unified-model-caller 0.2.0

A tiny library for manage calls to the LLMs of different services (Paris-Saclay Aristote Included).

Unified Model Caller

A small, lightweight library that provides a single unified interface for calling LLMs from different providers. Instead of learning each provider's SDK separately, you instantiate one LLMCaller and swap the service name.

Supported services

Service name	Provider
openai	OpenAI (GPT models)
anthropic	Anthropic (Claude models)
google	Google (Gemini models)
xai	xAI (Grok models)
ilaas	Ilaas
aristoteonmydocker	Aristote on MyDocker

Installation

Via pip:

pip install git+https://github.com/DobbiKov/unified-model-caller.git

Via uv:

uv add git+https://github.com/DobbiKov/unified-model-caller.git

Usage

from unified_model_caller import LLMCaller

caller = LLMCaller("google", "gemini-2.0-flash", api_key="<your-api-key>")
response = caller.call("What is a matrix?")
print(response)

The constructor signature is:

LLMCaller(service: str, model: str, api_key: str = "")

• service — case-insensitive service name (see table above)
• model — model identifier string passed directly to the provider
• api_key — API key; can be omitted for services that don't require one

Rate limiting

Call wait_cooldown() between requests to respect each service's built-in cooldown:

caller.wait_cooldown()
response = caller.call("Next prompt")

Listing available services

LLMCaller.get_services()
# ['openai', 'anthropic', 'google', 'xai', 'ilaas', 'aristoteonmydocker']

Adding an external service

You can register a new service at runtime from any Python file — no changes to the library are needed.

1. Create a service file

The file must define a class that inherits from BaseService and implements four methods:

# my_service.py
from unified_model_caller import BaseService

class MyService(BaseService):
    def get_name(self) -> str:
        """Unique lowercase name used to identify this service."""
        return "myservice"

    def requires_token(self) -> bool:
        """Return True if the service needs an API key."""
        return True

    def service_cooldown(self) -> int:
        """Minimum delay between calls, in milliseconds."""
        return 1000

    def call(self, model: str, prompt: str) -> str:
        """Send prompt to the model and return the response text."""
        import requests
        response = requests.post(
            "https://api.myservice.example/v1/completions",
            json={"model": model, "prompt": prompt},
            headers={"Authorization": f"Bearer {self.api_key}"},
        )
        return response.json()["text"]

The api_key passed to LLMCaller(...) is available as self.api_key inside your class.

2. Register and use it

from unified_model_caller import LLMCaller

LLMCaller.add_service("/path/to/my_service.py")

caller = LLMCaller("myservice", "my-model-name", api_key="<your-api-key>")
response = caller.call("Hello!")
print(response)

add_service loads the file, finds the BaseService subclass inside it, and registers it globally under the name returned by get_name(). The service is then available to all subsequent LLMCaller instances in the same process.

BaseService contract

Method	Return type	Description
get_name(self)	str	Unique service identifier (lowercase). Used as the service argument to LLMCaller.
requires_token(self)	bool	Whether the service needs an API key.
service_cooldown(self)	int	Cooldown between calls in milliseconds.
call(self, model, prompt)	str	Perform the API call and return the response text.