smg

Shepherd Model Gateway

Engine-agnostic, high-performance model-routing gateway for large-scale LLM deployments. SMG centralizes worker lifecycle management, balances traffic across self-hosted engines and cloud providers, and gives you enterprise-grade control over multi-tenancy, chat-history storage, MCP tooling, and observability — behind one unified endpoint.

Why SMG?

🚀 Maximize GPU Utilization	Cache-aware routing tracks each worker's KV-cache state in radix trees to reuse prefixes across SGLang, vLLM, TensorRT-LLM, TokenSpeed, and MLX — with load modeling that accounts for queued token work and KV pressure.
🔌 One API, Any Backend	Route to self-hosted engines over HTTP or gRPC, or to OpenAI, Anthropic, Gemini, and xAI — plus any OpenAI-compatible endpoint — through a single unified gateway.
⚡ Built for Speed	Native Rust with streaming gRPC pipelines, cached tokenization with zero-copy cache hits, prefill/decode disaggregation (including a separate encode stage for vision), and DP-aware routing for data-parallel engines.
🔒 Enterprise Control	Priority admission scheduling with preemption and per-tenant controls, API-key auth with OIDC on the control plane, WebAssembly plugins for custom logic, and chat history that never leaves your infrastructure.
📊 Full Observability	90+ Prometheus metrics, OpenTelemetry tracing with W3C trace context propagated into the engines over both HTTP and gRPC, and structured JSON logs with request correlation.

API Coverage: OpenAI Chat Completions, Completions, Embeddings, Rerank, and Classify; Responses and Conversations APIs for agents; Anthropic Messages; Gemini Interactions; Realtime over WebSocket and WebRTC; audio transcription; tokenize/detokenize; and MCP tool execution with approval policies in the Responses and Messages APIs.

Quick Start

Install — pick your preferred method:

# Docker
docker pull lightseekorg/smg:latest

# Kubernetes (Helm)
helm install smg oci://ghcr.io/smg-project/charts/smg

# Python
pip install smg

# Rust (needs protoc)
cargo install smg

Run — point SMG at your inference workers:

# Single worker
smg launch --worker-urls http://localhost:8000

# Multiple workers with cache-aware routing
smg launch --worker-urls http://gpu1:8000 http://gpu2:8000 --policy cache_aware

# With high availability mesh
smg launch --worker-urls http://gpu1:8000 --enable-mesh \
  --mesh-advertise-host 10.0.0.1 --mesh-peer-urls 10.0.0.2:39527

Use — send requests to the gateway:

curl http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "llama3", "messages": [{"role": "user", "content": "Hello!"}]}'

That's it. SMG is now load-balancing requests across your workers.

Supported Backends