What they do
analyses agent traces and sends approved fixes as pull requests
How close to us
2, near
measures and improves agent quality and cost for engineers; adjacent to seeing whether agents produce.
Wedges
- W2 Seeing whether your AI agents actually produce adjacent
Who buys
- Firm size
- not known
- Buyer type
- developer
- Role
- head of engineering
- UK
- not known
Their own description (from YC)
Papaya is the optimization engine for AI agents. Engineers don't have time to continually improve the agents they've shipped — the context bloat, overly chatty subagents, and broken tool calls sit in production traces nobody reads, quietly hurting agent efficiency. With Papaya they connect their agent directly via SDK, get actionable recommendations on how to improve quality, latency, and cost, and the ones they approve are pushed to production as pull requests.
Papaya runs 200+ research-backed analyses across context, prompts, prompt caching, subagents, and tool calls. Every recommendation is ranked by impact and comes with the production runs that produced it — typically a 20%+ quality improvement on the first workflow analysis.
Full teardown
No full teardown yet. On the deep list.
Technical detail
- Slug
- papaya
- Lens hash
- 179096c479
- Pass
- screen
- Stale
- no