Spend Control for gpt-engineer
gpt-engineer generates entire codebases from prompts — a build loop that can call paid tools and compute. The guard keeps the build on budget.
Why gpt-engineer builds spend
Long build loops call models and tools repeatedly.
Generated projects can trigger paid APIs.
Iteration cycles multiply token spend.
How it works
Attach the guard as a tool: before any spend during the build, the agent gets a deterministic decision.
Rules that fit
Per-build ceiling.
Velocity limit on iteration loops.
Merchant allowlist for provisioned services.
Guard call
from sipi_guard import sipi_guard
decision = sipi_guard(amount=55, merchant="provisioning-api.com", category="compute")
# APPROVED | BLOCKED | FLAGGEDBuild loops with budgets.
FAQ
Does this slow codegen?
No — ~5 ms per check, spend steps only.
What about the models it uses?
Same rules — category caps apply to inference too.
Related
Stop the next $12,400 night.
One API call (or MCP tool) in front of every agent transaction — APPROVED, BLOCKED, or FLAGGED, deterministic, ~5 ms, fully logged.
See plans — from $99/mo Try a live check