シーケンシャルエージェント¶

`SequentialAgent`¶

SequentialAgentは、リストで指定された順序でサブエージェントを実行するワークフローエージェントです。

実行を固定的で厳密な順序で行いたい場合にSequentialAgentを使用します。

例¶

あなたは、ウェブページを要約できるエージェントを構築したいと考えています。これにはGet Page ContentsとSummarize Pageという2つのツールを使用します。エージェントは常にSummarize Pageを呼び出す前にGet Page Contentsを呼び出す必要があります（何もないところから要約はできないため！）。したがって、SequentialAgentを使用してエージェントを構築すべきです。

他のワークフローエージェントと同様に、SequentialAgentはLLMによって駆動されるのではなく、その実行方法は決定的です。とはいえ、ワークフローエージェントは、その内部ロジックではなく、実行（つまりシーケンス）のみに関心があります。ワークフローエージェントのツールやサブエージェントは、LLMを利用する場合もあれば、しない場合もあります。

仕組み¶

SequentialAgentのRun Asyncメソッドが呼び出されると、以下のアクションを実行します：

反復処理： 提供された順序でサブエージェントのリストを反復処理します。
サブエージェントの実行： リスト内の各サブエージェントに対して、そのサブエージェントのRun Asyncメソッドを呼び出します。

Sequential Agent

完全な例：コード開発パイプライン¶

簡略化されたコード開発パイプラインを考えてみましょう：

コードライターエージェント： 仕様に基づいて初期コードを生成するLLMエージェント。
コードレビューアエージェント： 生成されたコードのエラー、スタイル上の問題、ベストプラクティスへの準拠をレビューするLLMエージェント。コードライターエージェントの出力を受け取ります。
コードリファクタリングエージェント： レビューされたコード（およびレビュー担当者のコメント）を受け取り、品質を向上させ、問題に対処するためにリファクタリングするLLMエージェント。

SequentialAgentはこれに最適です：

SequentialAgent(sub_agents=[CodeWriterAgent, CodeReviewerAgent, CodeRefactorerAgent])

これにより、コードが書かれ、次にレビューされ、最後にリファクタリングされるという、厳密で信頼性の高い順序が保証されます。各サブエージェントからの出力は、出力キー (Output Key)を介して状態（state）に保存されることで、次のエージェントに渡されます。

Code

PythonJava

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

# --- 1. Define Sub-Agents for Each Pipeline Stage ---

# Code Writer Agent
# Takes the initial specification (from user query) and writes code.
code_writer_agent = LlmAgent(
    name="CodeWriterAgent",
    model=GEMINI_MODEL,
    # Change 3: Improved instruction
    instruction="""You are a Python Code Generator.
Based *only* on the user's request, write Python code that fulfills the requirement.
Output *only* the complete Python code block, enclosed in triple backticks (```python ... ```). 
Do not add any other text before or after the code block.
""",
    description="Writes initial Python code based on a specification.",
    output_key="generated_code" # Stores output in state['generated_code']
)

# Code Reviewer Agent
# Takes the code generated by the previous agent (read from state) and provides feedback.
code_reviewer_agent = LlmAgent(
    name="CodeReviewerAgent",
    model=GEMINI_MODEL,
    # Change 3: Improved instruction, correctly using state key injection
    instruction="""You are an expert Python Code Reviewer. 
    Your task is to provide constructive feedback on the provided code.

    **Code to Review:**
    ```python
    {generated_code}
    ```

**Review Criteria:**
1.  **Correctness:** Does the code work as intended? Are there logic errors?
2.  **Readability:** Is the code clear and easy to understand? Follows PEP 8 style guidelines?
3.  **Efficiency:** Is the code reasonably efficient? Any obvious performance bottlenecks?
4.  **Edge Cases:** Does the code handle potential edge cases or invalid inputs gracefully?
5.  **Best Practices:** Does the code follow common Python best practices?

**Output:**
Provide your feedback as a concise, bulleted list. Focus on the most important points for improvement.
If the code is excellent and requires no changes, simply state: "No major issues found."
Output *only* the review comments or the "No major issues" statement.
""",
    description="Reviews code and provides feedback.",
    output_key="review_comments", # Stores output in state['review_comments']
)


# Code Refactorer Agent
# Takes the original code and the review comments (read from state) and refactors the code.
code_refactorer_agent = LlmAgent(
    name="CodeRefactorerAgent",
    model=GEMINI_MODEL,
    # Change 3: Improved instruction, correctly using state key injection
    instruction="""You are a Python Code Refactoring AI.
Your goal is to improve the given Python code based on the provided review comments.

  **Original Code:**
  ```python
  {generated_code}
  ```

  **Review Comments:**
  {review_comments}

**Task:**
Carefully apply the suggestions from the review comments to refactor the original code.
If the review comments state "No major issues found," return the original code unchanged.
Ensure the final code is complete, functional, and includes necessary imports and docstrings.

**Output:**
Output *only* the final, refactored Python code block, enclosed in triple backticks (```python ... ```). 
Do not add any other text before or after the code block.
""",
    description="Refactors code based on review comments.",
    output_key="refactored_code", # Stores output in state['refactored_code']
)


# --- 2. Create the SequentialAgent ---
# This agent orchestrates the pipeline by running the sub_agents in order.
code_pipeline_agent = SequentialAgent(
    name="CodePipelineAgent",
    sub_agents=[code_writer_agent, code_reviewer_agent, code_refactorer_agent],
    description="Executes a sequence of code writing, reviewing, and refactoring.",
    # The agents will run in the order provided: Writer -> Reviewer -> Refactorer
)

# For ADK tools compatibility, the root agent must be named `root_agent`
root_agent = code_pipeline_agent

import com.google.adk.agents.LlmAgent;
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.genai.types.Content;
import com.google.genai.types.Part;
import io.reactivex.rxjava3.core.Flowable;

public class SequentialAgentExample {

  private static final String APP_NAME = "CodePipelineAgent";
  private static final String USER_ID = "test_user_456";
  private static final String MODEL_NAME = "gemini-2.0-flash";

  public static void main(String[] args) {
    SequentialAgentExample sequentialAgentExample = new SequentialAgentExample();
    sequentialAgentExample.runAgent(
        "Write a Java function to calculate the factorial of a number.");
  }

  public void runAgent(String prompt) {

    LlmAgent codeWriterAgent =
        LlmAgent.builder()
            .model(MODEL_NAME)
            .name("CodeWriterAgent")
            .description("Writes initial Java code based on a specification.")
            .instruction(
                """
                You are a Java Code Generator.
                Based *only* on the user's request, write Java code that fulfills the requirement.
                Output *only* the complete Java code block, enclosed in triple backticks (```java ... ```).
                Do not add any other text before or after the code block.
                """)
            .outputKey("generated_code")
            .build();

    LlmAgent codeReviewerAgent =
        LlmAgent.builder()
            .model(MODEL_NAME)
            .name("CodeReviewerAgent")
            .description("Reviews code and provides feedback.")
            .instruction(
                """
                    You are an expert Java Code Reviewer.
                    Your task is to provide constructive feedback on the provided code.

                    **Code to Review:**
                    ```java
                    {generated_code}
                    ```

                    **Review Criteria:**
                    1.  **Correctness:** Does the code work as intended? Are there logic errors?
                    2.  **Readability:** Is the code clear and easy to understand? Follows Java style guidelines?
                    3.  **Efficiency:** Is the code reasonably efficient? Any obvious performance bottlenecks?
                    4.  **Edge Cases:** Does the code handle potential edge cases or invalid inputs gracefully?
                    5.  **Best Practices:** Does the code follow common Java best practices?

                    **Output:**
                    Provide your feedback as a concise, bulleted list. Focus on the most important points for improvement.
                    If the code is excellent and requires no changes, simply state: "No major issues found."
                    Output *only* the review comments or the "No major issues" statement.
                """)
            .outputKey("review_comments")
            .build();

    LlmAgent codeRefactorerAgent =
        LlmAgent.builder()
            .model(MODEL_NAME)
            .name("CodeRefactorerAgent")
            .description("Refactors code based on review comments.")
            .instruction(
                """
                You are a Java Code Refactoring AI.
                Your goal is to improve the given Java code based on the provided review comments.

                  **Original Code:**
                  ```java
                  {generated_code}
                  ```

                  **Review Comments:**
                  {review_comments}

                **Task:**
                Carefully apply the suggestions from the review comments to refactor the original code.
                If the review comments state "No major issues found," return the original code unchanged.
                Ensure the final code is complete, functional, and includes necessary imports and docstrings.

                **Output:**
                Output *only* the final, refactored Java code block, enclosed in triple backticks (```java ... ```).
                Do not add any other text before or after the code block.
                """)
            .outputKey("refactored_code")
            .build();

    SequentialAgent codePipelineAgent =
        SequentialAgent.builder()
            .name(APP_NAME)
            .description("Executes a sequence of code writing, reviewing, and refactoring.")
            // The agents will run in the order provided: Writer -> Reviewer -> Refactorer
            .subAgents(codeWriterAgent, codeReviewerAgent, codeRefactorerAgent)
            .build();

    // Create an InMemoryRunner
    InMemoryRunner runner = new InMemoryRunner(codePipelineAgent, 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());
          }
        });
  }
}

シーケンシャル エージェント¶

SequentialAgent¶

例¶

仕組み¶

完全な例：コード開発パイプライン¶

シーケンシャルエージェント¶

`SequentialAgent`¶