Show HN: I applied Lyapunov stability theory to detect when LLM agents spiral (github.com)

A developer has introduced the State-harness library, which leverages Lyapunov stability theory to monitor and mitigate runaway behaviors in large language model (LLM) agents. The library features the GrowthRatioGuard, which tracks token usage against a predefined baseline, identifying when agents enter a "context accumulation spiral." This early detection mechanism prevents excessive costs associated with LLM operations by providing failure diagnostics without incurring additional LLM calls. For example, when an agent misbehaves, the system classifies the failure, explaining the cause—like excessive token growth—and suggesting corrective actions, all of which significantly optimize budget management. This innovation is significant for the AI/ML community as it addresses a common pain point: the unpredictability and cost spikes associated with LLM misbehavior during production tasks. With the ability to pinpoint failure patterns and provide actionable insights, State-harness can enhance the reliability and efficiency of LLM applications in real-world scenarios, including those running multiple agent tasks daily. By combining swift, microsecond-level enforcement mechanisms implemented in Rust with a user-friendly Python SDK, the library is poised to improve LLM management across a spectrum of industries, ensuring teams can maintain operational budgets while effectively diagnosing execution failures.