Papaya
VerifiedReads your AI agents' traces and code, then ships ranked fixes as pull requests
Product preview
Papaya
What it is
Papaya helps engineering teams improve AI agents already running in production. It ingests agent traces along with the agent's code and looks for recurring problems such as redundant retries, excessive tool calls, missing context, oversized prompts, uncached embeddings, serial API calls and overly powerful models for simple steps. Each finding becomes a recommendation scored by its expected effect on quality, latency and cost, and on the Business plan Papaya can open the pull request itself and share recommendations in Slack. A trace viewer and observability dashboards support debugging. A free developer tier covers one agent with limited monthly traces, with paid and enterprise plans for larger workloads.
Key features
- Trace and code analysis
- Fixes ranked by quality, latency and cost
- Automated pull requests
- Observability dashboards
What we liked
- + Goes beyond monitoring to concrete fixes
- + Free tier for a single agent
- + Impact scoring helps prioritize
Worth noting
- · Big jump from free to paid tier
- · Most useful once agents have real traffic
Editor's notes
More context from AI BirdView
Plenty of tools show you what your agents did; Papaya focuses on what you should change. By reading both production traces and the underlying code, it can point to specific causes of slow or expensive runs and turn them into fixes, which saves engineers from combing through logs by hand.
- Detects common agent issues like retry loops, chatty sub-agents and bloated prompts
- Every recommendation is scored for its likely quality, latency and cost impact
- Business plans can open the pull request for you
- Slack sharing keeps the whole team aware of new findings
- Enterprise options include PII redaction and SLAs
Papaya is best for teams running LLM agents in production who care about reliability and inference spend. Our tip: start with the free developer tier on your busiest single agent, since more traces give the analysis more to work with, and review the top-ranked recommendations first; small changes such as caching embeddings or trimming a system prompt often pay off quickly. Connect Slack once you move to a paid plan so new findings reach the people who own each agent.
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