lm-proxy-server 0.2.2

"LM Proxy Server" is OpenAI-compatible http proxy server for inferencing various LLMs capable of working with Google, Anthropic, OpenAI APIs, local PyTorch inference, etc.

LM Proxy Server

LM Proxy Server is an OpenAI-compatible HTTP proxy server for various Large Language Models (LLMs) inference. It provides a unified interface for working with different AI providers through a single API endpoint that follows the OpenAI format. Stream like OpenAI, authenticate with your own API keys, and keep clients unchanged.

✨ Features

• Provider Agnostic: Connect to OpenAI, Anthropic, Google AI, local models, and more using a single API
• Unified Interface: Access all models through the standard OpenAI API format
• Dynamic Routing: Route requests to different LLM providers based on model name patterns
• Stream Support: Full streaming support for real-time responses
• API Key Management: Configurable API key validation and access control
• Easy Configuration: Simple TOML configuration files for setup

🚀 Getting Started

Installation

pip install lm-proxy-server

Quick Start

1. Create a config.toml file:

host = "0.0.0.0"
port = 8000

[connections]
[connections.openai]
api_type = "open_ai"
api_base = "https://api.openai.com/v1/"
api_key = "env:OPENAI_API_KEY"

[connections.anthropic]
api_type = "anthropic"
api_key = "env:ANTHROPIC_API_KEY"

[routing]
"gpt*" = "openai.*"
"claude*" = "anthropic.*"
"*" = "openai.gpt-3.5-turbo"

[groups.default]
api_keys = ["YOUR_API_KEY_HERE"]

2. Start the server:

lm-proxy-server

3. Use it with any OpenAI-compatible client:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY_HERE",
    base_url="http://localhost:8000/v1"
)

completion = client.chat.completions.create(
    model="gpt-5",  # This will be routed to OpenAI based on config
    messages=[{"role": "user", "content": "Hello, world!"}]
)
print(completion.choices[0].message.content)

Or use the same endpoint with Claude models:

completion = client.chat.completions.create(
    model="claude-opus-4-1-20250805",  # This will be routed to Anthropic based on config
    messages=[{"role": "user", "content": "Hello, world!"}]
)

📝 Configuration

LM Proxy Server is configured through a TOML file that specifies connections, routing rules, and access control.

Basic Structure

host = "0.0.0.0"  # Interface to bind to
port = 8000       # Port to listen on
dev_autoreload = false  # Enable for development

# API key validation function (optional)
check_api_key = "lm_proxy.core.check_api_key"

# LLM Provider Connections
[connections]

[connections.openai]
api_type = "open_ai"
api_base = "https://api.openai.com/v1/"
api_key = "env:OPENAI_API_KEY"

[connections.google]
api_type = "google_ai_studio"
api_key = "env:GOOGLE_API_KEY"

# Routing rules (model_pattern = "connection.model")
[routing]
"gpt*" = "openai.*"     # Route all GPT models to OpenAI
"claude*" = "anthropic.*"  # Route all Claude models to Anthropic
"gemini*" = "google.*"  # Route all Gemini models to Google
"*" = "openai.gpt-3.5-turbo"  # Default fallback

# Access control groups
[groups.default]
api_keys = [
    "KEY1",
    "KEY2"
]

Environment Variables

You can use environment variables in your configuration file by prefixing values with env::

[connections.openai]
api_key = "env:OPENAI_API_KEY"

Load these from a .env file or set them in your environment before starting the server.

🔌 API Usage

LM Proxy Server implements the OpenAI chat completions API endpoint. You can use any OpenAI-compatible client to interact with it.

Endpoint

POST /v1/chat/completions

Request Format

{
  "model": "gpt-3.5-turbo",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"}
  ],
  "temperature": 0.7,
  "stream": false
}

Response Format

{
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ]
}

🛠️ Advanced Usage

Custom API Key Validation

You can implement your own API key validation function:

# my_validators.py
def validate_api_key(api_key: str) -> str | None:
    """
    Validate an API key and return the group name if valid.
    
    Args:
        api_key: The API key to validate
        
    Returns:
        The name of the group if valid, None otherwise
    """
    if api_key == "secret-key":
        return "admin"
    elif api_key.startswith("user-"):
        return "users"
    return None

Then reference it in your config:

check_api_key = "my_validators.validate_api_key"

Dynamic Model Routing

The routing section allows flexible pattern matching with wildcards:

[routing]
"gpt-4*" = "openai.gpt-4"           # Route gpt-4 requests to OpenAI GPT-4
"gpt-3.5*" = "openai.gpt-3.5-turbo" # Route gpt-3.5 requests to OpenAI
"claude*" = "anthropic.*"           # Pass model name as-is to Anthropic
"gemini*" = "google.*"              # Pass model name as-is to Google
"custom*" = "local.llama-7b"        # Map any "custom*" to a specific local model
"*" = "openai.gpt-3.5-turbo"        # Default fallback for unmatched models

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details. © 2025 Vitalii Stepanenko