Maxim
4b17ea7d35
The #4893 hard-fail gate's loader-call detector gave WRONG VERDICTS IN BOTH DIRECTIONS. It layered two regexes — a comment/string/template alternation that blanked only the comment branch, and `\b(?:import|require(?:\.resolve)?)\s*\(` over the result — then classified an argument as static from the FIRST CHARACTER after the paren. All nine shapes below were reproduced against the real gate before the rewrite: false FAIL throw new Error("use require(path) instead") false FAIL `import(${x})` inside a template false FAIL o.import(y) / mod.require(x) (member calls, not loaders) false PASS /https:\/\//; …import(n) (the regex's `//` blanked the rest of the line, hiding a real dynamic call) false PASS import(`stream${n}`) (merely STARTS with a quote) false PASS import("zo" + n) (same) false PASS import(`${base}/v2/index.mjs`) (same — the fat entry) false PASS __require(name) (no \b inside `__require`) Replaced with `scanSource`, a single-pass tokenizer that classifies every character as code / comment / string / template / regex and returns a length-preserving masked view plus a literal-span list. The one surviving regex now only ever sees code, so import-shaped TEXT cannot reach it at all; an argument counts as static only when it is one COMPLETE literal with no concatenation or interpolation; `__require` is matched; and a member call is rejected both by lookbehind and by a whitespace-skipping back-scan (so `m\n .import(x)` is not a loader either). Proven in both directions: nine innocent/violation pairs run through the real `assertEntryPurity`, each innocent form CLEAN and each matching real violation FAIL. Re-proved end-to-end by prepending `import "streamdown"` to the real dist/v2/headless.mjs — exit 1 naming all five families — then restoring it byte-identically. On the untouched dist the scan sees 66 loader calls in the `.cjs` graph and classifies all 66 static, so it passes because it LOOKED. Also adds the first `.cjs` fixtures: every existing fixture was `.mjs`, leaving the script's `format: "cjs"` branch and the `require()` shape asserted by nothing. Tests 24 → 47. `stripComments` is renamed `maskNonCode`, since it now blanks literals and regexes too; it had no caller outside this script and its test. The RN guard keeps its own copy, untouched. dev-docs/bundle-size.md: the four holes a sibling agent documented as known limitations this round are closed and removed from that list; what genuinely remains (regex-vs-division heuristic, no JSX/TS, indirect loaders) replaces them. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
CopilotKit
Build agent-native applications — on any framework, on any surface.
Generative UI, shared state, and human-in-the-loop workflows for React, Angular, Vue, React Native — and beyond the browser.
What is CopilotKit
CopilotKit is a best-in-class SDK for building full-stack agentic applications, Generative UI, and chat applications.
What started as a React library is now a multi-platform agentic framework: the same agent can power your web app, your mobile app, and your team's Slack workspace.
We are the company behind the AG-UI Protocol - adopted by Google, LangChain, AWS, Microsoft, Mastra, PydanticAI, and more!
Quick Start
Up and running in under five minutes. All you need is an LLM key (OpenAI, Anthropic, Gemini, etc.).
npx copilotkit@latest create
Agent Skills
CopilotKit ships agent skills that teach your coding agent (Claude Code, Codex, Cursor, Gemini, and others) how to set up, build with, integrate, debug, and upgrade CopilotKit.
Install them into any project directory:
npx copilotkit@latest skills install
Run it again any time to refresh to the latest skills.
Bring Your App to Life
https://github.com/user-attachments/assets/72b7b4f3-b6e7-460c-a932-5746fe3c8db3
Features:
- Chat UI – A fully customizable chat interface that supports message streaming, tool calls, and agent responses.
- Backend Tool Rendering – Enables agents to call backend tools that return UI components rendered directly in the client.
- Generative UI – Allows agents to generate and update UI components dynamically at runtime based on user intent and agent state.
- Shared State – A synchronized state layer that both agents and UI components can read from and write to in real time.
- Human-in-the-Loop – Lets agents pause execution to request user input, confirmation, or edits before continuing.
- Self-Learning (early access) – Agents that continuously improve from user feedback via in-context reinforcement learning (CLHF).
🧩 Works With Your Stack
One agent backend. Every frontend.
| Platform | Status | Get Started |
|---|---|---|
| ⚛️ React / Next.js | ✅ GA | Quickstart |
| 🅰️ Angular | ✅ Supported | Source Code & Quickstart |
| 💚 Vue | ✅ Supported | Source Code - Quickstart coming soon |
| 📱 React Native | ✅ Supported | Quickstart |
| 💬 Slack / MS Teams / Discord / Google Chat | 🟡 Beta | Request early access |
Your agent logic stays the same — AG-UI handles the wire protocol, CopilotKit handles the UI layer for each framework.
💬 Beyond the Browser: Slack & Microsoft Teams (Discord, Google Chat coming soon...)
Your agents can run and generate Generative UI beyond the web app (Learn more).
CopilotKit now lets you deploy the same agent to the places your users already work:
- Slack – Agents as first-class Slack apps: threads, tool calls, and human-in-the-loop approvals right in the channel.
- Microsoft Teams – Bring agentic workflows to the enterprise, where your org already lives.
🔒 Early access: We're onboarding teams now.
🧠 Self-Learning Agents
Improve your product by learning over time.
With Continuous Learning from Human Feedback (CLHF), part of the CopilotKit Intelligence Platform, agents improve with every interaction:
- In-context reinforcement learning – Agents automatically improve from user interactions, no model fine-tuning required.
- Automatic prompt augmentation – Agent behavior adapts based on recent interactions and outcomes.
- Per-user adaptation – Agents learn individual preferences and get better for each user over time.
- Threads & persistence – Full interaction history — generative UI, human-in-the-loop, shared state — captured across sessions.
Available via CopilotKit Cloud or self-hosted.
🔒 Early access: We're onboarding teams now.
https://github.com/user-attachments/assets/7372b27b-8def-40fb-a11d-1f6585f556ad
What this gives you:
- CopilotKit installed – Core packages are fully set up in your app
- Provider configured – Context, state, and hooks ready to use
- Agent <> UI connected – Agents can stream actions and render UI immediately
- Deployment-ready – Your app is ready to deploy
Complete getting started guide →
How it works:
CopilotKit connects your UI, agents, and tools into a single interaction loop.
This enables:
- Agents that ask users for input
- Tools that render UI
- Stateful workflows across steps and sessions
- One agent, deployed across web, mobile, and chat platforms
⭐️ useAgent Hook
The useAgent hook sits directly on AG-UI, giving you full programmatic control over the agent connection.
// Programmatically access and control your agents
const { agent } = useAgent({ agentId: "my_agent" });
// Render and update your agent's state
return <div>
<h1>{agent.state.city}</h1>
<button onClick={() => agent.setState({ city: "NYC" })}>
Set City
</button>
</div>
Check out the useAgent docs to learn more.
https://github.com/user-attachments/assets/67928406-8abc-49a1-a851-98018b52174f
Generative UI
Generative UI is a core CopilotKit pattern that allows agents to dynamically render UI as part of their workflow.
https://github.com/user-attachments/assets/3cfacac0-4ffd-457a-96f9-d7951e4ab7b6
Compare the Three Types
Explore:
Generative UI educational repo →
🖥️ AG-UI: The Agent–User Interaction Protocol
Connect agent workflows to user-facing apps, with deep partnerships and 1st-party integrations across the agentic stack—including LangChain, CrewAI, Mastra, PydanticAI, and more.
npx create-ag-ui-app my-agent-app
🤝 Community
Have questions or need help?
Join our Discord →Read the Docs →
Try the Enterprise Intelligence Platform →
Stay up to date with our latest releases!
Follow us on LinkedIn →Follow us on X →
🙋🏽♂️ Contributing
Thanks for your interest in contributing to CopilotKit! 💜
We value all contributions, whether it's through code, documentation, creating demo apps, or just spreading the word.
Here are a few useful resources to help you get started:
-
For code contributions, CONTRIBUTING.md.
-
For documentation-related contributions, check out the documentation contributions guide.
-
Want to contribute but not sure how? Join our Discord and we'll help you out!
📄 License
This repository's source code is available under the MIT License.