Papaya

What it is

Papaya reads your agents' traces and code to find fixes you didn't know to look for, ranked by quality, latency, and cost impact. It automatically detects issues, groups traces into business use cases, and delivers recommendations as Slack alerts or pull requests.

Papaya runs 200+ analyses across prompts, context, memory, retrieval, tool calls, sub-agents, prompt caching, and model choice. It connects recurring behavior across thousands of runs to the code that causes it, showing which fixes matter most. The platform learns from your production data continuously and compounds improvements over time.

Integration takes minutes using the Papaya SDK (supporting OpenAI, Anthropic, Vercel AI SDK, Bedrock, and Gemini) or by connecting your existing observability tool. You can optionally connect your code repository for more precise fixes.

Questions from the bench

How is Papaya different from LangSmith, Langfuse, or Braintrust?

Papaya groups traces into use cases and identifies recurring problems across thousands of runs, then delivers ranked fixes. Unlike those tools that show what happened in each run, Papaya picks up from there. You don't need to replace your existing tool—Papaya works alongside it and can import traces from it.

What kinds of problems does Papaya find?

Papaya runs 200+ analyses across prompts, context and memory, retrieval, tool calls, sub-agents, prompt caching, and model choice—not just prompts. It merges overlapping findings into a short list of fixes ranked by their expected effect on quality, latency, and cost.

How do I connect my agent, and how much work is it?

Wrap your LLM client with the Papaya SDK (supporting OpenAI, Anthropic, Vercel AI SDK, Bedrock, and Gemini) or use the fetch wrapper for a custom agent loop. Setup takes a few minutes—you can paste the setup prompt into Claude Code or Codex and let a coding agent do the integration for you. Traces are sent asynchronously so Papaya never slows down your agent.

How does Papaya know whether a run succeeded?

For each use case, Papaya builds an LLM judge from your traces automatically, so you don't have to write a rubric. The judge checks each step along the way and whether the run achieved what the user wanted. You can edit any judge, bring your existing evals, or add user feedback like thumbs up and down.

What data does Papaya need?

Full traces work best: the system prompt, user messages, model responses, tool calls with their results, sub-agent steps, timestamps, and model names. Partial exports still work but findings are less specific. About 100 traces from one agent is enough for a first analysis.