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Shepherd Model Gateway

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Engine-agnostic, high-performance model-routing gateway for large-scale LLM deployments. Centralizes worker lifecycle management, balances traffic across HTTP/gRPC/OpenAI-compatible backends, and provides enterprise-ready control over history storage, MCP tooling, and privacy-sensitive workflows.

SMG Architecture

Why SMG?

🚀 Maximize GPU Utilization Cache-aware routing understands your inference engine's KV cache state—whether vLLM, TensorRT-LLM, TokenSpeed, or SGLang—to reuse prefixes and reduce redundant computation.
🔌 One API, Any Backend Route to self-hosted models (vLLM, TensorRT-LLM, TokenSpeed, SGLang) or cloud providers (OpenAI, Anthropic, Gemini, Bedrock, and more) through a single unified endpoint.
⚡ Built for Speed Native Rust with gRPC pipelines, sub-millisecond routing decisions, and zero-copy tokenization. Circuit breakers and automatic failover keep things running.
🔒 Enterprise Control Multi-tenant rate limiting with OIDC, WebAssembly plugins for custom logic, and a privacy boundary that keeps conversation history within your infrastructure.
📊 Full Observability 40+ Prometheus metrics, OpenTelemetry tracing, and structured JSON logs with request correlation—know exactly what's happening at every layer.

API Coverage: OpenAI Chat/Completions/Embeddings, Responses API for agents, Anthropic Messages, and MCP tool execution.

Quick Start

Install — pick your preferred method:

# Docker
docker pull lightseekorg/smg:latest

# Python
pip install smg

# Rust
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

Self-Hosted Cloud Providers
vLLM OpenAI
TensorRT-LLM Anthropic
TokenSpeed Google Gemini
SGLang AWS Bedrock
Ollama Azure OpenAI
Any OpenAI-compatible server Any OpenAI-compatible provider

Features

Feature Description
8 Routing Policies cache_aware, round_robin, power_of_two, consistent_hashing, prefix_hash, manual, random, bucket
gRPC Pipeline Native gRPC with streaming, reasoning extraction, and tool call parsing
MCP Integration Connect external tool servers via Model Context Protocol
High Availability Mesh networking with SWIM protocol for multi-node deployments
Chat History Pluggable storage: PostgreSQL, Oracle, Redis, or in-memory
WASM Plugins Extend with custom WebAssembly logic
Resilience Circuit breakers, retries with backoff, rate limiting

Documentation

Getting Started Installation and first steps
Architecture How SMG works
Configuration CLI reference and options
API Reference OpenAI-compatible endpoints
Kubernetes Setup In-cluster discovery and production setup

Contributing

We welcome contributions! See Contributing Guide for details.

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