Stop LLM cost shocks in one line of code.

Your agent shouldn't empty your budget while you sleep. Set a hard spending cap. Enforce it before the first API call.

The Problem

Real-time visibility beats 60+ hour alerts.

Your AI agent looped for 5 hours Friday night. Saturday morning you see $487 charged. But OpenAI's billing alert didn't fire until Monday 9 AM—60+ hours after the problem started. By then, the damage is done. You need cost visibility before the API call, not after the invoice arrives.

Someone's agent is eating your budget. Who?

Fourteen microservices calling GPT-4. One team's autocomplete endpoint burned $1,920 (40% of the $4,800 February bill). Another team's retry loop wasted $3,600. But nobody owned it until the invoice landed. With no per-agent tracking, "we all paid it" means "nobody fixed it." Your team deserves per-task cost visibility.

47 retries = $360 you didn't budget for.

Your agent hits a 529 server error. It retries immediately. Then again. Then 47 more times. A simple task that should cost $0.05 ends up costing $2.80 (56x). Without per-agent cost bounds, your agent spirals invisibly. You can't cap what you can't see.

The Solution

One line. Full control.

LLM-Breaker is a decorator you drop into your code:

# Before: unlimited agent loops
agent = Agent(model="gpt-4")

# After: 100-token hard cap
from llm_breaker import cost_cap
agent = cost_cap(Agent(model="gpt-4"), budget=100)

Cost cap enforces before the API call. Retry spirals stop. Agent complexity has real bounds.

Why This Works

Pre-request enforcement — catch budget overflow before it happens, not 60 hours later.
Per-agent granularity — each agent/task gets its own budget; team stops finger-pointing.
Works everywhere — OpenAI, Anthropic, Gemini. One decorator. Zero config debt.

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