Can I distinguish between rate limits and model errors?

Yes, the skill automatically classifies errors into categories like transient (rate limits, timeouts) and permanent (invalid requests, context length exceeded).

What is fallback tracking?

It monitors when a system switches from a primary model to a backup model due to failures, tracking the trigger reason, the model chain, and the impact on output quality.

Does it support popular frameworks like LangChain?

Yes, it provides implementation patterns for integrating with LangChain callbacks and Tenacity to ensure robust retry and error handling.

How does this skill help reduce LLM costs?

It identifies permanent errors early to prevent expensive, useless retries and tracks 'wasted' tokens from failed requests, allowing you to optimize your resource allocation.

Error and Retry Tracking

Name: Error and Retry Tracking
Author: nexus-labs-automation

bynexus-labs-automation

Analytics & Monitoring

Instruments and monitors AI agent error handling, retry logic, and fallback strategies to improve system reliability and observability.

About

This skill provides a comprehensive framework for tracking the lifecycle of failures within AI agent workflows. It enables developers to distinguish between transient and permanent errors, monitor retry success rates, visualize fallback paths, and manage rate limits effectively. By providing deep insights into failure patterns and recovery behaviors, it helps optimize LLM performance, reduce wasted token costs, and implement resilient circuit breaker patterns for more stable production deployments.

Key Features

Granular tracking of retry attempts, strategies, and delays
Circuit breaker pattern implementation for cascading failures
Classification of transient vs. permanent LLM errors
Rate limit monitoring and preemptive wait handling
Observability for model fallback chains and quality impact
0 GitHub stars

Use Cases

Optimizing retry strategies to balance response latency and token costs
Debugging agentic workflows that stall or fail in production environments
Monitoring provider health and rate limit exhaustion across multiple LLM models

About

Key Features

Granular tracking of retry attempts, strategies, and delays
Circuit breaker pattern implementation for cascading failures
Classification of transient vs. permanent LLM errors
Rate limit monitoring and preemptive wait handling
Observability for model fallback chains and quality impact
0 GitHub stars

Use Cases

Optimizing retry strategies to balance response latency and token costs
Debugging agentic workflows that stall or fail in production environments
Monitoring provider health and rate limit exhaustion across multiple LLM models