顺序智能体¶

`SequentialAgent`¶

SequentialAgent 是一种工作流智能体，它按照列表中指定的顺序执行其子智能体。

当你希望执行按固定、严格的顺序进行时，请使用 SequentialAgent。

示例¶

你想构建一个可以总结任何网页的智能体，使用两个工具：get_page_contents 和 summarize_page。因为智能体必须先调用 get_page_contents 然后才能调用 summarize_page（你不能凭空总结！），所以你应该使用 SequentialAgent 构建你的智能体。

与其他工作流智能体一样，SequentialAgent 不是由 LLM 驱动的，因此它的执行方式是确定性的。也就是说，工作流智能体只关注它们的执行方式（即按顺序），而不是它们的内部逻辑；工作流智能体的工具或子智能体可能使用也可能不使用 LLM。

工作原理¶

当调用 SequentialAgent 的 run_async() 方法时，它执行以下操作：

迭代： 它按照提供的顺序遍历 sub_agents 列表。
子智能体执行： 对于列表中的每个子智能体，它调用该子智能体的 run_async() 方法。

顺序智能体

完整示例：代码开发流水线¶

考虑一个简化的代码开发流水线：

代码编写智能体： 一个基于规范生成初始代码的 LlmAgent。
代码审查智能体： 一个检查生成代码中的错误、风格问题和是否遵守最佳实践的 LlmAgent。它接收代码编写智能体的输出。
代码重构智能体： 一个接收已审查的代码（和审查者的评论）并重构它以提高质量和解决问题的 LlmAgent。

SequentialAgent 非常适合这种情况：

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

这确保了代码先被编写，然后被审查，最后被重构，按照严格、可靠的顺序进行。每个子智能体的输出通过output_key存储在状态中，传递给下一个智能体。

代码

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());
          }
        });
  }
}

顺序智能体¶

SequentialAgent¶

示例¶

工作原理¶

完整示例：代码开发流水线¶

`SequentialAgent`¶