Show HN: LoopGain – Cut agent API spend by measuring when loops stop improving

Category: library

Tags: ai-agents, cost-optimization, control-loop, python-library, agent-observability

Score: 7.5/10 (Innovation: 8, Technical: 8, Documentation: 7, Utility: 7)

LoopGain is an open-source library that replaces fixed `max_iterations` in AI agent loops with a control-theoretic stop-and-rollback policy, measuring convergence in real time to slash API costs while preserving output quality. Its grounding in the Barkhausen criterion and demonstrated 92.8% cost reduction in benchmarks make it a novel, practical tool for optimizing iterative AI workflows.

Target audience: backend devs, data engineers, ml engineers

Repository: https://github.com/loopgain-ai/loopgain · Python · Apache-2.0 · 1 stars

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