How does it help with API costs?

It includes specialized tools for precise token counting, cost estimation per model, and implementation patterns for prompt caching to reduce redundant expenses.

What does the LLM App Architecture skill do?

It provides standardized patterns and code snippets for building robust, production-ready applications that interface with LLM APIs like Claude and OpenAI.

Does it handle API rate limits?

Yes, it implements a decorator-based retry mechanism featuring exponential backoff and jitter to safely manage rate limits and transient connection errors.

Can I use this for real-time applications?

Absolutely. It includes specific patterns for streaming completions to web frameworks like FastAPI, enabling real-time token delivery to front-end interfaces.

Which models does this skill support?

While the examples focus on Anthropic's Claude, the architectural patterns such as async handling, retries, and streaming are applicable to any major LLM provider.

LLM Application Architecture

Name: LLM Application Architecture
Author: ricardoroche

byricardoroche

Data Science & ML

Implements robust patterns for LLM-powered applications including async calls, streaming, token management, and resilient error handling.

About

This skill provides a comprehensive framework for building production-grade applications integrated with Large Language Models like Claude and GPT. It automates the implementation of essential architectural patterns such as asynchronous request handling to prevent blocking, real-time response streaming for better UX, and sophisticated retry logic with exponential backoff for handling API rate limits. Additionally, it offers tools for precise token counting, cost estimation, and response caching, ensuring that your AI-driven features are not only reliable and performant but also cost-efficient and observable.

Key Features

Asynchronous LLM client implementation with comprehensive error handling
Streaming response patterns for real-time UI/UX integration
Efficient batch processing and prompt caching optimizations
0 GitHub stars
Token management tools for usage tracking and cost estimation
Resilient retry logic with exponential backoff and jitter

Use Cases

Building high-performance AI chatbots with streaming capabilities
Creating resilient backend services that gracefully handle model API downtime
Developing cost-aware LLM agents that track token consumption in real-time

About

Key Features

Asynchronous LLM client implementation with comprehensive error handling
Streaming response patterns for real-time UI/UX integration
Efficient batch processing and prompt caching optimizations
0 GitHub stars
Token management tools for usage tracking and cost estimation
Resilient retry logic with exponential backoff and jitter

Use Cases

Building high-performance AI chatbots with streaming capabilities
Creating resilient backend services that gracefully handle model API downtime
Developing cost-aware LLM agents that track token consumption in real-time