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루프 에이전트

ADK에서 지원Python v0.1.0Go v0.1.0Java v0.2.0

LoopAgent는 하위 에이전트를 루프(즉, 반복적으로)로 실행하는 워크플로우 에이전트입니다. 이 에이전트는 명시된 반복 횟수만큼 또는 종료 조건이 충족될 때까지 에이전트 시퀀스를 반복적으로 실행합니다.

코드 수정과 같이 워크플로우에 반복이나 반복적인 개선이 포함될 때 LoopAgent를 사용하세요.

예시

  • 음식 이미지를 생성하는 에이전트를 구축하려는데, 특정 개수의 아이템(예: 바나나 5개)을 생성하려 할 때 다른 개수의 아이템(예: 바나나 7개가 있는 이미지)을 생성하는 경우가 있다고 가정해 봅시다. 당신에게는 이미지 생성하기와 음식 아이템 개수 세기라는 두 가지 도구가 있습니다. 명시된 개수의 아이템을 정확하게 생성하거나 특정 반복 횟수에 도달할 때까지 이미지 생성을 계속하고 싶기 때문에, LoopAgent를 사용하여 에이전트를 구축해야 합니다.

다른 워크플로우 에이전트와 마찬가지로, LoopAgent는 LLM으로 구동되지 않으므로 실행 방식이 결정적(deterministic)입니다. 하지만 워크플로우 에이전트는 내부 로직이 아닌 실행(즉, 루프 내 실행)에만 관여합니다. 워크플로우 에이전트의 도구나 하위 에이전트는 LLM을 활용할 수도 있고, 그렇지 않을 수도 있습니다.

작동 방식

LoopAgent의 Run Async 메서드가 호출되면 다음 작업을 수행합니다.

  1. 하위 에이전트 실행: 하위 에이전트 목록을 순서대로 순회합니다. 각 하위 에이전트에 대해 해당 에이전트의 Run Async 메서드를 호출합니다.

  2. 종료 확인:

    매우 중요한 점은, LoopAgent 자체는 언제 루프를 멈출지 본질적으로 결정하지 않는다는 것입니다. 무한 루프를 방지하기 위해 반드시 종료 메커니즘을 구현해야 합니다. 일반적인 전략은 다음과 같습니다.

    • 최대 반복 횟수(Max Iterations): LoopAgent에 최대 반복 횟수를 설정합니다. 루프는 해당 횟수만큼 반복된 후 종료됩니다.
    • 하위 에이전트의 종료 신호(Escalation from sub-agent): 하나 이상의 하위 에이전트가 특정 조건("문서 품질이 충분히 좋은가?", "합의에 도달했는가?")을 평가하도록 설계합니다. 조건이 충족되면, 하위 에이전트는 종료 신호(예: 커스텀 이벤트 발생, 공유 컨텍스트에 플래그 설정, 특정 값 반환 등)를 보낼 수 있습니다.

Loop Agent

전체 예시: 반복적인 문서 개선

문서를 반복적으로 개선하려는 시나리오를 상상해 보세요.

  • 작성 에이전트(Writer Agent): 특정 주제에 대한 초안을 생성하거나 개선하는 LlmAgent입니다.

  • 비평 에이전트(Critic Agent): 초안을 비평하고 개선할 부분을 식별하는 LlmAgent입니다.

    LoopAgent(sub_agents=[WriterAgent, CriticAgent], max_iterations=5)

이 설정에서 LoopAgent는 반복적인 프로세스를 관리합니다. Critic Agent는 문서가 만족스러운 품질 수준에 도달했을 때 "STOP" 신호를 반환하도록 설계하여 추가적인 반복을 막을 수 있습니다. 또는, max_iterations 매개변수를 사용하여 프로세스를 고정된 횟수로 제한하거나, 중지 결정을 내리기 위한 외부 로직을 구현할 수도 있습니다. 루프는 최대 5번 실행되어 반복적인 개선이 무한정 계속되지 않도록 보장합니다.

전체 코드 
# Part of agent.py --> Follow https://google.github.io/adk-docs/get-started/quickstart/ to learn the setup

import asyncio
import os
from google.adk.agents import LoopAgent, LlmAgent, BaseAgent, SequentialAgent
from google.genai import types
from google.adk.runners import InMemoryRunner
from google.adk.agents.invocation_context import InvocationContext
from google.adk.tools.tool_context import ToolContext
from typing import AsyncGenerator, Optional
from google.adk.events import Event, EventActions

# --- Constants ---
APP_NAME = "doc_writing_app_v3" # New App Name
USER_ID = "dev_user_01"
SESSION_ID_BASE = "loop_exit_tool_session" # New Base Session ID
GEMINI_MODEL = "gemini-2.0-flash"
STATE_INITIAL_TOPIC = "initial_topic"

# --- State Keys ---
STATE_CURRENT_DOC = "current_document"
STATE_CRITICISM = "criticism"
# Define the exact phrase the Critic should use to signal completion
COMPLETION_PHRASE = "No major issues found."

# --- Tool Definition ---
def exit_loop(tool_context: ToolContext):
  """Call this function ONLY when the critique indicates no further changes are needed, signaling the iterative process should end."""
  print(f"  [Tool Call] exit_loop triggered by {tool_context.agent_name}")
  tool_context.actions.escalate = True
  # Return empty dict as tools should typically return JSON-serializable output
  return {}

# --- Agent Definitions ---

# STEP 1: Initial Writer Agent (Runs ONCE at the beginning)
initial_writer_agent = LlmAgent(
    name="InitialWriterAgent",
    model=GEMINI_MODEL,
    include_contents='none',
    # MODIFIED Instruction: Ask for a slightly more developed start
    instruction=f"""You are a Creative Writing Assistant tasked with starting a story.
    Write the *first draft* of a short story (aim for 2-4 sentences).
    Base the content *only* on the topic provided below. Try to introduce a specific element (like a character, a setting detail, or a starting action) to make it engaging.
    Topic: {{initial_topic}}

    Output *only* the story/document text. Do not add introductions or explanations.
""",
    description="Writes the initial document draft based on the topic, aiming for some initial substance.",
    output_key=STATE_CURRENT_DOC
)

# STEP 2a: Critic Agent (Inside the Refinement Loop)
critic_agent_in_loop = LlmAgent(
    name="CriticAgent",
    model=GEMINI_MODEL,
    include_contents='none',
    # MODIFIED Instruction: More nuanced completion criteria, look for clear improvement paths.
    instruction=f"""You are a Constructive Critic AI reviewing a short document draft (typically 2-6 sentences). Your goal is balanced feedback.

    **Document to Review:**
    ```
    {{current_document}}
    ```

    **Task:**
    Review the document for clarity, engagement, and basic coherence according to the initial topic (if known).

    IF you identify 1-2 *clear and actionable* ways the document could be improved to better capture the topic or enhance reader engagement (e.g., "Needs a stronger opening sentence", "Clarify the character's goal"):
    Provide these specific suggestions concisely. Output *only* the critique text.

    ELSE IF the document is coherent, addresses the topic adequately for its length, and has no glaring errors or obvious omissions:
    Respond *exactly* with the phrase "{COMPLETION_PHRASE}" and nothing else. It doesn't need to be perfect, just functionally complete for this stage. Avoid suggesting purely subjective stylistic preferences if the core is sound.

    Do not add explanations. Output only the critique OR the exact completion phrase.
""",
    description="Reviews the current draft, providing critique if clear improvements are needed, otherwise signals completion.",
    output_key=STATE_CRITICISM
)


# STEP 2b: Refiner/Exiter Agent (Inside the Refinement Loop)
refiner_agent_in_loop = LlmAgent(
    name="RefinerAgent",
    model=GEMINI_MODEL,
    # Relies solely on state via placeholders
    include_contents='none',
    instruction=f"""You are a Creative Writing Assistant refining a document based on feedback OR exiting the process.
    **Current Document:**
    ```
    {{current_document}}
    ```
    **Critique/Suggestions:**
    {{criticism}}

    **Task:**
    Analyze the 'Critique/Suggestions'.
    IF the critique is *exactly* "{COMPLETION_PHRASE}":
    You MUST call the 'exit_loop' function. Do not output any text.
    ELSE (the critique contains actionable feedback):
    Carefully apply the suggestions to improve the 'Current Document'. Output *only* the refined document text.

    Do not add explanations. Either output the refined document OR call the exit_loop function.
""",
    description="Refines the document based on critique, or calls exit_loop if critique indicates completion.",
    tools=[exit_loop], # Provide the exit_loop tool
    output_key=STATE_CURRENT_DOC # Overwrites state['current_document'] with the refined version
)


# STEP 2: Refinement Loop Agent
refinement_loop = LoopAgent(
    name="RefinementLoop",
    # Agent order is crucial: Critique first, then Refine/Exit
    sub_agents=[
        critic_agent_in_loop,
        refiner_agent_in_loop,
    ],
    max_iterations=5 # Limit loops
)

# STEP 3: Overall Sequential Pipeline
# For ADK tools compatibility, the root agent must be named `root_agent`
root_agent = SequentialAgent(
    name="IterativeWritingPipeline",
    sub_agents=[
        initial_writer_agent, # Run first to create initial doc
        refinement_loop       # Then run the critique/refine loop
    ],
    description="Writes an initial document and then iteratively refines it with critique using an exit tool."
)

// ExitLoopArgs defines the (empty) arguments for the ExitLoop tool.
type ExitLoopArgs struct{}

// ExitLoopResults defines the output of the ExitLoop tool.
type ExitLoopResults struct{}

// ExitLoop is a tool that signals the loop to terminate by setting Escalate to true.
func ExitLoop(ctx tool.Context, input ExitLoopArgs) (ExitLoopResults, error) {
    fmt.Printf("[Tool Call] exitLoop triggered by %s \n", ctx.AgentName())
    ctx.Actions().Escalate = true
    return ExitLoopResults{}, nil
}

func main() {
    ctx := context.Background()

    if err := runAgent(ctx, "Write a document about a cat"); err != nil {
        log.Fatalf("Agent execution failed: %v", err)
    }
}

func runAgent(ctx context.Context, prompt string) error {
    model, err := gemini.NewModel(ctx, modelName, &genai.ClientConfig{})
    if err != nil {
        return fmt.Errorf("failed to create model: %v", err)
    }

    // STEP 1: Initial Writer Agent (Runs ONCE at the beginning)
    initialWriterAgent, err := llmagent.New(llmagent.Config{
        Name:        "InitialWriterAgent",
        Model:       model,
        Description: "Writes the initial document draft based on the topic.",
        Instruction: `You are a Creative Writing Assistant tasked with starting a story.
Write the *first draft* of a short story (aim for 2-4 sentences).
Base the content *only* on the topic provided in the user's prompt.
Output *only* the story/document text. Do not add introductions or explanations.`,
        OutputKey: stateDoc,
    })
    if err != nil {
        return fmt.Errorf("failed to create initial writer agent: %v", err)
    }

    // STEP 2a: Critic Agent (Inside the Refinement Loop)
    criticAgentInLoop, err := llmagent.New(llmagent.Config{
        Name:        "CriticAgent",
        Model:       model,
        Description: "Reviews the current draft, providing critique or signaling completion.",
        Instruction: fmt.Sprintf(`You are a Constructive Critic AI reviewing a short document draft.
**Document to Review:**
"""
{%s}
"""
**Task:**
Review the document.
IF you identify 1-2 *clear and actionable* ways it could be improved:
Provide these specific suggestions concisely. Output *only* the critique text.
ELSE IF the document is coherent and addresses the topic adequately:
Respond *exactly* with the phrase "%s" and nothing else.`, stateDoc, donePhrase),
        OutputKey: stateCrit,
    })
    if err != nil {
        return fmt.Errorf("failed to create critic agent: %v", err)
    }

    exitLoopTool, err := functiontool.New(
        functiontool.Config{
            Name:        "exitLoop",
            Description: "Call this function ONLY when the critique indicates no further changes are needed.",
        },
        ExitLoop,
    )
    if err != nil {
        return fmt.Errorf("failed to create exit loop tool: %v", err)
    }

    // STEP 2b: Refiner/Exiter Agent (Inside the Refinement Loop)
    refinerAgentInLoop, err := llmagent.New(llmagent.Config{
        Name:  "RefinerAgent",
        Model: model,
        Instruction: fmt.Sprintf(`You are a Creative Writing Assistant refining a document based on feedback OR exiting the process.
**Current Document:**

"""
{%s}
"""

**Critique/Suggestions:**
{%s}
**Task:**
Analyze the 'Critique/Suggestions'.
IF the critique is *exactly* "%s":
You MUST call the 'exitLoop' function. Do not output any text.
ELSE (the critique contains actionable feedback):
Carefully apply the suggestions to improve the 'Current Document'. Output *only* the refined document text.`, stateDoc, stateCrit, donePhrase),
        Description: "Refines the document based on critique, or calls exitLoop if critique indicates completion.",
        Tools:       []tool.Tool{exitLoopTool},
        OutputKey:   stateDoc,
    })
    if err != nil {
        return fmt.Errorf("failed to create refiner agent: %v", err)
    }

    // STEP 2: Refinement Loop Agent
    refinementLoop, err := loopagent.New(loopagent.Config{
        AgentConfig: agent.Config{
            Name:      "RefinementLoop",
            SubAgents: []agent.Agent{criticAgentInLoop, refinerAgentInLoop},
        },
        MaxIterations: 5,
    })
    if err != nil {
        return fmt.Errorf("failed to create loop agent: %v", err)
    }

    // STEP 3: Overall Sequential Pipeline
    iterativeWriterAgent, err := sequentialagent.New(sequentialagent.Config{
        AgentConfig: agent.Config{
            Name:      appName,
            SubAgents: []agent.Agent{initialWriterAgent, refinementLoop},
        },
    })
    if err != nil {
        return fmt.Errorf("failed to create sequential agent pipeline: %v", err)
    }

import static com.google.adk.agents.LlmAgent.IncludeContents.NONE;

import com.google.adk.agents.LlmAgent;
import com.google.adk.agents.LoopAgent;
import com.google.adk.agents.SequentialAgent;
import com.google.adk.events.Event;
import com.google.adk.runner.InMemoryRunner;
import com.google.adk.sessions.Session;
import com.google.adk.tools.Annotations.Schema;
import com.google.adk.tools.FunctionTool;
import com.google.adk.tools.ToolContext;
import com.google.genai.types.Content;
import com.google.genai.types.Part;
import io.reactivex.rxjava3.core.Flowable;
import java.util.Map;

public class LoopAgentExample {

  // --- Constants ---
  private static final String APP_NAME = "IterativeWritingPipeline";
  private static final String USER_ID = "test_user_456";
  private static final String MODEL_NAME = "gemini-2.0-flash";

  // --- State Keys ---
  private static final String STATE_CURRENT_DOC = "current_document";
  private static final String STATE_CRITICISM = "criticism";

  public static void main(String[] args) {
    LoopAgentExample loopAgentExample = new LoopAgentExample();
    loopAgentExample.runAgent("Write a document about a cat");
  }

  // --- Tool Definition ---
  @Schema(
      description =
          "Call this function ONLY when the critique indicates no further changes are needed,"
              + " signaling the iterative process should end.")
  public static Map<String, Object> exitLoop(@Schema(name = "toolContext") ToolContext toolContext) {
    System.out.printf("[Tool Call] exitLoop triggered by %s \n", toolContext.agentName());
    toolContext.actions().setEscalate(true);
    //  Return empty dict as tools should typically return JSON-serializable output
    return Map.of();
  }

  // --- Agent Definitions ---
  public void runAgent(String prompt) {
    // STEP 1: Initial Writer Agent (Runs ONCE at the beginning)
    LlmAgent initialWriterAgent =
        LlmAgent.builder()
            .model(MODEL_NAME)
            .name("InitialWriterAgent")
            .description(
                "Writes the initial document draft based on the topic, aiming for some initial"
                    + " substance.")
            .instruction(
                """
                    You are a Creative Writing Assistant tasked with starting a story.
                    Write the *first draft* of a short story (aim for 2-4 sentences).
                    Base the content *only* on the topic provided below. Try to introduce a specific element (like a character, a setting detail, or a starting action) to make it engaging.

                    Output *only* the story/document text. Do not add introductions or explanations.
                """)
            .outputKey(STATE_CURRENT_DOC)
            .includeContents(NONE)
            .build();

    // STEP 2a: Critic Agent (Inside the Refinement Loop)
    LlmAgent criticAgentInLoop =
        LlmAgent.builder()
            .model(MODEL_NAME)
            .name("CriticAgent")
            .description(
                "Reviews the current draft, providing critique if clear improvements are needed,"
                    + " otherwise signals completion.")
            .instruction(
                """
                    You are a Constructive Critic AI reviewing a short document draft (typically 2-6 sentences). Your goal is balanced feedback.

                    **Document to Review:**
                    ```
                    {{current_document}}
                    ```

                    **Task:**
                    Review the document for clarity, engagement, and basic coherence according to the initial topic (if known).

                    IF you identify 1-2 *clear and actionable* ways the document could be improved to better capture the topic or enhance reader engagement (e.g., "Needs a stronger opening sentence", "Clarify the character's goal"):
                    Provide these specific suggestions concisely. Output *only* the critique text.

                    ELSE IF the document is coherent, addresses the topic adequately for its length, and has no glaring errors or obvious omissions:
                    Respond *exactly* with the phrase "No major issues found." and nothing else. It doesn't need to be perfect, just functionally complete for this stage. Avoid suggesting purely subjective stylistic preferences if the core is sound.

                    Do not add explanations. Output only the critique OR the exact completion phrase.
                    """)
            .outputKey(STATE_CRITICISM)
            .includeContents(NONE)
            .build();

    // STEP 2b: Refiner/Exiter Agent (Inside the Refinement Loop)
    LlmAgent refinerAgentInLoop =
        LlmAgent.builder()
            .model(MODEL_NAME)
            .name("RefinerAgent")
            .description(
                "Refines the document based on critique, or calls exitLoop if critique indicates"
                    + " completion.")
            .instruction(
                """
                    You are a Creative Writing Assistant refining a document based on feedback OR exiting the process.
                    **Current Document:**
                    ```
                    {{current_document}}
                    ```
                    **Critique/Suggestions:**
                    {{criticism}}

                    **Task:**
                    Analyze the 'Critique/Suggestions'.
                    IF the critique is *exactly* "No major issues found.":
                    You MUST call the 'exitLoop' function. Do not output any text.
                    ELSE (the critique contains actionable feedback):
                    Carefully apply the suggestions to improve the 'Current Document'. Output *only* the refined document text.

                    Do not add explanations. Either output the refined document OR call the exitLoop function.
                """)
            .outputKey(STATE_CURRENT_DOC)
            .includeContents(NONE)
            .tools(FunctionTool.create(LoopAgentExample.class, "exitLoop"))
            .build();

    // STEP 2: Refinement Loop Agent
    LoopAgent refinementLoop =
        LoopAgent.builder()
            .name("RefinementLoop")
            .description("Repeatedly refines the document with critique and then exits.")
            .subAgents(criticAgentInLoop, refinerAgentInLoop)
            .maxIterations(5)
            .build();

    // STEP 3: Overall Sequential Pipeline
    SequentialAgent iterativeWriterAgent =
        SequentialAgent.builder()
            .name(APP_NAME)
            .description(
                "Writes an initial document and then iteratively refines it with critique using an"
                    + " exit tool.")
            .subAgents(initialWriterAgent, refinementLoop)
            .build();

    // Create an InMemoryRunner
    InMemoryRunner runner = new InMemoryRunner(iterativeWriterAgent, APP_NAME);
    // InMemoryRunner automatically creates a session service. Create a session using the service
    Session session = runner.sessionService().createSession(APP_NAME, USER_ID).blockingGet();
    Content userMessage = Content.fromParts(Part.fromText(prompt));

    // Run the agent
    Flowable<Event> eventStream = runner.runAsync(USER_ID, session.id(), userMessage);

    // Stream event response
    eventStream.blockingForEach(
        event -> {
          if (event.finalResponse()) {
            System.out.println(event.stringifyContent());
          }
        });
  }
}