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Quickstart

Wrap any function with @nullrun.protect to track its cost, tools, and behaviour, and let NullRun halt it when it goes off the rails.

from openai import OpenAI
from nullrun import init_or_die, guarded, protect, shutdown

init_or_die(api_key="nr_live_...")        # exits cleanly if api_key missing
client = OpenAI()

@guarded                                # catches NullRunError, prints
@protect                                # the catalog user-message,
def answer(prompt: str) -> str:         # sys.exit(1) — zero boilerplate
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
    )
    return response.choices[0].message.content

if __name__ == "__main__":
    try:
        print(answer("What does NullRun do?"))
    finally:
        shutdown()

That's it — every call inside answer() is now cost-attributed and governed by your workspace policy. On any policy outcome (budget cap, tool block, rate limit, transport outage), @guarded prints the catalog wording on stderr and exits 1 — no try/except NullRunError needed.

What gets tracked

  • LLM tokens in and out
  • Cost in cents (per-call and aggregate)
  • Latency
  • Tool calls (if you use a framework integration)
  • Loop / retry patterns

What can go wrong

See Troubleshooting for the full table of expected behaviours (budget cap, loop, sensitive-tool, gateway down, kill/pause, etc.) and recovery steps. For the three-layer error model and why the boilerplate stays at zero, see Concepts → Error handling.

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