Luthy Chihuahua
Lightweight runtime protection for production AI agents
Chihuahua is Luthy's lightweight runtime protection layer for agent systems. It detects when execution stops making real progress and intervenes before loops, stalls, and wasteful continuation turn into higher spend, rising latency, and unreliable outcomes.
Add Chihuahua on top of your existing Python agent stack without retraining models, changing weights, or rebuilding orchestration.
Free 14-day Observer Mode trial
Luthy Chihuahua
Available NowLightweight runtime protection for detecting non-progress and stopping wasteful agent execution.
Jack Russell
Coming SoonBounded autonomy for regulated and mission-critical agent workflows, with policy enforcement, intervention controls, and auditability.
What Luthy Chihuahua does
Luthy Chihuahua is the simplest way to add runtime protection to production agent systems. It focuses on one critical job: detecting when execution has stopped producing meaningful progress and stepping in before the run becomes expensive, slow, or unreliable.
Detect non-progress
Spot loops, stalls, repetition, and low-signal continuation while a run is still active.
Intervene automatically
Stop or constrain bad runs before they burn more tokens, time, and operator attention.
Fit into your stack easily
Wrap existing Python agent runtimes without retraining models or rebuilding orchestration.
Why teams start with Luthy Chihuahua
Chihuahua is built for teams that want runtime protection they can deploy quickly, understand clearly, and trust in production.
Unlike passive observability tools, Chihuahua is designed to detect non-progress during execution and act before waste compounds.
Fast to add
Add protection without rebuilding your system.
Lightweight by design
Use an external runtime layer with minimal stack disruption.
Focused protection
Catch non-progress before waste compounds.
Production-friendly
Reduce runaway execution without adding heavy operational overhead.
Start in Observer Mode
Luthy Chihuahua is designed to be easy to evaluate in real agent workflows. Start in Observer Mode to see where Luthy Chihuahua would detect non-progress and surface wasteful execution before enabling active intervention.
No forced workflow changes
Try Chihuahua on top of your current stack
See likely intervention points
Identify where runs stop making meaningful progress
Enable protection with confidence
Turn on active runtime protection once the behavior is clear
Easy integration with your existing stack
Add Chihuahua on top of your Python agent runtime without changing orchestration, retraining models, or rebuilding workflows.
Python-native
Works on top of existing Python agent runtimes
Model-agnostic
Compatible with OpenAI, Anthropic, and custom model APIs
Framework-friendly
Integrates with popular orchestration frameworks
Start with lightweight runtime protection
Try Luthy Chihuahua free in Observer Mode, see where it would catch non-progress, and enable active protection when you're ready.