Learn Provider-Agnostic Model Routing by Building a Tiny LLM Switchboard

Every few weeks, new language models emerge, prompting discussions in study groups about cheaper alternatives and better performance for specific tasks. However, verifying these claims quickly proves challenging due to hard-coded client dependencies in existing test scripts. This led to the creation of a ~60-line Python script allowing seamless switching between multiple providers with minimal code changes.

The script implements a provider-agnostic switchboard that routes tasks to different backends based on the task type, enabling evaluation of various models without altering the task logic. The switchboard defines task types (summarize, code, chat) and maps them to corresponding backends, including a fallback option. This approach allows easy adaptation to new models by adding a single entry, keeping evaluation criteria constant while enabling backend rotation.

The script is written in standard Python 3.11+, requiring no third-party packages or API keys, and provides a simple interface for executing tasks and monitoring costs. The final output demonstrates routing tasks to different backends, capturing costs, and displaying results, highlighting the effectiveness of the switchboard in managing diverse LLM providers.