Papaya

Teams running AI agents in production run into recurring inefficiencies that are hard to catch by hand: redundant retries, excessive tool calls, missing or bloated context, uncached embeddings, serial API calls, over-broad retrieval, model overkill, unbounded loops, chatty sub-agents, stale tool schemas, and verbose system prompts. Each of these quietly adds cost, slows responses, or degrades answer quality, but surfaces only as scattered symptoms across thousands of runs rather than a single obvious bug. Without a way to connect these patterns back to the specific code causing them, teams are left manually reviewing traces or reacting only after a customer complains.

Papaya wraps any LLM or agent client with a single line of code and asynchronously reads production traces alongside the underlying prompts, context, tools, and models. It runs over 200 analyses continuously, clustering recurring failure patterns across thousands of runs by root cause rather than inspecting traces one at a time, and ranks each finding by its impact on quality, latency, and cost. Findings are tied to real outcomes like drop-off, thumbs-down ratings, and support tickets, and delivered where teams work: as Slack alerts and as pull requests against the agent's own code. It also automatically splits traffic into business use cases, builds an evaluation rubric from trace data via an interactive LLM Judge, and offers a trace viewer and live observability dashboard, with humans able to stay in the loop on any fix.