Show HN: Aft, a Python toolkit to study agent behavior (opens in new tab)

aft was my stab at having a way to understand what claude is doing and also having the language to reason about differences in model behavior when we make them do long agentic runs / change prompts / alter tools etc. The intention of the toolkit to provide an empirical measure of how agent behavior can differ as things changes like environments, tools, prompts etc.

It gives the tools to measure the changes in "behaviors that the users define". This means that it is more like a hypothesis testing framework for what the agent is doing over actually telling what the agent might do.

The reasoning and derivations behind these tools is given over here https://technoyoda.github.io/agent-science.html

Would be very happy to hear feedback and questions. (Please ignore the names given to theorization, it was for shits and giggles)