Identify and fix prompt-cache leaks for AI coding agents seamlessly.
Project details
kvleak provides insights into where AI coding agents, like Claude Code, leak prompt-cache costs. With a 95% cache hit rate, it identifies expensive cache rebuilds and helps optimize spending with a simple command, ensuring efficient use of resources without the need for configurations or dependencies.
Optimize AI prompt caching and minimize unnecessary spending with kvleak. This powerful tool helps identify where an AI coding agent, such as Claude Code or Codex CLI, is leaking prompt-cache money and offers solutions to improve efficiency.
With a typical cache hit rate exceeding 95%, a significant portion of input costs—up to 20%—can still be attributed to cache rebuilds. kvleak effectively highlights the sources of these leaks and provides straightforward solutions, all implemented with a single command.
kvleak · prompt-cache leak report
Cache hit rate: 95.4% | Leaked to misses: $537.72 | Total input spend: $2,602
kvleak tune --apply # Optimize settings
kvleak statusline --install command, allowing users to visualize cache countdowns in their status bar.The true cost of a cache hit can be deceptive. A cache miss may appear infrequent, yet it costs significantly more than anticipated—potentially leading to costly rebuilds. kvleak clarifies these dynamics by offering detailed analyses of the underlying costs associated with misses, ensuring every user understands their spending patterns.
Several functionalities of kvleak further enhance its value:
kvleak probe command analyzes user history, providing before-and-after comparisons of changes made through adjustments.kvleak refine --apply
kvleak is an indispensable tool for developers relying on AI coding agents to enhance productivity and efficiency. By providing actionable insights and facilitating reliable optimizations, kvleak ensures that every dollar spent on input is truly justified. For detailed documentation and guidelines, visit the full guide. This tool not only enhances financial efficiency but also contributes to a better understanding of AI cache management.
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