Tokdash 2.7.0: 8 open-source repos for tracking agent token spend
Tokdash 2.7.0 shipped Oct 24, alongside a list of eight open-source repos for measuring token cost and capping agent budgets, totaling 22.8k stars.
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://github.com/getagentseal/codeburn → spend by model, project, task 02. tokscale ★5.7k ▸ https://github.com/junhoyeo/tokscale → token and cost CLI across agents 03. Tokdash ★87 ▸ https://github.com/JingbiaoMei/Tokdash → local dashboard with quota resets PRICE EACH CALL 04. genai-prices ★401 ▸ https://github.com/pydantic/genai-prices → price table for LLM calls CAP THE SPEND 05. floe-guard ★347 ▸ https://github.com/Floe-Labs/floe-guard → hard stop before budget ceiling 06. cycles-server ★5 ▸ https://github.com/runcycles/cycles-server → reserve budget before agent acts 07. ai-gateway ★2.3k ▸ https://github.com/envoyproxy/ai-gateway → quotas and attribution at gateway 08. openmeter ★2.4k ▸ https://github.com/openmeterio/openmeter → 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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