PapayaPapaya

Backed byY Combinator

Continuously improve your agents.

Papaya reads your agents' traces and code, finds the fixes you didn't know to look for, and opens the PR — every fix ranked by quality, latency, and cost impact.

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conversations analyzed
1.1M+

“The always-on recommendations from Papaya have significantly reduced the error states in our inference. As we find ourselves writing most of our prompts with AI, I think this product is a no-brainer.”

Ayush Tomar, Sunny Health

“Papaya has made my life much easier by giving me actionable recommendations to improve my AI agents. The simplified trace viewer helps me quickly understand what is happening.”

Jeff Hui, Hamlet
Why Papaya

Papaya doesn’t stop at observability.
It also ships the fixes.

We analyze your production traces and your agent’s code to uncover quality gains and cost wins, and turn each one into a fix.

+20%
Quality improvement
Typical success-rate gain from the first workflow analysis.
−30%
Latency reduction
Faster runs from trimmed context, fewer retries, and smarter model routing.
$25K+
Cost savings
Annualized, per large-scale workflow.
One line— wraps any LLM or agent client.
Async— no request-path latency.
In your control— human-in-the-loop if needed.
How it works

Papaya watches your agentand finds what’s holding you back.

Papaya reads your production traces and the code behind them: prompts, context, tools, models, and outcomes. It connects recurring behavior across thousands of runs to the code that causes it, and shows you which fixes matter most.

See how Papaya works, step by step
Request
Understand
Context
Plan
Next action
Use tool
Observe
Draft
Verify
Outcome

Context bloat builds up when the same history is sent on every turn.

18.0% of runs−$2.1K/mo

Prompt caching fails when context is ordered incorrectly.

62.4% cost reduction−$1.4K/mo

Tool schema drift causes valid requests to fail.

11.4% of runs−$840/mo

Retry loops persist because the agent is not configured to recover.

1,422 affected runs+8.2s latency
Book a demo

We'll work with you on your first agent. Share traces from one workflow, in any format, and get back a ranked set of improvements with the evidence behind each one. Your first analysis is on us.

What you get

The full improvement loop in one place.

Papaya ingests traces in any shape, builds your quality rubric, ranks every improvement by impact, and delivers fixes where you work — from Slack alert to opened PR.

01 / 07

Automatic issue detection across every trace

Papaya reads your traces in any shape and flags what’s going wrong, with how many traces and users each issue affects. Connect your code and it pinpoints the cause.

Papaya issue detection with issues found, affected traces, impacted users, and the top issues by affected traces
02 / 07

Use cases, identified automatically

Papaya splits your traces into the business use cases your agent handles, and shows which ones work and which ones fail.

Papaya use cases table with trace counts, quality, and the failing use cases flagged
03 / 07

User signals from real conversations

See the needs your agent couldn’t meet, how often they come up, and how those conversations ended.

Papaya user signal showing an unmet need, its share of conversations, and how they ended
04 / 07

Ranked recommendations to improve your agents

Know which improvements will have the highest impact on quality, latency, and cost. Implement the ones you choose.

Papaya improvements ranked by priority, with the selected fix's findings and expected impact on quality, latency, and cost
05 / 07

Use Papaya where you work

Get Slack alerts when Papaya finds an improvement, and get the fix as a pull request against your code.

Papaya posting a P0 improvement to a Slack channel
06 / 07

Live observability across every workflow

Get an overall picture of performance of your agents with top recommendations for improvement.

Papaya observability with success rate, cost, P95 latency, trace count, and pass/fail over time
07 / 07

Interactive LLM Judge

Build an evaluation rubric automatically from trace data and customer signals. Tell it what edits you want to make.

Papaya LLM Judge guidelines with score bands drafted from traces
Book a demo

Pick one agent and we'll work through it with you: a ranked set of fixes, each with the evidence behind it and its impact on quality, latency, and cost.

How it operates

Research-backed recommendations.
Your agents, always covered.

Papaya runs 200+ analyses on your own traffic around the clock and flags the fixes worth making — with proof.

A focus bracket groups repeated signals in a field of production runs

Finds recurring patterns

Findings cluster across thousands of sampled runs by root cause — not one trace at a time. Each one tells you how many runs it affects.

A moving focus window repeatedly scans a live row of production signals

Catch drift before customers do

Live alerts when quality metrics drift — before a customer escalates. You learn when it matters, not when you remember to check.

Outcome signals converge inside a shared evidence bracket

Tied to real outcomes

Drop-off, thumbs-down, Slack replies, and support tickets — all tied to the runs and code that actually produced them.

Successive focus brackets rise through an improving field of signals

Compounds over time

Every fix you ship and every new run feeds the next analysis. The system doesn't start from zero — findings get sharper as you go.

Read more about the research

Backed by the best

  • Y Combinator
  • Engineering Capital
  • Everywhere Ventures
Get started

Start with one workflow.
Your first analysis is on us.

Share your traces in whatever form you have, even a raw export — no integration needed. You'll get back a ranked set of improvements with the evidence behind each one, and what every fix is worth in quality, latency, and cost. No commitment: the audit is yours to keep either way.

Book a demo