Papaya
Papaya is an AI agent observability and optimization platform that analyzes production traces and source code to identify quality, latency, and cost improvements, then delivers ranked recommendations with automated pull requests. It runs 200+ research-backed analyses across six areas of agent behavior, including context bloat, tool execution reliability, and model routing, and integrates with Slack and GitHub for alerts and fix implementation.
- Artificial Intelligence
- Developer Tools
- AI Agents
- Data & Analytics
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
AI agents running in production are complex systems with prompts, context windows, tool calls, sub-agents, and caching layers that degrade in subtle ways. Teams lack visibility into how recurring patterns across thousands of runs connect to the code that causes them, making it difficult to identify which fixes will meaningfully improve quality, reduce latency, or cut costs.
Solution
Papaya analyzes AI agent traces and the code behind them to uncover quality gains and cost wins, turning each finding into an actionable fix. The platform reads production traces in any format, clusters findings by root cause across thousands of sampled runs, and ranks every improvement by expected impact on quality, latency, and cost. Papaya wraps any LLM or agent client with a single line of code, operates asynchronously with no request-path latency, and integrates with Slack and GitHub to deliver alerts and pull requests for approved changes. The system runs 200+ analyses continuously, ties outcomes to real user signals like drop-off and support tickets, and sharpens its recommendations over time as new runs and fixes feed back into the analysis.
Target Audience
Engineering and product teams running AI agents in production, from startups to large enterprises, who need to optimize agent performance across quality, latency, and cost without manual trace analysis.
Features
- Automatic trace and agent detection with analysis across production traces and source code
- 200+ research-backed analyses across six areas: outcome & trajectory, prompt & planning, context/memory/retrieval, tool execution reliability, cost & model routing, and evidence-backed validation
- Ranked recommendations scored for quality, latency, and cost impact with evidence for each fix
- Interactive LLM judge that builds evaluation rubrics automatically from trace data and customer signals
- Slack alerts for drift detection and pull request creation for approved fixes, with human-in-the-loop control
- Live observability dashboards across every agent workflow, including automatic detection of use cases and unmet user needs
- One-line integration supporting any LLM or agent client (OpenAI, Anthropic, Vercel AI SDK, Bedrock, Gemini) with async operation and no request-path latency
- SOC 2 Type 2 compliance with audit logging, data security controls, and data processing agreements