tokenspeed-smg 1.8.0.post20260728

High-performance Rust-based inference gateway for large-scale LLM deployments

Shepherd Model Gateway

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.

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.

• Development Setup
• Code Style