llmio

🥈Silver

LLMIO is a Go-based LLM load balancer gateway that provides a unified REST API, weight-based scheduling, logging, and a modern management interface. It integrates OpenAI, Anthropic, and Gemini models into a single service for LLM clients like Claude Code, Codex, Gemini CLI, and Cherry Studio.

226170Updated 1mo ago

Intermediate30min to implementautomation

Saves ~60 min per use

Quick InstallView Source

git clone https://github.com/atopos31/llmio.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/atopos31/llmio

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Design a load-balancing architecture for an LLM API gateway for [COMPANY], a [INDUSTRY] company. The system should handle [DATA] requests per second and ensure high availability, low latency, and scalability. Include details on traffic distribution, failover mechanisms, and monitoring.

Example Output

# LLM API Gateway Load-Balancing Architecture

## Overview
For [COMPANY], a leading [INDUSTRY] firm, we propose a robust load-balancing architecture for their LLM API gateway to handle [DATA] requests per second. The architecture ensures high availability, low latency, and scalability.

## Key Components
- **Traffic Distribution**: Utilize a combination of round-robin and least connections algorithms to distribute traffic evenly across multiple LLM instances.
- **Failover Mechanisms**: Implement health checks and automatic failover to redirect traffic to healthy instances in case of failures.
- **Monitoring**: Deploy real-time monitoring tools to track performance metrics, such as response times, error rates, and resource utilization.

## Implementation Steps
1. **Assess Current Infrastructure**: Evaluate existing servers, network bandwidth, and LLM instances to identify bottlenecks.
2. **Deploy Load Balancers**: Set up load balancers in a redundant configuration to ensure continuous operation.
3. **Configure Health Checks**: Implement health checks to monitor the status of LLM instances and redirect traffic accordingly.
4. **Monitor and Optimize**: Continuously monitor performance and make adjustments to optimize traffic distribution and resource utilization.