ModelMux - Vertex AI to OpenAI Proxy (Rust)

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ModelMux is a production-ready, async Rust proxy that acts as a drop-in replacement for the OpenAI API. It translates OpenAI-compatible requests into Google Vertex AI (Anthropic Claude) calls while preserving streaming, tool/function calling, and error semantics. Designed for performance, safety, and clean architecture, ModelMux is ideal for teams standardizing on OpenAI APIs while running on Vertex AI infrastructure.

🎉 New in v1.0.0: Production Ready

ModelMux v1.0.0 adds service management and Linux packaging:

🍺 Brew services: brew services start modelmux — run as a background service (macOS)
🐧 systemd daemon: Linux system and user service units — see packaging/systemd/
📦 .deb packages: Install on Ubuntu/Debian with dpkg -i modelmux_*.deb
🏗️ Multi-layered configuration: CLI args > env vars > user config > system config > defaults
📝 TOML configuration: Human-readable config files; modelmux config init for quick setup

Quick setup: modelmux config init creates your configuration interactively!

"The internet is like a vast electronic library. But someone has scattered all the books on the floor." — Lao Tzu

What is ModelMux?

ModelMux is a high-performance Rust proxy server that seamlessly converts OpenAI-compatible API requests to Vertex AI (Anthropic Claude) format. Built with Rust Edition 2024 for maximum performance and type safety.

🔁 Drop-in OpenAI replacement — zero client changes
⚡ High performance — async Rust with Tokio
🧠 Full tool/function calling support
📡 Streaming (SSE) compatible
🛡 Strong typing & clean architecture
☁️ Built for Vertex AI (Claude)

Use ModelMux to standardize on the OpenAI API while keeping full control over your AI backend.

Stop rewriting API glue code. Start muxing.

Features

🔌 OpenAI-Compatible API: Drop-in replacement for OpenAI API endpoints
🛠️ Tool/Function Calling: Full support for OpenAI tool calling format
📡 Smart Streaming: Server-Sent Events (SSE) with intelligent client detection
🎯 Client Detection: Automatically adjusts behavior for IDEs, browsers, and CLI tools
⚡ High Performance: Async Rust with Tokio for maximum concurrency
🔒 Type Safety: Leverages Rust's type system for compile-time guarantees
🔄 Retry Logic: Configurable retry mechanisms with exponential backoff
📊 Observability: Structured logging and health monitoring
🧩 Clean Architecture: SOLID principles with modular design
⚙️ Professional Config: Multi-layered configuration with CLI management tools

Installation

Homebrew (macOS)

brew tap yarenty/tap
brew install modelmux

Cargo

cargo install modelmux

From Source

git clone https://github.com/yarenty/modelmux
cd modelmux
cargo build --release
./target/release/modelmux

As a Library

Add to your Cargo.toml:

[dependencies]
modelmux = "1.0"

Quick Start

1. Set up configuration

Use the interactive configuration wizard:

modelmux config init

Or create a configuration file manually. On macOS: ~/Library/Application Support/com.SkyCorp.modelmux/config.toml (or ~/.config/modelmux/config.toml on Linux):

[server]
port = 3000
log_level = "info"
enable_retries = true
max_retry_attempts = 3

[auth]
# Path to Google Cloud service account JSON file
service_account_file = "~/Library/Application Support/com.SkyCorp.modelmux/service-account.json"
# Or inline JSON for containers:
# service_account_json = '{"type": "service_account", ...}'

[vertex]
# Vertex AI provider - set these OR use env vars (.env supported)
project = "{your-project}"
region = "{your-region}"
location = "{your-region}"
publisher = "anthropic"
model = "{your-model}"

[streaming]
mode = "auto"  # auto, never, standard, buffered, always
buffer_size = 65536
chunk_timeout_ms = 5000

Note: You can also use a .env file or environment variables (VERTEX_PROJECT, VERTEX_REGION, etc.) for provider config.

2. Run ModelMux

modelmux
# or
cargo run --release

Homebrew (macOS): Run as a background service with brew services start modelmux (start/stop/restart like PostgreSQL or Redis).

Linux (systemd): Run as a daemon with systemd — see packaging/systemd/README.md.

3. Validate and start

# Validate your configuration
modelmux config validate

# Start the server
modelmux

4. Send OpenAI-compatible requests

curl -X POST http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "messages": [{"role": "user", "content": "Hello, ModelMux!"}],
    "stream": false
  }'

That's it! Your OpenAI code now talks to Vertex AI.

Configuration

ModelMux uses a modern, professional configuration system with multiple sources:

Configuration File (Recommended)

Create ~/.config/modelmux/config.toml:

# ModelMux Configuration
# Platform-specific locations:
#   Linux: ~/.config/modelmux/config.toml
#   macOS: ~/Library/Application Support/modelmux/config.toml  
#   Windows: %APPDATA%/modelmux/config.toml

[server]
port = 3000
log_level = "info"        # trace, debug, info, warn, error
enable_retries = true
max_retry_attempts = 3

[auth]
# Recommended: Use service account file
service_account_file = "~/.config/modelmux/service-account.json"

# Alternative: Inline JSON (for containers)
# service_account_json = '{"type": "service_account", ...}'

[vertex]
# Vertex AI provider (config file OR env vars / .env)
project = "{your-project}"
region = "{your-region}"
location = "{your-region}"
publisher = "{your publisher}}"
model = "{your-model}"

[streaming]
mode = "auto"             # auto, never, standard, buffered, always
buffer_size = 65536
chunk_timeout_ms = 5000

CLI Configuration Commands

# Interactive setup wizard
modelmux config init

# Display current configuration  
modelmux config show

# Validate configuration
modelmux config validate

# Edit configuration file
modelmux config edit

Environment Variables and .env

Supported for backward compatibility. Place a .env file in your project directory or current working directory:

# Provider configuration
LLM_PROVIDER=vertex
VERTEX_PROJECT=my-gcp-project
VERTEX_REGION=europe-west1
VERTEX_LOCATION=europe-west1
VERTEX_PUBLISHER=anthropic
VERTEX_MODEL_ID=claude-3-5-sonnet@20241022

# Configuration overrides (use MODELMUX_ prefix)
MODELMUX_SERVER_PORT=3000
MODELMUX_SERVER_LOG_LEVEL=info
MODELMUX_AUTH_SERVICE_ACCOUNT_FILE=/path/to/key.json

The .env file is loaded automatically when modelmux starts (from the current working directory).

Streaming Modes

ModelMux intelligently adapts its streaming behavior based on the client:

auto (default): Automatically detects client capabilities and chooses the best streaming mode
- Forces non-streaming for IDEs (RustRover, IntelliJ, VS Code) and CLI tools (goose, curl)
- Uses buffered streaming for web browsers
- Uses standard streaming for API clients
non-streaming: Forces complete JSON responses for all clients
standard: Word-by-word streaming as received from Vertex AI
buffered: Accumulates chunks for better client compatibility

Client Detection

ModelMux automatically detects problematic clients:

Non-streaming clients:

JetBrains IDEs (RustRover, IntelliJ, PyCharm, etc.)
CLI tools (goose, curl, wget, httpie)
API testing tools (Postman, Insomnia, Thunder Client)
Clients that don't accept text/event-stream

Buffered streaming clients:

Web browsers (Chrome, Firefox, Safari, Edge)
VS Code and similar editors

API Endpoints

Chat Completions

POST /v1/chat/completions

OpenAI-compatible chat completions with full tool calling support.

Models

GET /v1/models

List available models in OpenAI format.

Health Check

GET /health

Service health and metrics endpoint.

Library Usage

Use ModelMux programmatically in your Rust applications:

use modelmux::{Config, create_app};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Load configuration from environment
    let config = Config::from_env()?;
    
    // Create the application
    let app = create_app(config).await?;
    
    // Start server
    let listener = tokio::net::TcpListener::bind("0.0.0.0:3000").await?;
    axum::serve(listener, app).await?;
    
    Ok(())
}

Architecture

OpenAI Client ──► ModelMux ──► Vertex AI (Claude)
     │               │              │
     │               │              │
  OpenAI API ──► Translation ──► Anthropic API
  Format         Layer         Format

Core Components:

config - Configuration management and environment handling
auth - Google Cloud authentication for Vertex AI
server - HTTP server with intelligent routing
converter - Bidirectional format translation
error - Comprehensive error types and handling

Project Structure

modelmux/
├── Cargo.toml              # Dependencies and metadata
├── README.md               # This file
├── LICENSE-MIT             # MIT license
├── LICENSE-APACHE          # Apache 2.0 license
├── docs/
└── src/
    ├── main.rs             # Application entry point
    ├── lib.rs              # Library interface
    ├── config.rs           # Configuration management
    ├── auth.rs             # Google Cloud authentication
    ├── error.rs            # Error types
    ├── server.rs           # HTTP server and routes
    └── converter/          # Format conversion modules
        ├── mod.rs
        ├── openai_to_anthropic.rs
        └── anthropic_to_openai.rs

Examples

Tool/Function Calling

curl -X POST http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "messages": [
      {"role": "user", "content": "List files in the current directory"}
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "list_directory",
          "description": "List files in a directory",
          "parameters": {
            "type": "object",
            "properties": {
              "path": {"type": "string"}
            },
            "required": ["path"]
          }
        }
      }
    ]
  }'

Streaming Response

curl -X POST http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -d '{
    "model": "claude-sonnet-4",
    "messages": [{"role": "user", "content": "Write a haiku about Rust"}],
    "stream": true
  }'

Performance

ModelMux is built for production workloads:

Zero-copy JSON parsing where possible
Async/await throughout for maximum concurrency
Connection pooling for upstream requests
Intelligent buffering for streaming responses
Memory efficient request/response handling

Comparison with Node.js Version

Feature	Node.js	ModelMux (Rust)
Performance	Good	Excellent
Memory Usage	Higher	Lower
Type Safety	Runtime	Compile-time
Error Handling	Try/catch	Result types
Concurrency	Event loop	Async/await
Startup Time	Fast	Very Fast
Binary Size	Large	Small

Observability

Health Endpoint

curl http://localhost:3000/health

Returns service metrics:

{
  "status": "ok",
  "metrics": {
    "total_requests": 1337,
    "successful_requests": 1300,
    "failed_requests": 37,
    "quota_errors": 5,
    "retry_attempts": 42
  }
}

Logging

Configure log levels via environment:

export LOG_LEVEL=debug
export RUST_LOG=modelmux=trace

License

Licensed under either of

Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contributing

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

Development

git clone https://github.com/yarenty/modelmux
cd modelmux
cargo test
cargo run

Roadmap

See ROADMAP.md for detailed future plans.

✅ Completed in v1.0.0:

✅ Brew services and systemd daemon support
✅ .deb packages for Ubuntu/Debian (amd64, arm64)
✅ Professional configuration system with TOML files
✅ CLI configuration management (modelmux config init/show/edit)

Near term:

Docker container images
Enhanced metrics and monitoring (Prometheus, OpenTelemetry)

Future:

Multiple provider support (OpenAI, Anthropic, Cohere, etc.)
Intelligent request routing and load balancing
Request/response caching layer
Web UI for configuration and monitoring
Advanced analytics and usage insights

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