Launching today
CacheCatch
Agent Cache Audit
8 followers
Agent Cache Audit
8 followers
Audit LLM token costs and prompt-cache efficiency across LangSmith, Langfuse, Braintrust, and local IDE agents. CacheCatch detects cache breakers, estimates wasted spend, and gives exact prompt-layout fixes to cut AI costs up to 90%.
How does it actually identify a cache breaker in practice, like is it pattern matching on prompt structure or something more dynamic?
A Slack or Teams alert when wasted spend crosses a daily threshold would be really useful, so I don't have to remember to check the dashboard. Bonus if it links straight to the specific prompt that triggered the spike.
Caught a sneaky cache breaker in one of our prompts that was silently doubling our spend — the layout fix recommendation was specific and worked on the first try. Wish I'd found this months ago.