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| import os | |
| from smolagents import CodeAgent, ToolCallingAgent | |
| from smolagents import OpenAIServerModel | |
| from tools.fetch import fetch_webpage | |
| from tools.yttranscript import get_youtube_transcript, get_youtube_title_description | |
| import myprompts | |
| # --- Basic Agent Definition --- | |
| class BasicAgent: | |
| def __init__(self): | |
| print("BasicAgent initialized.") | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| try: | |
| # Use the reviewer agent to determine if the question can be answered by a model or requires code | |
| print("Calling reviewer agent...") | |
| reviewer_answer = reviewer_agent.run(myprompts.review_prompt + "\nThe question is:\n" + question) | |
| print(f"Reviewer agent answer: {reviewer_answer}") | |
| question = question + '\n' + myprompts.output_format | |
| fixed_answer = "" | |
| if reviewer_answer == "code": | |
| fixed_answer = gaia_agent.run(question) | |
| print(f"Code agent answer: {fixed_answer}") | |
| elif reviewer_answer == "model": | |
| # If the reviewer agent suggests using the model, we can proceed with the model agent | |
| print("Using model agent to answer the question.") | |
| fixed_answer = model_agent.run(myprompts.model_prompt + "\nThe question is:\n" + question) | |
| print(f"Model agent answer: {fixed_answer}") | |
| return fixed_answer | |
| except Exception as e: | |
| error = f"An error occurred while processing the question: {e}" | |
| print(error) | |
| return error | |
| model = OpenAIServerModel( | |
| model_id="gpt-4.1-nano", | |
| api_base="https://api.openai.com/v1", | |
| api_key=os.environ["OPENAI_API_KEY"], | |
| ) | |
| reviewer_agent= ToolCallingAgent(model=model, tools=[]) | |
| model_agent = ToolCallingAgent(model=model,tools=[fetch_webpage]) | |
| gaia_agent = CodeAgent(tools=[fetch_webpage,get_youtube_title_description,get_youtube_transcript ], model=model) | |
| if __name__ == "__main__": | |
| # Example usage | |
| question = "What was the actual enrollment of the Malko competition in 2023?" | |
| agent = BasicAgent() | |
| answer = agent(question) | |
| print(f"Answer: {answer}") |