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Implement OpenAI o4-mini based CodeAnalyzer Agent

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Linked challenge: LangChain A2A Code Refactoring with OpenAI o4-mini & AutoGPT

Format
Code-aware
Lines
6
Sections
1
Linked challenge
LangChain A2A Code Refactoring with OpenAI o4-mini & AutoGPT

Prompt source

Original prompt text with formatting preserved for inspection.

6 lines
1 sections
No variables
1 code block
Now, implement the `CodeAnalyzer` agent within your LangGraph workflow. This agent should utilize OpenAI o4-mini to analyze a given code snippet for potential issues, complexity, and refactoring opportunities. The agent should output a list of `refactoring_suggestions` to the `AgentState`. Ensure it can make tool calls to You.com for context-specific best practices. ```python
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool # Initialize OpenAI o4-mini
model_o4_mini = ChatOpenAI(model="openai/o4-mini", temperature=0.7) @tool
def search_you_com(query: str) -> str: """Searches You.com for code best practices or definitions.""" # Implement You.com API call here return f"Search results for '{query}' from You.com" def code_analyzer_node(state: AgentState): print("---CODE ANALYZER---") code = state["code"] # Your o4-mini and You.com tool call logic here # Update state['refactoring_suggestions'] return state
```

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.