chore: Creates a sample main.py to demonstrate how to use adk as a library for the workflow agent in docs.

For Issue #573.

PiperOrigin-RevId: 760380509

contributing/samples/workflow_agent_seq/agent.py

@@ -15,80 +15,97 @@
 from google.adk.agents.llm_agent import LlmAgent
 from google.adk.agents.sequential_agent import SequentialAgent
 
+# 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="code_writer_agent",
-    model="gemini-1.5-flash-001",
-    instruction="""You are a Code Writer AI.
-    Based on the user's request, write the initial Python code.
-    Output *only* the raw code block.
-    """,
-    description="Writes initial code based on a specification.",
-    # Stores its output (the generated code) into the session state
-    # under the key 'generated_code'.
-    output_key="generated_code",
+    name="CodeWriterAgent",
+    model="gemini-1.5-flash",
+    # 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="code_reviewer_agent",
-    model="gemini-2.0-flash-001",
-    instruction="""You are a Code Reviewer AI.
+    name="CodeReviewerAgent",
+    model="gemini-2.0-flash",
+    # 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.
 
-Review the below Python code.
+    **Code to Review:**
+    ```python
+    {generated_code}
+    ```
 
-```
-{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?
 
-Provide constructive feedback on potential errors, style issues, or improvements.
-Focus on clarity and correctness.
-Output only the review comments.
-
-    """,
+**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.",
-    # Stores its output (the review comments) into the session state
-    # under the key 'review_comments'.
-    output_key="review_comments",
+    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="code_refactorer_agent",
-    model="gemini-2.0-flash-001",
-    instruction="""You are a Code Refactorer AI.
+    name="CodeRefactorerAgent",
+    model="gemini-2.0-flash",
+    # 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.
 
-Below is the original Python code:
+  **Original Code:**
+  ```python
+  {generated_code}
+  ```
 
-```
-{generated_code}
-```
+  **Review Comments:**
+  {review_comments}
 
-Below are the 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.
 
-{review_comments}
-
-Refactor the code based on the provided feedback.
-
-Output *only* the final, refactored code block.
-    """,
+**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.",
-    # Stores its output (the refactored code) into the session state
-    # under the key 'refactored_code'.
-    output_key="refactored_code",
+    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="code_pipeline_agent",
+    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