Support Llama API in crewAI

[seyeong-han]

Documentation Updates:

• Meta-Llama API Integration:
  • Added a new accordion section titled "Meta-Llama" in the documentation.
  • Provided an overview of Meta's Llama API, including a link to the Meta Llama API.
  • Included instructions for setting the LLAMA_API_KEY environment variable in the .env file.
  • Listed supported models with their specifications, including input/output context lengths and modalities.

[joaomdmoura]

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

Code Review Comment for PR #2825: Support Llama-API in crewAI

Overview

The recent changes introduce comprehensive documentation for the integration of the Meta-Llama API into CrewAI. This is a significant enhancement, as it not only clarifies how developers can use the API but also provides essential context regarding environment setup and model specifications.

Code Improvements

Environment Variable Documentation

• Current:

  LLAMA_API_KEY=LLM|...

• Improvement: It would be beneficial to add contextual comments for clarity:

  # Meta Llama API Key Configuration
  LLAMA_API_KEY=LLM|your_api_key_here  # Get your API key from https://llama.developer.meta.com

Code Example Enhancement

• Current:

  from crewai import LLM
  
  llm = LLM(
      model="meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8",
      temperature=0.8,
      stop=["END"],
      seed=42
  )

• Improvement: Adding comments can guide users on each parameter's purpose:

  from crewai import LLM
  
  # Initialize Meta Llama LLM
  llm = LLM(
      provider="meta_llama",  # Explicitly specify the provider
      model="meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8",
      temperature=0.8,        # Controls randomness (0.0-1.0)
      stop=["END"],          # Custom stop sequence
      seed=42                # For reproducible results
  )
  
  # Example usage in an agent
  agent = Agent(
      name="Analysis Agent",
      llm=llm,
      # ... other configurations
  )

Model Table Enhancements

• Current Format: The existing model table could be expanded with additional information for clarity:
• Improvement:

  | Model ID                                        | Parameters | Context Length | Recommended Use Cases              | Performance Characteristics   |
  |-------------------------------------------------|------------|----------------|------------------------------------|------------------------------|
  | `meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8`| 17B        | 128k          | Multi-modal tasks, General reasoning | Fast inference, Efficient memory usage |

Missing Features Documentation

• Enhancing the documentation with more features will improve usability:

  ### Additional Features
  - **Streaming Support**: All Meta Llama models support streaming responses
  - **Token Usage**: Endpoint provides detailed token usage statistics
  - **Error Handling**: Common error scenarios and troubleshooting steps
  
  ### Rate Limits and Quotas
  Please refer to [Meta Llama API documentation](https://llama.developer.meta.com/docs/limits) for current rate limits and quotas.

Historical Context and Related PR Insights

Focusing on optimizing documentation has previously improved user experiences across various PRs:

1. Improving clarity and structured formatting has led to better user onboarding.
2. Historical PR fix: correct parameter name in crew template test function #2567 focused on expanding documentation related to dependencies, reinforcing the importance of comprehensive updates.

Security Considerations

To enhance security in the documentation:

• Include a section on best practices for API key management.
• Emphasize the importance of proper handling of environment variables to prevent leaks.

General Recommendations

1. Include examples for error handling scenarios.
2. Add a cost considerations and performance benchmarks section for comprehensive user guidance.
3. Ensure version compatibility information is incorporated.

Conclusion

The documentation is a commendable effort that significantly enhances user guidance and usability of CrewAI with the Meta Llama API. However, implementing the suggested improvements related to examples, security best practices, and more detailed feature descriptions will ensure a robust and thorough integration guide for users. Recommend merging this PR once the suggested improvements are incorporated.

---

[seyeong-han]

Hi @lucasgomide, @joaomdmoura !

Thank you for merging our PR into support-provider-llama-api.
I'd like to know if this PR will be merged into main branch so that we can see meta_llama usage in this part of website.