showcase/shell-docs/src/content/docs/integrations/langgraph/frontend-tools.mdx
<IframeSwitcher id="frontend-actions-example" exampleUrl="https://feature-viewer.copilotkit.ai/langgraph/feature/agentic_chat?sidebar=false&chatDefaultOpen=false" codeUrl="https://feature-viewer.copilotkit.ai/langgraph/feature/agentic_chat?view=code&sidebar=false&codeLayout=tabs" exampleLabel="Demo" codeLabel="Code" height="700px" />
Frontend tools enable you to define client-side functions that your LangGraph agent can invoke, with execution happening entirely in the user's browser. When your agent calls a frontend tool, the logic runs on the client side, giving you direct access to the frontend environment.
This can be utilized to let your agent control the UI, for generative UI, or for Human-in-the-loop interactions.
In this guide, we cover the use of frontend tools driving and interacting with the UI.
Use frontend tools when you need your agent to interact with client-side primitives such as:
<Step>
### Create a frontend tool
First, you'll need to create a frontend tool using the [useFrontendTool](/reference/v2/hooks/useFrontendTool) hook. Here's a simple one to get you started
that says hello to the user.
```tsx title="page.tsx"
import { z } from "zod";
import { useFrontendTool } from "@copilotkit/react-core/v2" // [!code highlight]
export function Page() {
// ...
// [!code highlight:12]
useFrontendTool({
name: "sayHello",
description: "Say hello to the user",
parameters: z.object({
name: z.string().describe("The name of the user to say hello to"),
}),
handler: async ({ name }) => {
alert(`Hello, ${name}!`);
return `Said hello to ${name}!`;
},
});
// ...
}
```
</Step>
<Step>
### Install the CopilotKit SDK
Now, we'll need to modify the agent to access these frontend tools. In your terminal, navigate to your agent's folder and continue from there!
<InstallSDKSnippet/>
</Step>
<Step>
### Inheriting from CopilotKitState
To access the frontend tools provided by CopilotKit, you can inherit from CopilotKitState in your agent's state definition:
<Tabs groupId="language_langgraph_agent" items={['Python', 'TypeScript']} default="Python" persist>
<Tab value="Python">
```python title="agent.py"
from copilotkit import CopilotKitState # [!code highlight]
class YourAgentState(CopilotKitState): # [!code highlight]
your_additional_properties: str
```
</Tab>
<Tab value="TypeScript">
```typescript title="agent-js/src/agent.ts"
import { StateSchema } from "@langchain/langgraph";
import { CopilotKitStateSchema } from "@copilotkit/sdk-js/langgraph"; // [!code highlight]
import { z } from "zod";
export const YourAgentStateSchema = new StateSchema({
yourAdditionalProperty: z.string(),
...CopilotKitStateSchema.fields, // [!code highlight]
});
export type YourAgentState = typeof YourAgentStateSchema.State;
```
</Tab>
</Tabs>
By doing this, your agent's state will include the `copilotkit` property, which contains the frontend tools that can be accessed and invoked.
</Step>
<Step>
### Accessing Frontend Tools
Once your agent's state includes the `copilotkit` property, you can access the frontend tools and utilize them within your agent's logic.
Here's how you can call a frontend tool from your agent:
<FrontEndToolsImpl />
These tools are automatically populated by CopilotKit and are compatible with LangChain's tool call definitions, making it straightforward to integrate them into your agent's workflow.
</Step>
<Step>
### Give it a try!
You've now given your agent the ability to directly call any frontend tools you've defined. These tools will be available to the agent where they can be used as needed.
<video src="https://cdn.copilotkit.ai/docs/copilotkit/images/frontend-actions-demo.mp4" className="rounded-lg shadow-xl" loop playsInline controls autoPlay muted />
</Step>