Human input for agent workflows

ADK에서 지원Python v2.0.0TypeScript v2.0.0Go v2.0.0

Being able to request human input for data input, decision verification, or action permission is an important part of many agent-powered workflows. Graph-based workflows in ADK can include human in the loop (HITL) nodes specifically built for obtaining input from humans as part of a workflow. These nodes do not require artificial intelligence (AI) models to run, which can make the input process more predictable and reliable.

시작하기

PythonTypeScriptGo

You can implement a human input node in a graph using the RequestInput class and a text prompt for the user. The following code example shows how to add a human input node to a Workflow graph:

from google.adk.events import RequestInput
from google.adk import Workflow

def step1(): # Human input step
  yield RequestInput(message="Enter a number:")

def step2(node_input):
  return node_input * 2

root_agent = Workflow(
    name="root_agent",
    edges=[('START', step1, step2)],
)

In this code example, step1 pauses the execution of the agent until the system receives an input from a user. Once the system receives input from the user, that input is passed to the next node.

ADK TypeScript v2.0.0에서 인간 입력 노드는 RequestInput을 yield합니다. step1 노드는 사용자가 응답할 때까지 워크플로를 일시 중지하고, 사용자의 응답은 다음 노드의 입력으로 전달됩니다. 인간 참여형(HITL) 노드는 모델을 필요로 하지 않으므로 일시 중지가 결정론적입니다.

import { node, NodeContext, RequestInput, Workflow } from '@google/adk';

const step1 = node(
  async function* () {
    yield new RequestInput({ message: 'Enter a number:' });
  },
  { name: 'step1' },
);

const step2 = node(
  (_ctx: NodeContext, nodeInput: string | number) => {
    const value = Number(nodeInput);
    return Number.isFinite(value)
      ? value * 2
      : `"${nodeInput}" is not a number.`;
  },
  { name: 'step2' },
);

export const rootAgent = new Workflow({
  name: 'root_agent',
  edges: [['START', step1, step2]],
});

이 구현은 기본값인 rerunOnResume: false 핸드오프를 보여줍니다. 중단된 노드는 다시 실행되지 않으며, 사용자의 응답을 출력으로 삼아 완료됩니다. ctx.runNode()를 호출하는 노드는 대신 rerunOnResume: true가 필요합니다. 자세한 내용은 동적 워크플로에서의 인간 입력을 참조하세요.

In ADK Go v2.0.0, a HITL graph node is built with workflow.NewEmittingFunctionNode and workflow.ResumeOrRequestInput. This is the direct equivalent of Python's RequestInput node:

• On the first pass, workflow.ResumeOrRequestInput emits a session.RequestInput event (surfaced as Event.RequestedInput) and returns ErrNodeInterrupted, pausing the workflow.
• After the human replies, the node is re-invoked from the top (RerunOnResume: &true) and ResumeOrRequestInput returns the reply payload, which flows as typed input to the next node via event.Output.

// newGraphHITLWorkflow demonstrates a graph HITL node using
// workflow.NewEmittingFunctionNode and workflow.ResumeOrRequestInput.
//
// This is the Go equivalent of the Python RequestInput node:
//
//  def step1():  # Human input step
//      yield RequestInput(message="Enter a number:")
//
//  def step2(node_input):
//      return node_input * 2
//
//  root_agent = Workflow(
//      name="root_agent",
//      edges=[('START', step1, step2)],
//  )
//
// On the first pass, step1Node emits a RequestInput event and pauses the
// workflow (ErrNodeInterrupted). After the human replies, the node is re-run
// and ResumeOrRequestInput returns the reply, which flows as typed input to
// step2Node via event.Output.
func newGraphHITLWorkflow() (agent.Agent, error) {
    rerun := true

    // step1Node: pauses for human input on the first pass, returns the
    // human's reply on resume. workflow.ResumeOrRequestInput handles both
    // phases — no manual re-entry bookkeeping needed.
    step1Node := workflow.NewEmittingFunctionNode[any, string]("step1",
        func(ctx agent.Context, _ any, emit func(*session.Event) error) (string, error) {
            reply, err := workflow.ResumeOrRequestInput(ctx, emit, session.RequestInput{
                InterruptID: "enter_number",
                Message:     "Enter a number:",
            })
            if err != nil {
                // ErrNodeInterrupted on first pass — workflow pauses here.
                return "", err
            }
            // On resume, reply is the human's text response.
            number, _ := reply.(string)
            return number, nil
        },
        workflow.NodeConfig{RerunOnResume: &rerun},
    )

    // step2Node: receives the human's input as its typed string input via
    // event.Output and doubles the number.
    step2Node := workflow.NewFunctionNode("step2",
        func(_ agent.Context, input string) (string, error) {
            return fmt.Sprintf("You entered: %s (doubled: %s%s)", input, input, input), nil
        },
        workflow.NodeConfig{},
    )

    return workflowagent.New(workflowagent.Config{
        Name:        "root_agent",
        Description: "Pauses for a number from the user, then doubles it.",
        Edges:       workflow.Chain(workflow.Start, step1Node, step2Node),
    })
}

구성 옵션

PythonTypeScriptGo

Human input nodes can use the RequestInput class with the following configuration options:

• message: Text provided to the user to explain the human input request.
• payload: Structured data to be used as part of the human input request.
• response_schema: A data structure the human response must conform to.

RequestInput 클래스는 다음 구성 옵션을 사용합니다:

• message: 사용자에게 질문 내용을 설명하는 텍스트입니다.
• payload: 클라이언트가 추가 컨텍스트를 렌더링할 수 있도록 프롬프트와 함께 전송되는 구조화된 데이터입니다.
• responseSchema: 응답이 취할 것으로 예상되는 형태입니다. 스키마는 인터럽트 시 functionCall.args.response_schema로 전달되며, 클라이언트는 이를 읽어 응답용 양식을 렌더링합니다.

노드의 rerunOnResume 옵션은 응답이 도착했을 때 일어나는 동작을 제어합니다:

• false (리프 노드 기본값): 응답이 중단된 노드를 우회하여 해당 노드의 후속 노드에 입력으로 라우팅됩니다.
• true: 노드 본문이 처음부터 다시 실행됩니다. 이 설정은 ctx.runNode()를 호출하는 모든 노드에 필요하며, 재개 시 캐시된 하위 결과를 전달할 수 있습니다.

session.RequestInput carries the following fields, which map directly to Python's RequestInput parameters:

• InterruptID (string): A unique identifier for this pause point. Use a stable prefix plus a UUID to avoid collision across workflow runs. Equivalent to the implicit interrupt ID in Python.
• Message (string): Human-readable prompt displayed to the user. Equivalent to Python's message parameter.
• Payload (any): Optional structured data sent alongside the prompt so the client can render additional context. Equivalent to Python's payload parameter.

workflow.NodeConfig.RerunOnResume controls what happens on resume:

• &true: the node body is re-run from the top; ResumeOrRequestInput returns the human's reply on the second pass. Required for nodes that use ResumeOrRequestInput.
• &false or nil (leaf default): the reply is routed to the node's successor as input, bypassing the interrupted node.

Note: Structured response from the client

ADK Go does not automatically parse or validate the structure of the human's reply payload. If your workflow needs structured feedback, include a UI or a downstream agent node to validate the response before acting on it.

참고: 응답 스키마 입력 제한사항

응답 스키마는 사람의 응답을 지정된 구조에 맞게 자동으로 재구성하지 않습니다. 응답은 이미 해당 형식이어야 합니다. 더 나은 사용자 경험을 위해 클라이언트 인터페이스에서 구조화된 데이터를 수집하거나, 일시 중지 뒤에 에이전트 노드를 배치하여 응답을 필요한 형식으로 변환하세요.

사람의 입력 (Human Input) examples

The following code examples demonstrate more detailed human input requests.

Request input with a message and payload

PythonTypeScriptGo

The following code sample shows how to construct a RequestInput object in a workflow node, including a payload and response schema. In this example, the ActivitiesList is expected to be completed by an agent node that composes a list of activities, and the get_user_feedback() node requests feedback from the user.

class ActivitiesList(BaseModel):
   """Itinerary should be a list of dictionaries for each activity. Each
   activity has a name and a description"""
   itinerary: List[Dict[str, str]]

class UserFeedback(BaseModel):
   """Expected response structure from the user."""
   user_response: str

async def get_user_feedback(node_input: ActivitiesList):
   """
   Retrieves the user's thoughts on the agents initial itinerary in order to
   either expand on, change the list, or exit the loop
   """
   message = (
       f"""
       Here is your recommended base itinerary:\n{node_input}\n\n
       Which of these items appeal to you (if any)?
       """
   )

   yield RequestInput(
       message=message,
       payload=node_input,
        response_schema=UserFeedback,
   )

다음 3개 노드 그래프는 구조화된 여행 일정을 작성하고, 클라이언트가 이를 렌더링할 수 있도록 프롬프트와 함께 payload로 전송한 다음 사용자의 피드백에 따라 작동합니다:

import { node, NodeContext, RequestInput, Workflow } from '@google/adk';
import { z } from 'zod';

/**
 * Itinerary is a list of activities. Each activity has a name and a
 * description.
 */
const activitiesListSchema = z.object({
  itinerary: z.array(z.object({ name: z.string(), description: z.string() })),
});
type ActivitiesList = z.infer<typeof activitiesListSchema>;

/** Expected response structure from the user. */
const userFeedbackSchema = z.object({
  userResponse: z.string(),
});

const buildItinerary = node(
  (_ctx: NodeContext, city: string): ActivitiesList => {
    const place = city.trim() || 'your city';
    return {
      itinerary: [
        { name: 'Morning walk', description: `A stroll through old ${place}.` },
        { name: 'Local lunch', description: `Regional food in ${place}.` },
        { name: 'Museum visit', description: `The main museum of ${place}.` },
      ],
    };
  },
  { name: 'build_itinerary', outputSchema: activitiesListSchema },
);

/**
 * Retrieves the user's thoughts on the agent's initial itinerary in order to
 * either expand on it, change the list, or exit the loop.
 */
const getUserFeedback = node(
  async function* (_ctx: NodeContext, nodeInput: ActivitiesList) {
    const rendered = nodeInput.itinerary
      .map((a, i) => `  ${i + 1}. ${a.name} — ${a.description}`)
      .join('\n');

    yield new RequestInput({
      message:
        `Here is your recommended base itinerary:\n${rendered}\n\n` +
        'Which of these items appeal to you (if any)?',
      payload: nodeInput,
      responseSchema: userFeedbackSchema,
    });
  },
  { name: 'get_user_feedback' },
);

const applyFeedback = node(
  (_ctx: NodeContext, nodeInput: unknown) => {
    const feedback =
      typeof nodeInput === 'string'
        ? nodeInput
        : String(
            (nodeInput as { userResponse?: unknown } | null)?.userResponse ??
              JSON.stringify(nodeInput),
          );
    return `Noted. Building the final itinerary around: ${feedback}`;
  },
  { name: 'apply_feedback' },
);

export const rootAgent = new Workflow({
  name: 'concierge_workflow',
  edges: [['START', buildItinerary, getUserFeedback, applyFeedback]],
});

The following code sample shows a three-node graph: a builder node generates a structured itinerary, a HITL node sends it as Payload alongside the prompt, and a final node acts on the user's feedback. The Payload field lets the client render the full itinerary for the user before they respond:

// ItineraryItem represents a single activity in a travel plan.
type ItineraryItem struct {
    Name        string `json:"name"`
    Description string `json:"description"`
}

// newItineraryReviewWorkflow demonstrates a graph HITL node that sends a
// structured payload alongside the input prompt so the client can render
// additional context for the user. This mirrors Python's:
//
//  async def get_user_feedback(node_input: ActivitiesList):
//      yield RequestInput(
//          message="Which items appeal to you?",
//          payload=node_input,
//          response_schema=UserFeedback,
//      )
func newItineraryReviewWorkflow() (agent.Agent, error) {
    rerun := true

    // buildItineraryNode: generates an itinerary and passes it to the HITL
    // node as its typed output via event.Output.
    buildItineraryNode := workflow.NewFunctionNode("build_itinerary",
        func(_ agent.Context, _ any) ([]ItineraryItem, error) {
            return []ItineraryItem{
                {Name: "Eiffel Tower", Description: "Iconic iron lattice tower."},
                {Name: "Louvre Museum", Description: "World's largest art museum."},
                {Name: "Seine River Cruise", Description: "Scenic boat tour of Paris."},
            }, nil
        },
        workflow.NodeConfig{},
    )

    // reviewNode: sends the itinerary as payload alongside the prompt so the
    // client can display it. On resume, the human's selection is returned.
    reviewNode := workflow.NewEmittingFunctionNode[[]ItineraryItem, string]("get_user_feedback",
        func(ctx agent.Context, itinerary []ItineraryItem, emit func(*session.Event) error) (string, error) {
            reply, err := workflow.ResumeOrRequestInput(ctx, emit, session.RequestInput{
                InterruptID: "itinerary_review",
                Message:     fmt.Sprintf("Here is your recommended itinerary (%d activities). Which items appeal to you?", len(itinerary)),
                Payload:     itinerary, // structured payload rendered by the client
            })
            if err != nil {
                // ErrNodeInterrupted on first pass — workflow pauses here.
                return "", err
            }
            feedback, _ := reply.(string)
            return feedback, nil
        },
        workflow.NodeConfig{RerunOnResume: &rerun},
    )

    // finalNode: receives the user's feedback and produces a confirmation.
    finalNode := workflow.NewFunctionNode("finalize",
        func(_ agent.Context, feedback string) (string, error) {
            return fmt.Sprintf("Itinerary finalised with your feedback: %q", feedback), nil
        },
        workflow.NodeConfig{},
    )

    return workflowagent.New(workflowagent.Config{
        Name:        "concierge_workflow",
        Description: "Builds an itinerary, asks the user for feedback, then finalises.",
        Edges:       workflow.Chain(workflow.Start, buildItineraryNode, reviewNode, finalNode),
    })
}

도구 확인: LLM 에이전트의 승인 프롬프트

Tool-confirmation is a separate, LLM-agent–level mechanism for yes/no approval prompts. Unlike graph HITL nodes, tool-confirmation works inside an llmagent tool function rather than as a standalone graph node. It is useful when you want an LLM agent to pause and ask for approval before executing a specific tool call.

PythonTypeScriptGo

The following code sample shows how to construct a RequestInput object in a workflow node, including a response schema:

async def initial_prompt(ctx: Context):
   """Ask the user for itinerary information"""
   input_message = """
       This is an interactive concierge workflow tasked with making you a great
       itinerary for you in your city of choice. If you give some details about
       yourself or what you are generally looking for I can better personalize
       your itinerary.
       For example, input your:
           City (Required),
           Age,
           Hobby,
           Example of attraction you liked
   """
    yield RequestInput(message=input_message, response_schema=str)

도구가 실행되기 전에 에이전트가 승인을 위해 일시 중지하도록 하려면 FunctionTool에 requireConfirmation: true를 설정하세요. 그래프의 인간 참여형(HITL) 노드는 다른 목적으로 사용됩니다. 도구 호출을 확인하는 대신 사용자에게 입력을 요청하여 워크플로를 시작할 수 있습니다. responseSchema: z.string() 옵션은 일반 텍스트 응답을 요청합니다:

import { node, NodeContext, RequestInput, Workflow } from '@google/adk';
import { z } from 'zod';

/** Asks the user for itinerary information. */
const initialPrompt = node(
  async function* () {
    const inputMessage = `
        This is an interactive concierge workflow tasked with making you a great
        itinerary for you in your city of choice. If you give some details about
        yourself or what you are generally looking for I can better personalize
        your itinerary.
        For example, input your:
            City (Required),
            Age,
            Hobby,
            Example of attraction you liked
    `;
    yield new RequestInput({
      message: inputMessage,
      responseSchema: z.string(),
    });
  },
  { name: 'initial_prompt' },
);

const buildItinerary = node(
  (_ctx: NodeContext, nodeInput: string) => {
    const [city = 'your city'] = nodeInput.split(',');
    return (
      `Personalized itinerary for ${city.trim()}:\n` +
      '  1. Morning walk through the old town\n' +
      '  2. Lunch at a neighbourhood favourite\n' +
      '  3. An afternoon activity matched to your hobby\n\n' +
      `(based on: ${nodeInput.trim()})`
    );
  },
  { name: 'build_itinerary' },
);

export const rootAgent = new Workflow({
  name: 'concierge_workflow',
  edges: [['START', initialPrompt, buildItinerary]],
});

Set RequireConfirmation: true in functiontool.Config for a static yes/no approval before a tool executes, or call ctx.RequestConfirmation from inside the tool for a custom hint message:

// DoubleNumberArgs holds the input for the doubleNumber tool.
type DoubleNumberArgs struct {
    Number int `json:"number" jsonschema:"The number to double."`
}

// DoubleNumberResults holds the output of the doubleNumber tool.
type DoubleNumberResults struct {
    Result int `json:"result"`
}

// doubleNumber is a tool that doubles the given number.
// Because RequireConfirmation is true, the framework automatically pauses
// execution and emits an "adk_request_confirmation" event to the client before
// running the tool. The client must reply with a FunctionResponse confirming
// or denying the action.
func doubleNumber(_ agent.Context, args DoubleNumberArgs) (DoubleNumberResults, error) {
    return DoubleNumberResults{Result: args.Number * 2}, nil
}

// newSimpleHITLAgent creates an LLM agent with a tool that always requires
// user confirmation before it executes (tool-confirmation pattern).
func newSimpleHITLAgent(ctx context.Context) (agent.Agent, error) {
    model, err := gemini.NewModel(ctx, modelName, &genai.ClientConfig{})
    if err != nil {
        return nil, fmt.Errorf("failed to create model: %w", err)
    }

    doubleNumberTool, err := functiontool.New(
        functiontool.Config{
            Name:                "double_number",
            Description:         "Doubles the given number. Requires user approval before running.",
            RequireConfirmation: true,
        },
        doubleNumber,
    )
    if err != nil {
        return nil, fmt.Errorf("failed to create tool: %w", err)
    }

    return llmagent.New(llmagent.Config{
        Name:        "double_number_agent",
        Model:       model,
        Instruction: "You are a helpful assistant. When asked to double a number, use the double_number tool.",
        Tools:       []tool.Tool{doubleNumberTool},
    })
}

For a custom hint with manual re-entry handling:

// BookFlightArgs holds the input for the bookFlight tool.
type BookFlightArgs struct {
    Origin      string `json:"origin"      jsonschema:"Departure airport code."`
    Destination string `json:"destination" jsonschema:"Arrival airport code."`
    Date        string `json:"date"        jsonschema:"Travel date in YYYY-MM-DD format."`
}

// BookFlightResults holds the outcome of the bookFlight tool.
type BookFlightResults struct {
    Status        string `json:"status"`
    ConfirmNumber string `json:"confirm_number,omitempty"`
}

// bookFlight is a tool that pauses for human approval before completing a
// booking (tool-confirmation pattern with a custom hint message).
func bookFlight(ctx agent.Context, args BookFlightArgs) (BookFlightResults, error) {
    if confirmation := ctx.ToolConfirmation(); confirmation != nil {
        if !confirmation.Confirmed {
            return BookFlightResults{Status: "Booking cancelled by user."}, nil
        }
        return BookFlightResults{
            Status:        "Booking confirmed.",
            ConfirmNumber: "FLT-20251031",
        }, nil
    }

    hint := fmt.Sprintf(
        "The agent wants to book a flight from %s to %s on %s. Do you approve?",
        args.Origin, args.Destination, args.Date,
    )
    if err := ctx.RequestConfirmation(hint, nil); err != nil {
        return BookFlightResults{}, fmt.Errorf("failed to request confirmation: %w", err)
    }
    return BookFlightResults{Status: "Awaiting user approval."}, nil
}

// newHITLWithHintAgent creates an LLM agent whose bookFlight tool manually
// requests confirmation with a descriptive hint (tool-confirmation pattern).
func newHITLWithHintAgent(ctx context.Context) (agent.Agent, error) {
    model, err := gemini.NewModel(ctx, modelName, &genai.ClientConfig{})
    if err != nil {
        return nil, fmt.Errorf("failed to create model: %w", err)
    }

    bookFlightTool, err := functiontool.New(
        functiontool.Config{
            Name:        "book_flight",
            Description: "Books a flight between two airports on a given date.",
        },
        bookFlight,
    )
    if err != nil {
        return nil, fmt.Errorf("failed to create tool: %w", err)
    }

    return llmagent.New(llmagent.Config{
        Name:        "flight_booking_agent",
        Model:       model,
        Instruction: "You are a flight booking assistant. Help the user book flights.",
        Tools:       []tool.Tool{bookFlightTool},
    })
}