# llm-gateway

This skill walks you through deploying one API gateway in front of several LLMs: load balancing, rate limits, keys, caching and spend tracking.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 333 installs · ★ 1177
- Author: devops-skills
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/HEAD/infrastructure/networking/llm-gateway/SKILL.md
- Updated: 2026-05-22
- HTML: https://affpapa.org/en/nevibecode/skills/llm-gateway

## What it does

The skill covers standing up a single endpoint for LLM requests. Anthropic, Anthropic, vLLM and Ollama can sit behind it, while routing, retries and fallback to a backup model come from the config. The examples use LiteLLM Proxy, which speaks the Anthropic API, plus Nginx and Traefik patterns.

Running LiteLLM takes Docker and Docker Compose, a PostgreSQL or SQLite database for state, and provider API keys. Redis is optional and handles caching and rate limiting. Semantic caching in the sample config kicks in above the similarity_threshold, and virtual keys carry a budget plus rpm and tpm limits per team.

Slow streaming usually traces back to proxy_buffering, which the Nginx config turns off. A ConnectionRefusedError means the backend is unreachable, so check api_base and the service health. Note that the source text is truncated, so some sections may be missing from this description.

## Requirements
- Works with: Docker

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-gateway -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/llm-gateway/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/llm-gateway/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-gateway -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/llm-gateway/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-gateway -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/llm-gateway/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install BagelHole/DevOps-Security-Agent-Skills llm-gateway
```

2. **Manually: put the files in place**
- File: `.github/skills/llm-gateway/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-gateway -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/BagelHole/DevOps-Security-Agent-Skills --path infrastructure/networking/llm-gateway
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/llm-gateway/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-gateway -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/HEAD/infrastructure/networking/llm-gateway/SKILL.md

## When to use it
- route Anthropic, Anthropic and vLLM requests through one endpoint
- issue a team virtual key with a monthly budget cap
- fail over to a backup model when a provider goes down
- turn on semantic caching to cut repeat API spend
- check spend by model and list active keys

## Example prompts
- Deploy LiteLLM Proxy from the llm-gateway skill and wire up Anthropic and Anthropic
- Generate a virtual key for the backend team with rpm and budget limits
- Set up a fallback from Claude to claude-sonnet-4-6 in the gateway config

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 3f209c854aa3588a.

BEGIN THIRD-PARTY FILE 3f209c854aa3588a
````text
---
name: llm-gateway
description: Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking. Covers LiteLLM Proxy, OpenRouter-compatible setup, and custom Nginx/Traefik patterns.
license: MIT
metadata:
  author: devops-skills
  version: "1.0"
---

# LLM Gateway

A unified API gateway that routes LLM requests across providers and self-hosted models — with rate limiting, cost tracking, caching, and failover.

## When to Use This Skill

Use this skill when:
- Running multiple LLM backends (OpenAI, Anthropic, vLLM, Ollama) behind a single endpoint
- Enforcing per-team or per-user rate limits and spend budgets
- Implementing automatic fallback when a provider is down
- Adding semantic caching to reduce API costs by 20–50%
- Centralizing API key management instead of distributing keys to every app

## Prerequisites

- Docker and Docker Compose
- A PostgreSQL or SQLite database (for LiteLLM state)
- LLM API keys (OpenAI, Anthropic, etc.) or self-hosted vLLM endpoints
- Optional: Redis for caching and rate limiting

## LiteLLM Proxy — Quick Start

LiteLLM is the de facto open-source LLM gateway with OpenAI-compatible API.

```bash
# Run with Docker
docker run -d \
  --name litellm-proxy \
  -p 4000:4000 \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
  -v $(pwd)/litellm-config.yaml:/app/config.yaml \
  ghcr.io/berriai/litellm:main-latest \
  --config /app/config.yaml \
  --detailed_debug
```

## LiteLLM Configuration

```yaml
# litellm-config.yaml
model_list:
  # OpenAI models
  - model_name: gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_key: os.environ/OPENAI_API_KEY
      rpm: 10000
      tpm: 2000000

  - model_name: gpt-4o-mini
    litellm_params:
      model: openai/gpt-4o-mini
      api_key: os.environ/OPENAI_API_KEY

  # Anthropic
  - model_name: claude-sonnet-4-6
    litellm_params:
      model: anthropic/claude-sonnet-4-6
      api_key: os.environ/ANTHROPIC_API_KEY

  # Self-hosted vLLM instances (load balanced)
  - model_name: llama-3.1-8b
    litellm_params:
      model: openai/meta-llama/Llama-3.1-8B-Instruct
      api_base: http://vllm-1:8000/v1
      api_key: fake                    # vLLM key
  - model_name: llama-3.1-8b
    litellm_params:
      model: openai/meta-llama/Llama-3.1-8B-Instruct
      api_base: http://vllm-2:8000/v1  # second replica — auto load balanced
      api_key: fake

  # Fallback: cheap model if primary fails
  - model_name: gpt-4o
    litellm_params:
````
END THIRD-PARTY FILE 3f209c854aa3588a

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