papayya
Production infrastructure
for AI agents.
Crash recovery, cost control, and full execution traces — so your agents can run for hours without burning your budget or losing progress.
# wrap your existing agent
from papayya.durable import papayya
t = papayya()
run = t.run(agent="my-agent", budget_usd=5.0)
# every task is checkpointed
search = run.task("search", my_search_fn)
summarize = run.task("summarize", my_llm_call)
results = search("AI agents")
summary = summarize(results)
run.complete(summary)You built the agent. Now what?
AI agents that call tools, make decisions, and run for minutes or hours need more than a single API call. They need infrastructure.
Your agent crashed at step 47.
You start from scratch.
Papayya checkpoints every step and resumes automatically.
A stuck loop burned $200 overnight.
You didn't notice until morning.
Papayya enforces per-run budgets and kills runaways.
A customer got a bad output.
You can't reproduce it.
Papayya records full traces — replay any run from any step.
Two ways to use Papayya
Wrap your existing code or deploy to the cloud. Either way, you get checkpointing, cost tracking, and observability.
Local Execution
Already have an agent? Add 5 lines. Your code stays the same — Papayya adds checkpointing and cost tracking.
from papayya.durable import papayya
t = papayya()
run = t.run(agent="my-agent", budget_usd=5.0)
# your existing functions, now checkpointed
search = run.task("search", search_fn)
summarize = run.task("summarize", llm_call)
results = search("AI agents")
summary = summarize(results)
run.complete(summary)Cloud Execution
Deploy your agent and let Papayya run it. Containers, crash recovery, scheduling, and scaling — handled.
from papayya import agent
from openai import OpenAI
@agent(name="research-bot", model="gpt-4o-mini", budget_usd=1.00)
def research_bot(input_data):
client = OpenAI()
return client.chat.completions.create(...)
$ papayya deploy
Deployed research-bot v3 ✔What you get
Everything your agent needs to run safely in production.
Crash Recovery
Checkpoint after every step. If a worker dies, the run resumes from the last completed step — not from scratch.
Budget Enforcement
Set a dollar cap per run. Papayya tracks token usage in real time and stops the run before it blows your budget.
Execution Traces
Step-by-step trace of every LLM call and tool invocation. See exactly what happened, when, and how much it cost.
Step Replay
Replay any run from any step for debugging. Reproduce customer issues deterministically.
Any Model, Any Provider
Your code calls the LLM directly. OpenAI, Anthropic, Google, Mistral, self-hosted — bring your own API key.
Schedules & Webhooks
Run agents on cron schedules or trigger them from external events. Per-run budgets on every execution.
Simple, compute-based pricing
You bring your own API keys — we never touch your LLM costs. You only pay for compute time.
Free
Try it out. No credit card required.
- ✓100 compute-minutes/mo
- ✓2 concurrent runs
- ✓7-day trace retention
- ✓Community support
Starter
For teams shipping their first agents to production.
- ✓500 compute-minutes/mo
- ✓5 concurrent runs
- ✓30-day trace retention
- ✓$0.004/min overage
- ✓Email support
Pro
For teams running agents at scale.
- ✓3,000 compute-minutes/mo
- ✓25 concurrent runs
- ✓90-day trace retention
- ✓$0.0035/min overage
- ✓Priority support
Scale
For high-volume production workloads.
- ✓15,000 compute-minutes/mo
- ✓100 concurrent runs
- ✓365-day trace retention
- ✓$0.003/min overage
- ✓Dedicated support