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CacheCatch
Agent Cache Audit
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%.
Top comment
CacheCatch audits AI agent context and shows what to move so repeated tokens hit cache instead of full-price input. It finds context waste, prompt cache misses, and hidden agent cost leaks; and give you actionable insights how to fix them.
About CacheCatch on Product Hunt
“Agent Cache Audit”
CacheCatch was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #103 on the daily leaderboard. 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%.
On the analytics side, CacheCatch competes within Open Source, Analytics, Artificial Intelligence and GitHub — topics that collectively have 755.7k followers on Product Hunt. The dashboard above tracks how CacheCatch performed against the three products that launched closest to it on the same day.
Who hunted CacheCatch?
CacheCatch was hunted by Shayan T.. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of CacheCatch including community comment highlights and product details, visit the product overview.