LeanTokens is a control layer that optimizes token usage, cost, and latency for AI agent stacks while maintaining quality standards. It sits alongside existing model routers and execution tools, applying per-run policy updates, parallel execution, and verifier-guarded optimization to every agent workflow. The platform enables teams to set cost caps, latency targets, and accuracy floors, with each run generating reproducible, diffable reports that explain decision-making and improve subsequent runs.
Funding
Funding not disclosed
Founders
Product
Problem
Agent stacks are growing increasingly complex, with multiple model calls, context windows, and verifier retries driving up token consumption and operational costs. Teams lack a unified way to control spending, latency, and output quality across their agent pipelines, leading to unpredictable expenses and inefficient execution.
Solution
LeanTokens is a command center that adds a control layer on top of existing agent stacks, optimizing token usage and cost without requiring changes to the underlying tools. It runs independent steps in parallel, collapses long chains where possible, and bounds verifier retries to minimize waste. The platform applies per-run policy updates that jointly optimize quality, tokens, and latency, while enforcing user-defined accuracy floors so cost and speed gains never compromise output integrity. Every run is fully logged, reproducible, and diffable, with reports that explain each decision to enable continuous learning and improvement across runs.
Target Audience
Primary users are engineering and ML teams running production AI agent stacks who need to control token spend, latency, and output quality across complex multi-model workflows.
Features
- Per-run cost caps, latency targets, minimum accuracy thresholds, and maximum retry limits for granular control
- Parallel execution of independent steps and chain collapsing to reduce token consumption and wall-clock time
- Quality gate enforcement that verifies outputs against regression checks before shipping optimizations
- Live orchestration logs that show config, verification status, and learned token savings per model call
- Learning layer that tracks model performance and automatically applies token-saving strategies to future runs
- Harness integrations designed to work alongside existing model routers and agent execution tools