Your Free AI Token Allowance Is a Budget, Not a Gift

说明与用途

通过添加轻量级计量脚本、状态文件以避免上下文重新发送以及重试避免规则，团队能够预测并控制令牌使用，完成两周冲刺并保持在 MonkeyCode 免费层。

问题

Teams running free AI models often exhaust token budgets mid‑sprint because they do not track or manage token consumption, leading to wasted requests and stalled work.

结果

Token consumption dropped by ~30 % after state file and retry rules; teams could finish two‑week sprints on the free tier without running out of tokens.

工作流

• 1. Wrap each agent task with a meter script that estimates prompt and completion tokens.
• 2. Log task metadata (label, start time, elapsed, exit code, estimated tokens) to a JSONL audit file.
• 3. After each task, write a concise STATE.md (≤5 lines) summarizing changes, failures, and next steps.
• 4. Before running a new task, have the agent read STATE.md to avoid re‑reading full context.
• 5. Apply a retry rule: if a command fails twice with the same error, stop retrying and change the approach.
• 6. Use the token estimator output to forecast weekly burn and adjust task selection accordingly.

证据

The state file from the previous section fixed that leak within a couple of days, and the daily totals dropped accordingly.

来源

Two failures in that period, an unstubbed network call and a missing fixture, each produced several repeated attempts... a one-line rule stopped that category: never repeat a command that produced the same failure twice.

来源