lm-proxy 0.3.0

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

LM-Proxy

LM-Proxy 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

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

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 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"

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

# optional
[[loggers]]
class = 'lm_proxy.loggers.BaseLogger'
[loggers.log_writer]
class = 'lm_proxy.loggers.log_writers.JsonLogWriter'
file_name = 'storage/json.log'
[loggers.entry_transformer]
class = 'lm_proxy.loggers.LogEntryTransformer'
completion_tokens = "response.usage.completion_tokens"
prompt_tokens = "response.usage.prompt_tokens"
prompt = "request.messages"
response = "response"
group = "group"
connection = "connection"
api_key_id = "api_key_id"
remote_addr = "remote_addr"
created_at = "created_at"
duration = "duration"

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.

🔑 Proxy API Keys vs. Provider API Keys

LM-Proxy utilizes two distinct types of API keys to facilitate secure and efficient request handling.

• Proxy API Key (Virtual API Key, Client API Key):
  A unique key generated and managed within the LM-Proxy.
  Clients use these keys to authenticate their requests to the proxy's API endpoints.
  Each Client API Key is associated with a specific group, which defines the scope of access and permissions for the client's requests.
  These keys allow users to securely interact with the proxy without direct access to external service credentials.

• Provider API Key (Upstream API Key): A key provided by external LLM inference providers (e.g., OpenAI, Anthropic, Mistral, etc.) and configured within the LM-Proxy.
  The proxy uses these keys to authenticate and forward validated client requests to the respective external services.
  Provider API Keys remain hidden from end users, ensuring secure and transparent communication with provider APIs.

This distinction ensures a clear separation of concerns: Virtual API Keys manage user authentication and access within the proxy, while Upstream API Keys handle secure communication with external providers.

🔌 API Usage

LM-Proxy 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