Every time I ran a LangChain research agent, I was nervous.

Not because the agent was bad. It was good. Too good. One bad prompt, one infinite loop, one customer who accidentally triggered 50 parallel runs, and my monthly OpenAI cap would be toast. By the time the alert fired, the damage was done.

Monthly caps are reactive. They fire after the money is spent.

So I built something different.

The idea: block the run before it starts

Instead of capping after the fact, I wanted to check budget before any tokens are consumed. If the budget says no, the agent never runs. No compute. No cost.

I called it a preflight check, borrowing the term from aviation. Before a plane takes off, the pilot checks everything. Before the agent runs, you check the budget.

from agentbill import AgentBillClient

client = AgentBillClient ( api_key = " agb_your_key " )

check = client . preflight ( agent_id = " research_agent " , customer_id = " user_123 " , estimated_units = 10 ) if not check .…