Papaya
Scans your AI agent's production traces and opens PRs to fix what's costing you
Papaya wraps your LLM or agent client in one line, then runs 200+ analyses against production traces to find concrete failure patterns, like context bloat re-sending the same history every turn (18% of runs, -$2.1K/month) or a drifted tool schema breaking valid calls (11.4% of runs, -$840/month), and ranks each fix by quality, latency and cost impact. Findings land as Slack alerts with evidence and can be shipped as a pull request, with a human reviewing and approving every change before it deploys. The team claims under 15 minutes from SDK install to a first ranked finding, and a typical +10% success-rate lift on the first workflow analysis.
What holds up
- +One-line SDK wrap, async and off the request path — no added latency to the agent's response
- +Every fix needs human review and approval before deploying — nothing changes automatically
- +Findings come with named mechanisms and dollar estimates, like a specific prompt-cache fix
Mind the limits
- −No pricing shown on the page — cost looks like it depends on a custom demo or quote
- −The $25K+ savings figure is for 'large scale workflows' — smaller agents may see less