model-router

The model-router automates AI model selection for code analysis by evaluating your task and identifying the optimal NEURO API models from a library of 120+ options, improving accuracy and efficiency.

Multi-model integration ensures workflows benefit from multiple AI models while optimizing API costs by routing subagent tasks to cheaper models. This minimizes overall expenses without sacrificing analytical quality.

Run a multi-model code review by executing the model_router.py Python script with your specific task description and target skills as arguments. This triggers automated model selection for your analysis.

Yes, you need access to the NEURO API and Python to run the task analysis and model execution. The automated model selection relies on this API to access its library of 120+ models.

The best way to manage multi-model workflows for complex coding tasks is to automate model selection, ensuring each subtask uses the most effective and cost-efficient AI model available through the API.

You should avoid automated model routing if your environment lacks Python or NEURO API access, or if your coding tasks are too simple to benefit from the overhead of querying a 120+ model library.