Spending tokens blind drains your whole budget
Tokdash v2.7.0 shipped Oct 24.
8 open-source GitHub repos for tracking token spend and capping agent budgets:
22.8k stars as of Oct 24. MIT and Apache-2.0. Pushed Oct 19–24.
MEASURE THE BURN
01. codeburn ★11.6k
▸ https://t.co/7JqpqGxGTI
→ spend by model, project, task
02. tokscale ★5.7k
▸ https://t.co/jUNpRpda2X
→ token and cost CLI across agents
03. Tokdash ★87
▸ https://t.co/orOp04Ma9l
→ local dashboard with quota resets
PRICE EACH CALL
04. genai-prices ★401
▸ https://t.co/vo7yQUpoAr
→ price table for LLM calls
CAP THE SPEND
05. floe-guard ★347
▸ https://t.co/mJ5dgXB9fV
→ hard stop before budget ceiling
06. cycles-server ★5
▸ https://t.co/tX6YrMkcs7
→ reserve budget before agent acts
07. ai-gateway ★2.3k
▸ https://t.co/GVfqDcSYpN
→ quotas and attribution at gateway
08. openmeter ★2.4k
▸ https://t.co/cd7PQmrRwB
→ usage metering and enforcement
pip install floe-guard
from floe_guard import BudgetGuard
guard = BudgetGuard(limit_usd=5.00)
the stack:
logs → count → price → guard → reserve → quota → meter
Wrong ceiling stops a live agent.
Not the model. The blind spend around it.
Full cost stack in the terminal ↓
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