0 @tavilyai @focused_dot_io Come learn about building fullstack agentic products 👀
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