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Model-agnostic AI code

crewaiinc/crewai
Based on 5 comments
Python

When building applications that interact with AI models, avoid hardcoding model-specific behavior and instead design for flexibility across different LLM providers and versions. This approach increases resilience and makes your code more maintainable as the AI ecosystem evolves.

AI Python

Reviewer Prompt

When building applications that interact with AI models, avoid hardcoding model-specific behavior and instead design for flexibility across different LLM providers and versions. This approach increases resilience and makes your code more maintainable as the AI ecosystem evolves.

Key practices:

  1. Allow LLM instances to be passed as parameters rather than hardcoding model names
  2. Implement provider-specific handling through abstraction rather than special cases
  3. Validate LLM instances early to prevent runtime errors
  4. Use dependency injection for LLM components to make testing easier

Example:

# Instead of this:
def process_with_ai(prompt, model="gpt-4o"):
    client = OpenAI()
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# Do this:
def process_with_ai(prompt, llm=None):
    llm = llm or get_default_llm()
    
    # Validate LLM instance early
    if not hasattr(llm, 'call') or not callable(llm.call):
        raise ValueError("Invalid LLM instance: missing call method")
    
    # Use provider-agnostic interface
    return llm.call([{"role": "user", "content": prompt}])

def get_default_llm():
    # Create default LLM instance with appropriate config
    return BaseLLM(model="default-model")

This approach lets you easily switch models, test with mock LLMs, and adapt to new model APIs without changing your core application logic.

5
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Python
Primary Language
AI
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