[Feature Request] Support O4-mini For Reasoning

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Problem Description

The current implementation of agno does not support o4-mini for reasoning. This limitation is evident in the openai.py file, where the is_openai_reasoning_model function checks for specific model names and IDs. However, the o4-mini model is not recognized, resulting in a warning message and defaulting to manual Chain-of-Thought reasoning.

Current Implementation

The is_openai_reasoning_model function uses a hardcoded list to check for supported models. This approach is inflexible and does not allow users to define their preferred models. The function checks for the following conditions:

  • The model class name is either OpenAIChat, OpenAIResponses, or AzureOpenAI.
  • The model ID contains specific strings, such as o3, o1, 4.1, or 4.5.

Error Message

When an unsupported model is used, agno displays a warning message:

WARNING Reasoning model: OpenAIResponses is not a native reasoning model, defaulting to manual Chain-of-Thought reasoning

Proposed Solution

To address this issue, we propose introducing a way for users to define their preferred models. This can be achieved by allowing users to specify the models they want to use, rather than relying on a hardcoded list. This approach provides more flexibility and allows users to take advantage of the latest models, such as o4-mini.

Alternatives Considered

A quick solution to this issue is to add a match for the o4 model and fix the issue. This approach would involve modifying the is_openai_reasoning_model function to include the o4 model in the list of supported models. However, this solution is not ideal, as it still relies on a hardcoded list and does not provide the flexibility and customization that users require.

Modified Implementation

To support o4-mini for reasoning, we can modify the is_openai_reasoning_model function to include the o4 model in the list of supported models. The modified function would look like this:

def is_openai_reasoning_model(reasoning_model: Model) -> bool:
    return (
        (
            reasoning_model.__class__.__name__ == "OpenAIChat"
            or reasoning_model.__class__.__name__ == "OpenAIResponses"
            or reasoning_model.__class__.__name__ == "AzureOpenAI"
        )
        and (
            ("o4" in reasoning_model.id)  # New o4 reasoning model
            or ("o3" in reasoning_model.id)
            or ("o1" in reasoning_model.id)
            or ("4.1" in reasoning_model.id)
            or ("4.5" in reasoning_model.id)
        )
    ) or (isinstance(reasoning_model, OpenAILike) and "deepseek-r1" in reasoning_model.id.lower())

Conclusion

Supporting o4-mini for reasoning is a crucial feature that can enhance the performance and flexibility of agno. By introducing a way for users to define their preferred models, we can provide a more customized and efficient experience. We welcome contributions and invite users to collaborate on this feature.

Would You Like to Work on This?

We welcome contributions! Let us know if you’d like to help implement this feature.

[X] Yes, I’d love to work on it! [X] I’m open to collaborating but need guidance. [ ] No, I’m just sharing the idea.

Getting Started

If you’re interested in contributing to this feature, here are some steps to get started:

  1. Review the current implementation and understand the requirements.
  2. Familiarize yourself with the agno codebase and the reasoning module.
  3. Propose a solution that meets the requirements and provides a flexible and customizable experience.
  4. Collaborate with the agno team to implement the feature and ensure it meets the requirements.

Frequently Asked Questions

We've compiled a list of frequently asked questions to help you understand the feature request and get started with contributing to the project.

Q: What is the current issue with o4-mini support in agno?

A: The current implementation of agno does not support o4-mini for reasoning. This limitation is evident in the openai.py file, where the is_openai_reasoning_model function checks for specific model names and IDs. However, the o4-mini model is not recognized, resulting in a warning message and defaulting to manual Chain-of-Thought reasoning.

Q: Why is supporting o4-mini important?

A: Supporting o4-mini for reasoning is crucial for enhancing the performance and flexibility of agno. By introducing a way for users to define their preferred models, we can provide a more customized and efficient experience.

Q: What is the proposed solution to support o4-mini?

A: The proposed solution involves introducing a way for users to define their preferred models, rather than relying on a hardcoded list. This approach provides more flexibility and allows users to take advantage of the latest models, such as o4-mini.

Q: What are the alternatives considered to support o4-mini?

A: A quick solution to this issue is to add a match for the o4 model and fix the issue. However, this solution is not ideal, as it still relies on a hardcoded list and does not provide the flexibility and customization that users require.

Q: How can I contribute to supporting o4-mini in agno?

A: We welcome contributions! To get started, review the current implementation and understand the requirements. Familiarize yourself with the agno codebase and the reasoning module. Propose a solution that meets the requirements and provides a flexible and customizable experience. Collaborate with the agno team to implement the feature and ensure it meets the requirements.

Q: What are the benefits of supporting o4-mini in agno?

A: Supporting o4-mini in agno provides several benefits, including:

  • Enhanced performance and flexibility
  • Customizable experience for users
  • Ability to take advantage of the latest models, such as o4-mini

Q: How can I get started with contributing to agno?

A: To get started with contributing to agno, follow these steps:

  1. Review the current implementation and understand the requirements.
  2. Familiarize yourself with the agno codebase and the reasoning module.
  3. Propose a solution that meets the requirements and provides a flexible and customizable experience.
  4. Collaborate with the agno team to implement the feature and ensure it meets the requirements.

Conclusion

Supporting o4-mini for reasoning in agno is a crucial feature that can enhance the performance and flexibility of the project. By introducing a way for users to define their preferred models, we can provide a more customized and efficient experience. We welcome contributions and invite users to collaborate on this feature.

Getting Started

If you’re interested in contributing to this, here are some resources to get you started:

We look forward to working with you on this feature!