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:
```bash
# 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:
```bash
# 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:
```bash
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 Engines** | vLLM · SGLang · TokenSpeed · TensorRT-LLM · MLX (Apple Silicon) · any OpenAI-compatible server (e.g. Ollama) |
| **Cloud Providers** | OpenAI · Anthropic · Google Gemini · xAI · OCI Generative AI · AWS Bedrock · Azure OpenAI · any OpenAI-compatible provider (Groq, Together, …) |
## Features
| Feature | Description |
|---------|-------------|
| **[10 Routing Policies](https://lightseek.org/smg/concepts/routing/load-balancing)** | cache_aware, least_load, power_of_two, consistent_hashing, prefix_hash, bucket, round_robin, random, manual, passthrough |
| **[gRPC Pipeline](https://lightseek.org/smg/concepts/architecture/grpc-pipeline)** | Native streaming gRPC to the engines with prefill/decode and encode disaggregation and DP-aware routing |
| **[Kubernetes Discovery](https://lightseek.org/smg/getting-started/service-discovery)** | Native pod watchers with label selectors, per-role prefill/decode/encode selectors, and router peer discovery |
| **[Model Parsers](https://lightseek.org/smg/getting-started/tokenization-and-parsing)** | 21 tool-call parsers and 16 reasoning parsers with automatic model detection — DeepSeek, Qwen, Kimi, GLM, Llama, Mistral, Command, Nemotron, and more |
| **[MCP Integration](https://lightseek.org/smg/concepts/extensibility/mcp)** | Tool discovery and execution over stdio, SSE, and streamable HTTP, with approval policies and audit logging |
| **[High Availability](https://lightseek.org/smg/concepts/architecture/high-availability)** | Mesh networking with SWIM gossip and CRDT-replicated state for multi-node deployments |
| **[Chat History](https://lightseek.org/smg/concepts/data/chat-history)** | Pluggable storage with schema migrations: PostgreSQL, Oracle, Redis, or in-memory |
| **[WASM Plugins](https://lightseek.org/smg/concepts/extensibility/wasm-plugins)** | Extend request and response handling with custom WebAssembly middleware |
| **[Resilience](https://lightseek.org/smg/concepts/reliability/index)** | Circuit breakers, retries with backoff and jitter, rate limiting, and priority admission scheduling |
## Documentation
Full documentation lives at **[lightseek.org/smg](https://lightseek.org/smg/)**.
| | |
|:--|:--|
| [Getting Started](https://lightseek.org/smg/getting-started) | Installation and first steps |
| [Architecture](https://lightseek.org/smg/concepts/architecture/overview) | How SMG works |
| [Configuration](https://lightseek.org/smg/reference/configuration) | CLI reference and options |
| [API Reference](https://lightseek.org/smg/reference/api/openai) | OpenAI-compatible endpoints |
| [Kubernetes Setup](https://lightseek.org/smg/getting-started/service-discovery) | In-cluster discovery and production setup |
## Contributing
We welcome contributions! See the [Contributing Guide](https://lightseek.org/smg/contributing) for details.
- [Development Setup](https://lightseek.org/smg/contributing/development)
- [Code Style](https://lightseek.org/smg/contributing/code-style)
