CopilotKit
React UI + elegant infrastructure for AI Copilots, AI chatbots, and in-app AI agents. The Agentic last-mile 🪁
Stars: 17501
CopilotKit is an open-source framework for building, deploying, and operating fully custom AI Copilots, including in-app AI chatbots, AI agents, and AI Textareas. It provides a set of components and entry points that allow developers to easily integrate AI capabilities into their applications. CopilotKit is designed to be flexible and extensible, so developers can tailor it to their specific needs. It supports a variety of use cases, including providing app-aware AI chatbots that can interact with the application state and take action, drop-in replacements for textareas with AI-assisted text generation, and in-app agents that can access real-time application context and take action within the application.
README:
Transform spreadsheets with natural language data analysis and formula generation. Users can ask questions about their data, create complex formulas through conversation, and get instant visualizations—all without knowing a single Excel function.
A context-aware financial assistant that analyzes transactions, provides spending insights, and helps users manage their finances through natural conversation. Demonstrates how CopilotKit can integrate deeply with complex data structures.
Transform tedious form-filling into natural conversations. The AI assistant asks the right questions, understands context, and completes forms automatically—no more field-by-field drudgery.
Transform your data visualization experience with an AI-powered dashboard assistant. Ask questions about your data in natural language, get insights, and interact with your metrics—all through a conversational interface powered by CopilotKit.
Transform complex conversational flows into manageable state machines. This AI-powered car sales application demonstrates how to build sophisticated multi-stage interactions with contextual awareness and state transitions.
An agent-native application that helps users plan trips by generating detailed itineraries, finding attractions, and visualizing travel plans. Shows how agents can collaborate with users to create rich, interactive experiences.
Multi-agent document analysis system that helps users analyze papers, synthesize information, and generate comprehensive research summaries through collaborative AI workflows.
Get started in minutes - check out the quickstart documentation.
// Headless UI with full control
const { visibleMessages, appendMessage, setMessages, ... } = useCopilotChat();
// Pre-built components with deep customization options (CSS + pass custom sub-components)
<CopilotPopup
instructions={"You are assisting the user as best as you can. Answer in the best way possible given the data you have."}
labels={{ title: "Popup Assistant", initial: "Need any help?" }}
/>
// ---
// Frontend RAG
useCopilotReadable({
description: "The current user's colleagues",
value: colleagues,
});
// knowledge-base integration
useCopilotKnowledgebase(myCustomKnowledgeBase)
// ---
// Frontend actions + generative UI, with full streaming support
useCopilotAction({
name: "appendToSpreadsheet",
description: "Append rows to the current spreadsheet",
parameters: [
{ name: "rows", type: "object[]", attributes: [{ name: "cells", type: "object[]", attributes: [{ name: "value", type: "string" }] }] }
],
render: ({ status, args }) => <Spreadsheet data={canonicalSpreadsheetData(args.rows)} />,
handler: ({ rows }) => setSpreadsheet({ ...spreadsheet, rows: [...spreadsheet.rows, ...canonicalSpreadsheetData(rows)] }),
});
// ---
// structured autocomplete for anything
const { suggestions } = useCopilotStructuredAutocompletion(
{
instructions: `Autocomplete or modify spreadsheet rows based on the inferred user intent.`,
value: { rows: spreadsheet.rows.map((row) => ({ cells: row })) },
enabled: !!activeCell && !spreadsheetIsEmpty,
},
[activeCell, spreadsheet]
);// Share state between app and agent
const { agentState } = useCoAgent({
name: "basic_agent",
initialState: { input: "NYC" }
});
// agentic generative UI
useCoAgentStateRender({
name: "basic_agent",
render: ({ state }) => <WeatherDisplay {...state.final_response} />,
});
// Human in the Loop (Approval)
useCopilotAction({
name: "email_tool",
parameters: [{ name: "email_draft", type: "string", description: "The email content", required: true }],
renderAndWaitForResponse: ({ args, status, respond }) => (
<EmailConfirmation
emailContent={args.email_draft || ""}
isExecuting={status === "executing"}
onCancel={() => respond?.({ approved: false })}
onSend={() => respond?.({ approved: true, metadata: { sentAt: new Date().toISOString() } })}
/>
),
});
// ---
// intermediate agent state streaming (supports both LangGraph.js + LangGraph python)
const modifiedConfig = copilotKitCustomizeConfig(config, {
emitIntermediateState: [{
stateKey: "outline",
tool: "set_outline",
toolArgument: "outline"
}],
});
const response = await ChatOpenAI({ model: "gpt-4o" }).invoke(messages, modifiedConfig);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!
💡 NOTE: All contributions must be submitted via a pull request and be reviewed by our team. This ensures all contributions are of high quality and align with the project's goals.
You are invited to join our community on Discord and chat with our team and other community members.
This repository's source code is available under the MIT License.
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