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Enterprise Layer Intelligence
Categories/AI Orchestration & MLOps/Papaya
Papaya

Papaya

Analyze AI agent traces and fix code issues

Pricing model

Free Tier

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Papaya to inspect production traces and code from your AI agents to fix quality issues, reduce latency, and lower running costs. The software monitors execution paths, identifies context bloat, catches broken tool schemas, and flags excessive retry loops across thousands of runs. It ranks every detected issue by its expected financial and performance impact, then suggests actionable fixes directly via Slack notifications or opened GitHub pull requests, allowing your engineering team to approve code updates without manual trace debugging.

What Papaya does

Inspect LLM execution tracesIdentify context bloat in prompt historiesGenerate code fixes for agent issuesReceive performance drift alerts in SlackCreate evaluation rubrics with an interactive LLM judgeTrack agent latency and execution cost metricsSubmit pull requests for agent fixesAutomatic detection of bottlenecks across agent tracesDirect code fix generation with automated pull requestsSlack alerts for agent performance and quality driftIssue ranking based on quality, latency, and cost impactInteractive evaluation rubric builder using trace dataDetection of context bloat, prompt caching failures, and retry loopsAsynchronous trace monitoring with zero request-path latency

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price
Developer planFree
Business plan$500 / mo

Tutorials & Demos

Frequently asked

SlackSlackGithubGithub

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Papaya, AI agent monitoring, trace analysis, LLM observability, latency reduction, prompt debugging, prompt caching, tool schema drift, retry loops, automated pull requests, LLM evaluation, developer tools