Fix issue #2517: Use agent's LLM for function calling when no function_calling_llm is specified

[devin-ai-integration]

Description

This PR fixes issue #2517 where users would get OpenAI authentication errors even when using non-OpenAI models like Gemini.

The root cause was that when a user didn't explicitly specify a function_calling_llm for their agent or crew, the system would default to using the crew's function_calling_llm, which could be None, leading to OpenAI API key errors even when the agent was configured to use a different model like Gemini.

The fix ensures that when no function_calling_llm is specified, the agent uses its own LLM for function calling rather than defaulting to the crew's function_calling_llm.

Testing

Added tests to verify that agents with non-OpenAI models can use tools without needing OpenAI credentials.

Link to Devin run: https://app.devin.ai/sessions/d6e3c2922797403d8eb47ff23f2f8477
Requested by: Joe Moura (joao@crewai.com)

[devin-ai-integration]

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[joaomdmoura]

Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment for PR #2518

Summary

This PR enhances the crewAI framework by implementing a method for LLM assignment to agents, allowing them to utilize their own LLMs when a specific function_calling_llm is not provided. This change is a key progression in enabling flexibility within the agent functionalities, while importantly maintaining backward compatibility.

Detailed Findings

Code Improvements

1. Type Hints in kickoff Method:

   • Suggest implementing type hints to enhance code readability:

   def kickoff(
       self,
       agent: BaseAgent,
       function_calling_llm: Optional[BaseLLM] = None
   ) -> None:

   The above implementation clarifies expected parameters and return type, making it explicit for future developers.

2. Docstring Addition:

   • A docstring elucidating the fallback behavior for LLM assignment should be included:

   """
   Sets the function calling LLM for the agent.
   If no specific function_calling_llm is provided, uses the agent's own LLM.
   """

3. Agent Test Clarity:

   • Consider reorganizing tests for clarity and maintainability:

   @pytest.mark.vcr(filter_headers=["authorization"])
   def test_agent_uses_own_llm_for_function_calling_when_not_specified():
       """Test that the agent defaults to its own LLM if none is specified."""

Historical Context

Reviewing related PRs can provide insights into previous discussions around LLM assignment and agent behavior. Past changes have focused on compliance with various LLM models, such as non-OpenAI integrations, which are crucial for expanding use cases. Refer to previous PRs here.

Implications for Related Files

• agent_test.py & crew_test.py: Both test files show extensive coverage for the new functionality. Future tests should focus on utilizing combinations of LLMs and provide more edge cases to ensure system robustness.

Suggestions for Improvement

• Documentation Enhancement: A clear section in the documentation should explain the implications of using various LLMs within crewAI, particularly noting backwards compatibility and limitations when incorporating models like Gemini alongside OpenAI functionality.

• Error Handling:

  • Adding validation for LLM compatibility when making function calls could prevent potential runtime errors.
  • Ensure the messages returned during errors are clear and explicit, enhancing user experience during model operations.

• Test Implementation: Future tests should not only evaluate functionality but should also assess performance when multiple LLM integrations are active. Further, edge cases involving empty or invalid configurations can help fortify the application against unexpected inputs.

General Comments

The proposed changes represent a forward-thinking approach that enhances the flexibility of the crewAI framework while maintaining a high level of testing and documentation standards. With the implementation of the suggested improvements, this PR should deliver solid, maintainable code ready for production deployment.

Thank you for your contributions!

[devin-ai-integration]

Closing due to inactivity for more than 7 days.