cost-tracker

You can track token usage and costs across multiple AI agent sessions by recording input and output token counts for every LLM invocation. The cost-tracker automates this monitoring process per agent session, aggregating spend by task and time period for accurate cost allocation.

Yes, you can automatically route tasks to different model tiers like Haiku, Sonnet, or Opus to optimize spend. The dynamic routing evaluates task complexity and assigns the appropriate model tier, ensuring you do not overspend on high-cost models for simple operations.

The best way to generate cost summaries for investor updates is to aggregate historical cost data by agent session and time period. This process produces per-agent and aggregate cost reports, enabling accurate budgeting reviews and spend forecasting for stakeholders.

To enforce daily and weekly budget limits for multi-agent startup harness workflows, you apply per-task budget ceilings with automated 80% warnings and 100% hard stops. This spend oversight prevents budget overruns across daily and weekly periods.

Cost tracking for AI agents supports historical spend trend analysis and budget forecasting by storing historical cost data. This stored token usage and spend data enables you to analyze cost trends over time and accurately forecast future budget requirements.