What problem does it solve?
It prevents runaway costs in production multi-agent LLM systems by tracking token usage, attributing spend, and enforcing budget limits before overages occur.
Core Features & Use Cases
- Real-time token accounting with attribution: tracks prompt vs completion tokens and aggregates usage by agent and task to identify what’s costly.
- Budget enforcement and reservation: applies per-agent and global (daily/weekly) token ceilings, blocking requests that would exceed limits.
- Token optimization and proactive cost controls: compresses context, summarizes older history, and selects more cost-effective model tiers based on available budget.
- Budget dashboards and alerting rules: generates current burn status, cost estimates, and threshold-based alerts (spikes, runaway detection, forecast notifications).
- Use case: in an agent workflow that loops through research and code steps, allocate daily budgets per agent, detect a spike from one agent-task pair, compress context automatically, and switch to cheaper models for low-difficulty steps.
Quick Start
Ask your AI system to instrument token tracking, define per-agent and daily budgets, enable alerts at 80% spend, and run a budget-optimized request flow that compresses context when usage is near the limit.
