ToolExperimental

loopgain

Name: loopgain
Rating: 3.0 (124 reviews)
Author: loopgain-ai

by loopgain-ai

Real-time convergence control and rollback for agent loops

Python

Updated Aug 17, 2026

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What It Does

Stops runaway agent loops by detecting real-time convergence (loop-gain Aβ) and rolling back to the best-so-far state instead of running to a fixed iteration cap. Computes loop-gain bands and divergence signals to trigger early-stopping or rollback, with adapters for LangGraph, CrewAI, AutoGen, LangChain, OpenAI Agents, and Claude SDK. Exposes a raw API so you can plug cost-control into custom agent stacks and production pipelines. For guidance on how to design resilient development workflows, consider the Evaluation-Driven Development (EDDOps) pattern and the Model Context Protocol (MCP).

Test in the Playground

Validate against scenarios

Why It Matters

As agents delegate and iterate autonomously, resource waste and silent degradation become common — fixed iteration caps either overspend or miss regressions. Loopgain gives teams a signal-driven safety valve that stops loops when they truly converge and undoes steps that reduce quality, making agent-to-agent interactions auditable and more cost-effective. Treating convergence and rollback as first-class controls helps establish objective agent track records and practical trust signals for continuous evaluation.

Target Use Cases

Best for teams building multi-agent workflows who need automated early-stopping, rollback, and cost control across LangChain/CrewAI/AutoGen/LangGraph stacks. See the Pattern for consensus-based and market-aware coordination to complement these controls and the MCP Protocol for standardizing cross-agent context handling.

Use Cases

Stop long-running agent loops when output quality has converged to save compute and cost
Automatically rollback to the best-so-far agent state when downstream steps degrade results
Add convergence signals and cost-control hooks to LangChain/AutoGen/CrewAI/ LangGraph pipelines for production monitoring