Hi HN, I'm Yusuke Hata. I built polaris (https://github.com/octu0/polaris), an open-source Go framework designed to help AI agents effectively use distributed Tool/Agent via Function Calling.

Managing function schemas gets messy fast.

polaris simplifies this with a central cluster: agents connect, dynamically fetch up-to-date function definitions, and are ready to call. No more manual schema wrangling for every agent/update. (Details in README).

More importantly, polaris provides the infrastructure to unlock AI's inherent ability to orchestrate distributed functions dynamically.

Here's how it differs from traditional RPC:

  - No Manual Interface Definition Exchange: Agents get definitions directly from the polaris-registry cluster upon connection.

  - AI Chooses the Path: Instead of humans hard-coding how services interact, the AI agent itself analyzes the available Function Definitions from the cluster and decides which remote function to call based on the current context.

The goal is to make it practical for AI agents to autonomously leverage a wide array of distributed tools/services, moving beyond the rigid, human-defined connections of typical RPC.

I'd love your feedback on this approach. Ask me anything!