llm-gateway
llm-gateway is an engineering AI skill with a core value of Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking. It
helps developers solve real-world problems in the engineering domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.
Quick Facts
mkdir -p ./skills/llm-gateway && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/llm-gateway/SKILL.md -o ./skills/llm-gateway/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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.
# 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_debugLiteLLM Configuration
# 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:
model: openai/gpt-4o-mini # fallback to cheaper model
api_key: os.environ/OPENAI_API_KEY
router_settings:
routing_strategy: least-busy # or: latency-based, simple-shuffle
num_retries: 3
retry_after: 5
allowed_fails: 2
cooldown_time: 60
# Fallback configuration
fallbacks:
- gpt-4o: [claude-sonnet-4-6]
- claude-sonnet-4-6: [gpt-4o]
litellm_settings:
# Semantic caching
cache: true
cache_params:
type: redis
host: redis
port: 6379
similarity_threshold: 0.90 # cache if >90% semantic similarity
# Logging
success_callback: ["langfuse"]
failure_callback: ["langfuse"]
langfuse_public_key: os.environ/LANGFUSE_PUBLIC_KEY
langfuse_secret_key: os.environ/LANGFUSE_SECRET_KEY
general_settings:
master_key: os.environ/LITELLM_MASTER_KEY
database_url: postgresql://litellm:password@postgres:5432/litellm
store_model_in_db: trueDocker Compose: Full Gateway Stack
services:
litellm:
image: ghcr.io/berriai/litellm:main-latest
command: ["--config", "/app/config.yaml", "--port", "4000"]
volumes:
- ./litellm-config.yaml:/app/config.yaml
ports:
- "4000:4000"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
- LITELLM_MASTER_KEY=${LITELLM_MASTER_KEY}
- DATABASE_URL=postgresql://litellm:password@postgres:5432/litellm
depends_on:
postgres:
condition: service_healthy
redis:
condition: service_started
restart: unless-stopped
postgres:
image: postgres:16-alpine
environment:
POSTGRES_DB: litellm
POSTGRES_USER: litellm
POSTGRES_PASSWORD: password
volumes:
- postgres-data:/var/lib/postgresql/dat🎯 Best For
- Claude users
- AI users
💡 Use Cases
- Using llm-gateway in daily workflow
- Automating repetitive engineering tasks
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply llm-gateway to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
How do I install llm-gateway?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/llm-gateway/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.