Introducing Copilot SDK: Production-Ready AI Copilots for Any App

Over the past year, we’ve seen SaaS products rapidly adopt AI chatbots. They answer questions, guide users, and reduce some support load. While that’s a great start, it’s no longer enough.

People do not come to chat to a software. They come to get things done.

Answers are helpful. Actions are transformative.

We have always believed that the best customer experience does not come from talking to an AI. It comes from an AI that actually does the work.

That belief is why we built the YourGPT Copilot Builder.

It allows teams to build and deploy copilots quickly inside YourGPT, making it a strong fit for business and operational use cases that need speed, reliability and centralized management.

But SaaS products have different needs.

SaaS teams need copilots that understand internal state, permissions, users context, and product logic. They need copilots that can help update data, and act as a native part of the product experience.

That is why we are launching the YourGPT Copilot SDK. An open-source SDK for developers and SaaS teams who want to go beyond conversational AI and build copilots that take action.

Not copilots that sit on the side or ask users to explain context. Copilots that live inside the product, understand product and user context, and act at the moment help is needed.

This is not about adding AI as a feature. It is about redesigning how your product works. Once you see it this way, there is no going back.

What is YourGPT Copilot SDK?

Copilot SDK is an open-source SDK for building production-grade AI agents inside applications.

It is designed for teams who want AI to be a part of product. The SDK gives copilot access to application state, structured context, and controlled actions, so they can operate safely inside the product instead of working in isolation. Rather than assembling streaming interfaces, model APIs, tool execution, and context handling from scratch, Copilot SDK provides a production-ready architecture teams can build on.

With Copilot SDK, AI agents become native parts of the product experience. They can execute custom functions, reason across multiple steps, and coordinate complex product workflows. It can also render interactive UI components, making help feel native and actionable rather than conversational.

The SDK is open source, self hosted, and provider agnostic. Teams connect it to their own infrastructure, choose their preferred LLMs, and retain full control over security, data flow, and execution. There are no black-box abstractions that limit how the product evolves.

This changes the role of AI in software. Instead of telling users what to do, the copilot does the work for them or walks them through it in context.

Copilot SDK vs YourGPT Copilot Widget

These are two different layers, designed for different needs.

The YourGPT Copilot Widget is built for speed and accessibility. It is low-code friendly and works well for business platforms, where fast setup and centralized management is requried.

Copilot SDK is built for developers. It is designed for deep frontend and backend integration, where AI needs to behave like a native part of the product and interact directly with product logic, state, and permissions.

Who Copilot SDK Is For

Copilot SDK is built for teams that want AI to be a core part of their product experience.

It is a strong fit for SaaS startups, developer-first products, and teams building modern web applications who want AI to act, not just respond. If your users expect AI to complete tasks, guide workflows, and reduce manual effort inside the product, this SDK is designed for that purpose.

Copilot SDK helps simplify complex products by letting AI handle work where users need it most, directly in the flow of the application.

Common Integration Challenges When Implementing AI in Production Applications

The primary obstacle to successful AI implementation is is the engineering complexity of building stable, maintainable systems. Organizations consistently encounter four fundamental integration problems that determine whether AI capabilities can be reliably delivered to users.

1. Frontend behavior becomes complex as soon as responses are streamed. Partial outputs need to render correctly, state must remain consistent across re-renders and refreshes, and error conditions must be handled without breaking the interface. Without careful design, teams end up with flickering text, stuck loading states, or UI elements that desync from the underlying conversation state.
   
2. Backend integration introduces its own set of challenges. LLM providers differ in request formats, streaming behavior, error responses, and rate limits. Teams must manage credentials securely, handle retries, and normalize responses so the rest of the application can rely on a consistent contract. These differences tend to leak into application code, making future changes harder than expected.
   
3. Application context is another persistent issue. User questions often depend on what they are currently viewing or doing, but the model has no direct access to that information. Teams manually assemble context for each request, deciding what state to include and what to omit. Over time, this logic becomes scattered, fragile, and difficult to reason about.
   
4. Action execution raises the bar further. When AI is expected to interact with application logic, teams must ensure that inputs are validated, execution is deterministic, and results are reflected accurately in the user interface. Coordinating model responses with application state requires careful sequencing and error handling to avoid inconsistent outcomes.

These problems are not unique to any one product. Most teams spend a significant amount of time solving the same integration concerns before they can focus on how AI should actually support their users.

How Copilot SDK Eliminates These Repeated Problems

Copilot SDK address these recurring integration concerns directly, without imposing assumptions about how an application should behave.

On the frontend, the SDK provides components that handle response streaming, rendering, and error states in a consistent way. This removes the need to reimplement the same interaction patterns for every project, while still allowing teams to control layout and presentation.

On the backend, Copilot SDK introduces a unified interface for working with different model providers. Provider-specific differences in streaming, error handling, and request structure are handled within the SDK. Application code interacts with a stable interface, which makes provider changes or upgrades easier to manage over time.

For application context, the SDK allows state to be registered directly from the parts of the app that already own that data. Context is attached to the AI runtime automatically, instead of being manually constructed for each request. This keeps context logic closer to the source of truth and reduces duplication.

Copilot SDK also defines clear boundaries for how the AI interacts with application logic. Tool execution follows explicit contracts, with structured inputs and predictable outputs. This makes it easier to reason about how AI-driven interactions affect the rest of the system.

All of this runs on infrastructure owned by the team using the SDK. Requests flow through the application’s backend, and model providers are accessed directly. There is no intermediary service and no requirement to hand off control over data or execution.

By handling these foundational concerns, Copilot SDK lets teams spend their time on product-specific decisions instead of rebuilding the same integration layer for every AI feature.

Building a Working AI Copilot Inside a Real Application

Once you decide to use the Copilot SDK, the next step is to get it up and running in your product. The goal is not just to show chat bubbles, but to embed a copilot that reliably streams responses, fits into your app’s lifecycle, and provides a foundation for further extension.

1. Install Required Packages

Start by adding the SDK packages that provide the runtime and model integration utilities. At a minimum, you will need the Copilot SDK and the LLM SDK for model access.

npm install @yourgpt/copilot-sdk @yourgpt/llm-sdk zod

npm install @yourgpt/copilot-sdk @yourgpt/llm-sdk zod

You will use @yourgpt/copilot-sdk for the copilot runtime and interface, @yourgpt/llm-sdk for connecting to the model provider, and zod for safe input validation when defining tools if you choose to add them later.

2. Configure the Copilot Provider

Wrap your application with the Copilot provider at the root of your component tree. The provider sets up the runtime so child components can communicate with the copilot backend.

// app/providers.tsx
'use client';

import { CopilotProvider } from '@yourgpt/copilot-sdk/react';

export function Providers({ children }: { children: React.ReactNode }) {
  return (
    <CopilotProvider runtimeUrl="/api/chat">
      {children}
    </CopilotProvider>
  );
}

// app/providers.tsx
'use client';

import { CopilotProvider } from '@yourgpt/copilot-sdk/react';

export function Providers({ children }: { children: React.ReactNode }) {
  return (
    <CopilotProvider runtimeUrl="/api/chat">
      {children}
    </CopilotProvider>
  );
}

The runtimeUrl should point to your backend route that will act as the bridge to the model provider.

3. Add the Chat Interface

Place the copilot interface where users will interact with it. The SDK provides a pre-built component that handles streaming, rendering, and interaction UI.

// app/page.tsx
import { CopilotChat } from '@yourgpt/copilot-sdk/react';

export default function SupportPage() {
  return (
    <div className="container mx-auto p-4">
      <div className="h-[600px]">
        <CopilotChat 
          title="Support Assistant"
          placeholder="Ask about orders, shipping, returns..."
          suggestions={[
            "Where's my order?",
            "Start a return",
            "Check product availability"
          ]}
          className="h-full rounded-xl border"
        />
      </div>
    </div>
  );
}

// app/page.tsx
import { CopilotChat } from '@yourgpt/copilot-sdk/react';

export default function SupportPage() {
  return (
    <div className="container mx-auto p-4">
      <div className="h-[600px]">
        <CopilotChat 
          title="Support Assistant"
          placeholder="Ask about orders, shipping, returns..."
          suggestions={[
            "Where's my order?",
            "Start a return",
            "Check product availability"
          ]}
          className="h-full rounded-xl border"
        />
      </div>
    </div>
  );
}

You now have a functional chat interface. It streams responses in real-time, renders markdown, handles errors gracefully, and shows loading states during processing.

4. Create the Backend Route

The backend route receives messages from the frontend and forwards them to your model provider using the LLM SDK. A simple streaming implementation enables low-latency responses.

// app/api/chat/route.ts
import { streamText } from '@yourgpt/llm-sdk';
import { openai } from '@yourgpt/llm-sdk/openai';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = await streamText({
    model: openai('gpt-5.2'),
    system: `
      You are a helpful support copilot.
      Provide accurate, direct answers based on the app context and requests.
    `,
    messages,
  });

  return result.toTextStreamResponse();
}

// app/api/chat/route.ts
import { streamText } from '@yourgpt/llm-sdk';
import { openai } from '@yourgpt/llm-sdk/openai';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = await streamText({
    model: openai('gpt-5.2'),
    system: `
      You are a helpful support copilot.
      Provide accurate, direct answers based on the app context and requests.
    `,
    messages,
  });

  return result.toTextStreamResponse();
}

Add your OpenAI API key to .env.local:

OPENAI_API_KEY=sk-...

OPENAI_API_KEY=sk-...

Sending the model responses as a stream back to the frontend keeps the UI responsive without waiting for complete text generation.

5. Validate the Integration

At this stage, you should have:

• A Copilot provider initialized at the application root
• A chat interface that can accept user input
• A backend route that streams model responses

This baseline ensures the copilot is integrated into the product and can communicate with an LLM provider, with minimal infrastructure code in your application outside of the provider and route configuration.

6. Gradually Extend With Adding Tools to Your Copilot

A copilot becomes more useful when it can do more than respond with text. Tools are how you give it controlled capabilities inside your app. You decide what the copilot can access, what it can run, and what it can return.

Copilot SDK supports tools in a few forms:

• Backend tools: run on your server, where you already enforce auth, permissions, and access rules.
  
• Frontend tools: run in the browser when you want the copilot to work with client-side state or UI behavior.
  
• Built-in tools: ready-made capabilities you can enable for common needs, such as capturing a screenshot of the current view or reading console output for debugging-style context.
  
• Multi-step tool usage: if a request needs more than one tool call, the copilot can call tools in sequence rather than forcing you to script the full path upfront.

All tools follow a defined contract. Inputs are validated, execution is explicit, and outputs are structured. This keeps behavior predictable and prevents the copilot from acting outside the boundaries you set.

Once tools are registered, the copilot can call them as part of a response, including in multiple steps when a request cannot be handled in a single action.

What Teams Can Build

Teams use Copilot SDK to build AI capabilities that operate directly within product interfaces, with full access to application state and execution context. These implementations go beyond conversational assistance to enable contextual intervention and autonomous problem resolution.

The examples below represent common examples, but the potential applications are limited only by product requirements and team creativity.

Contextual Debugging and Issue Resolution

AI copilots embedded in operational interfaces can diagnose and resolve system issues without user explanation. A billing dashboard copilot observes subscription state, console logs, and error messages in real time. When a payment fails to load, it identifies the root cause from system state, proposes a corrective action, and executes the fix with user approval. The copilot operates on live product state rather than static documentation or user-provided descriptions.

Financial Analysis with Live Data Integration

Banking copilots access complete financial context across accounts, investments, credit cards, and upcoming obligations. Rather than generating text summaries, these implementations render interactive UI components: balance charts, bill timelines, and financial health scores computed from current data. The interface updates automatically as account state changes, maintaining consistency between AI outputs and source data.

Churn Prevention in Support Workflows

Support copilots analyze active ticket context including conversation history, account usage patterns, subscription details, sentiment indicators, and renewal timing. They surface churn risk scores, identify applicable retention offers, and generate customer-appropriate responses. These interventions occur within the support interface, using real-time customer data rather than historical snapshots or manual context assembly.

Dynamic Trip Planning with Interactive Mapping

Travel planning copilots build itineraries while observing user preferences, budget constraints, and booking state as interactions progress. The copilot annotates locations directly on an interactive map in real time, showing reasoning and options as the itinerary develops. Context updates automatically as users modify preferences or booking parameters, maintaining consistency between AI recommendations, map annotations, and current planning state.

Common Characteristics

These use cases share foundational requirements that Copilot SDK addresses directly:

• Live state access: Copilots read current UI state, application data, and system status without requiring users to describe context
  
• Contextual execution: Actions are proposed and executed based on observed state rather than inferred intent
  
• Interface integration: AI outputs render as native UI components, maintaining visual and interaction consistency
  
• Autonomous operation: Common issues and questions are resolved within the product interface, reducing dependency on external support channels

Teams implement these capabilities without building separate AI infrastructure. The copilot becomes an extension of the product itself, with direct access to the same state and operations available to other application components. You can also Try the interactive playground: https://copilot-sdk.yourgpt.ai/playground

How to Decide If Copilot SDK Fits Your Product

Before adding an AI copilot, it helps to decide whether AI should simply answer questions or actively participate in how your product works. Copilot SDK is built for the second case. It fits when AI needs to operate with the same awareness and constraints as the rest of your application.

1. Your product already has screens that drive user decisions:
   If users make choices based on what they see on a page, charts, settings, configuration states, or logs, then AI needs access to that same view to be useful. Copilot SDK fits when answers must depend on what is currently on screen, not on generic explanations.
   
2. Your product behavior depends on permissions and state:
   If different users see different data or can take different actions, AI cannot be treated as a separate layer. Copilot SDK fits when the copilot must follow the same rules as the product, using the same permissions and visibility checks.
   
3. Your product workflows do not end in a single response:
   If helping a user often requires multiple steps, checking conditions, or coordinating actions, a simple request-response model breaks down. Copilot SDK fits when AI needs to reason across steps instead of replying once and stopping.
   
4. Your team wants AI to remain maintainable as the product changes:
   If screens change, flows evolve, or models are swapped, AI should not require a rewrite every time. Copilot SDK fits when AI is expected to adapt alongside the product rather than being rebuilt for each change.
   
5. Your team cannot afford AI behavior that feels inconsistent:
   If users lose trust when AI behaves differently across similar screens or gives answers that do not match the product state, tighter integration becomes necessary. Copilot SDK fits when consistency matters more than flexibility.
   
6. You want AI issues to be debuggable not mysterious:
   If something goes wrong, teams need to understand why. Copilot SDK fits when AI behavior should be traceable through context, tool calls, and state rather than appearing as a black box.

Conclusion

The YourGPT Copilot SDK is available today, fully open source.

We’re launching first with our community—on Discord, Twitter, and Newsletter. we can’t wait to see what you build. We’re starting with React and JavaScript, with plans to expand to more frameworks like Vue in the near future.

We believe the next generation of SaaS will be different. Our vision is simple: Superior customer experience comes from actions, not just conversations.

A copilot should behave like a native part of the product, not something added on top. When AI understands product state, user context, and business rules, changes to the product do not break the copilot. It evolves alongside the product.

Copilot SDK provides a stable foundation so teams do not have to rebuild core AI infrastructure for every use case. Engineering effort stays focused on how AI fits into real product workflows. The SDK is open source and self hosted, teams stay in control of deployment, data flow, and behavior. The copilot evolves alongside the product instead of requiring repeated rewo

If your goal is to integrate AI directly into your product architecture, Copilot SDK gives you the foundation to do it without rebuilding the same infrastructure from scratch.

Read the quickstart guide: https://copilot-sdk.yourgpt.ai/docs/quickstart
Star the repo: https://github.com/YourGPT/copilot-sdk