---
title: Human-in-the-Loop
icon: "lucide/Wrench"
description: Create frontend tools and use them within your Pydantic AI agent for human-in-the-loop interactions.
---
import InstallSDKSnippet from "@/snippets/install-sdk.mdx"
import RunAndConnect from "@/snippets/integrations/pydantic-ai/run-and-connect.mdx"
import { IframeSwitcher } from "@/components/content"

<IframeSwitcher
  id="frontend-tools-based-hitl-example"
  exampleUrl="https://feature-viewer.copilotkit.ai/pydantic-ai/feature/human_in_the_loop?sidebar=false&chatDefaultOpen=false"
  codeUrl="https://feature-viewer.copilotkit.ai/pydantic-ai/feature/human_in_the_loop?view=code&sidebar=false&codeLayout=tabs"
  exampleLabel="Demo"
  codeLabel="Code"
  height="700px"
/>

## What is this?

Frontend tools enable you to define client-side functions that your Pydantic AI 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](/pydantic-ai/frontend-tools), [generative UI](/pydantic-ai/frontend-tools), or for Human-in-the-loop interactions.

In this guide, we cover the use of frontend tools for Human-in-the-loop.

## When should I use this?

Use frontend tools when you need your agent to interact with client-side primitives such as:
- Reading or modifying React component state
- Accessing browser APIs like localStorage, sessionStorage, or cookies
- Triggering UI updates or animations
- Interacting with third-party frontend libraries
- Performing actions that require the user's immediate browser context

## Implementation

<Steps>
    <Step>
      ### Run and connect your agent
      <RunAndConnect components={props.components} />
    </Step>

    <Step>
        ### Create a frontend human-in-the-loop tool

        Frontend tools can be leveraged in a variety of ways. One of those ways is to have a human-in-the-loop flow where the response
        of the tool is gated by a user's decision.

        In this example we will simulate an "approval" flow for executing a command. First, use the `useHumanInTheLoop` hook to create a tool that
        prompts the user for approval.

        ```tsx title="page.tsx"
        import { useHumanInTheLoop } from "@copilotkit/react-core/v2" // [!code highlight]

        export function Page() {
          // ...

          useHumanInTheLoop({
            name: "offerOptions",
            description: "Give the user a choice between two options and have them select one.",
            parameters: [
              {
                name: "option_1",
                type: "string",
                description: "The first option",
                required: true,
              },
              {
                name: "option_2",
                type: "string",
                description: "The second option",
                required: true,
              },
            ],
            render: ({ args, respond }) => {
              if (!respond) return <></>;
              return (
                <div>
                  {/* [!code highlight:2] */}
                  <button onClick={() => respond(`${args.option_1} was selected`)}>{args.option_1}</button>
                  <button onClick={() => respond(`${args.option_2} was selected`)}>{args.option_2}</button>
                </div>
              );
            },
          });

          // ...
        }
        ```
    </Step>
    <Step>
        ###  Set up your agent

        Pydantic AI automatically picks up human-in-the-loop tools when you create your agent. No special configuration is needed:

        ```python title="agent.py"
        from pydantic_ai import Agent

        agent = Agent('openai:gpt-5.4-mini')
        app = agent.to_ag_ui()
        ```

        The frontend tools are automatically populated by CopilotKit through the AG-UI protocol and are available to your agent.
    </Step>
    <Step>
        ### Try it out!

        You've now given your agent the ability to show the user two options and have them select one. The agent will then be aware of the user's choice and can use it in subsequent steps.

        ```
        Can you show me two good options for a restaurant name?
        ````
    </Step>
</Steps>
