Optimize your agents
for scale.

We run 200+ research-backed analyses on every layer of your agentic workflow — prompts, tools, context, scaffolding — and continuously recommend exactly what to fix. Quality goes up, cost goes down.

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Workflow · support-ticket-resolutionanalyzing

Connect

→

Analyze

→

Judge

→

Suggest

→

Implement

Papaya recommends

Missing context

Quality+7 ptsCost-12%data needed is missing from payload

Excessive tool calls

Quality+4 ptsCost-23%repeating tool calls inflating cost

Users unhappy with agent action

Quality+9 pts60% of runs need guidance from user

3,984 runs analyzed · 200+ checks per run

Why Papaya

Meaningful improvements from day one.

A thorough workflow analysis surfaces quality gains and cost wins together — backed by evidence from your own production runs.

< 15 min

Time to first improvement

From SDK install to a ranked, evidence-backed finding.

+10%

Success rate improvement

Typical quality increase with first workflow analysis.

$25K+

annualized savings per workflow

For large scale workflows.

One line— wraps any LLM or agent client.

Async— no request-path latency.

In your control— human-in-the-loop on every change.

How it works

Three simple steps to get ranked recommendations.

Wrap your LLM calls and then spend your time building the new features customers want, not on agent maintenance.

Step 01

Share your workflow traces.

One line wraps any LLM or agent client. Connect directly to your observability tool, or share a dataset in any form. Papaya will automatically detect the shape of the data.

Step 02

Papaya analyzes for improvements.

200+ analyses based on the latest research run against your data. Ranked improvements with impact assessment detailed out.

Outcome & Trajectory

Prompt & Planning

Context, Memory & Retrieval

Tool Execution Reliability

Cost & Model Routing

Evidence-Backed Validation

Step 03

Review and implement suggestions.

Understand the exact runs that are producing a recommendation and then choose to implement. Automatic alerts in your tool of choice when a new optimization is found.

The exact run that produced the finding
The prompt, tools, and context as the model saw them
Estimated impact, risk, and confidence

What you get

Continuous monitoring and improvements for scale.

01 / 05

Automated trace detection

Papaya will read your data no matter the shape and automatically detect what is happening in your workflows.

IMPORT WIZARD

Imports

Bring traces in, map them to the canonical contract, then import them for analysis.

Completed

Source

Preview

Import

SOURCE POOL

996traces

2 objects

ANALYSIS SAMPLE

100traces

load target

PREVIEW SAMPLE

100traces

100 objects read

WORKFLOWS

6detected

WORKFLOWS DISCOVERED

What kinds of workflows are in the samples?

Showing the largest workflow clusters from 100 sampled traces.

6 clusters

Flight Cancellation

25 traces

25% of sampled

Flight Change Or Modification

21 traces

21% of sampled

Class Upgrade Or Downgrade

17 traces

17% of sampled

Passenger Or Booking Changes

13 traces

13% of sampled

New Flight Booking

8 traces

8% of sampled

Miles Balance Inquiry

5 traces

5% of sampled

02 / 05

Interactive LLM Judge

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

Judge workflow

Configure the run, review the proposed guidelines, then run and inspect the judge results.

1Configure

Choose workflow, run size, and judge model.

2Review guidelines

Inspect the proposed rubric before running judgments.

3Run judge

Track final judgment progress and spend.

4Inspect results

Review scores, failures, and usage.

Review proposed guidelines

Draft v1 · draft

draft

TASK

Evaluate an airline customer-service agent that handles reservation changes including upgrades, rebookings, cancellations, new bookings for others, and complaint resolution—using tools like get_user_details, get_reserva…

SUCCESS

The agent correctly resolves the customer's reservation request by: (1) verifying the customer's identity and retrieving relevant reservation details, (2) checking eligibility rules (e.g., cancellation window, cabin class, membership tier), (3) performing the correct action (upgrade, cancel, rebook), (4) confirming the outcome with accura…

GUIDELINES

Reviewer Guidelines: Airline Reservation Agent Evaluation — Handles cancel, upgrade, rebook, new bookings. Looks up user & reservation, checks eligibility, confirms actions, escalates when needed.

Configure judge run

normal

Choose how many traces to judge. The rubric draft uses up to 5 examples; the final run scores the run size you pick.

WORKFLOW

Reservation Balance Inquiry Change · macy-test · 901 traces

TRACES TO JUDGE

2550100300All

JUDGE MODEL

Claude Sonnet 4.6 · $3.00/M in

WORKFLOW TOTAL

901

SOURCE CAND.

901

FINAL RUN SIZE

100

RUBRIC EX.

AVG TOKENS

03 / 05

Ranked recommendations to improve your agents

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

Improvements (34)

Proposed changes for this workflow

view all →

Flight Cancellation failures overuse code evaluation

+10pp-$284.12

Large context is being reread across the workflow

+7pplower prompt load-$212.40

Tool outputs need RTK-style reducer instrumentation

-$176.55

Retry churn is visible before the workflow stabilizes

bounded retries-$138.07

High-cost models are handling tiny deterministic calls

-$104.83

04 / 05

Use Papaya where you work

Get alerts to Slack sharing improvements and failures, and deploy those to your code.

Acme Corp

jen

Channels

# general

# eng

# papaya-alerts

# incidents

# papaya-alertsImprovements posted from production workflows

4 members

PapayaApp10:42 AM

2 new high-impact improvements found for flight-cancellation

Flight Cancellation failures overuse code evaluation

+10pp quality−$284.12

Large context is being reread across the workflow

+7pp quality−$212.40

View all 34 improvements in Papaya →

Message #papaya-alerts

05 / 05

Executive summary dashboard

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

Overview

Persisted rollups across apps, workflows, issues, and approvals.

6 apps

TRACES

3,984

18 workflows

QUALITY

52%

weighted by trace count

OPEN ISSUES

52 critical/high

APPROVED

7 approval records

ALL WORKFLOWS

Token and spend trend

18 buckets

Input tokens Output tokens

MODEL MIX

Tokens and spend by model

7 models

Tokens by model

INPUT + OUTPUT

Kimi-K2373.2K

5,528 calls23.3% of total

claude-sonnet-4-5270.5K

2,827 calls16.9% of total

gpt-5.2240.7K

2,356 calls15.1% of total

claude-opus-4-5228.9K

1,767 calls14.3% of total

gpt-5.1-codex196.7K

3,025 calls12.3% of total

Spend by model

PRICED USAGE

claude-opus-4-5$4.25

41.3% of total

claude-sonnet-4-5$2.68

26% of total

gpt-5.2$1.88

18.2% of total

gpt-5.1-codex$0.83

8% of total

Kimi-K2$0.42

4.1% of total

How it operates

200+ research-backed analyses. Run continuously.

Our engine analyzes your sampled traffic around the clock. We understand the user behaviors that produced each pattern, share optimizations, and alert you of drift on sample data.