univllm 0.1.8

Universal interface for different LLM providers

univllm

A universal Python package that provides a standardised interface for different LLM providers including OpenAI, Anthropic, Deepseek, and Mistral.

Features

• Universal Interface: Single API to interact with multiple LLM providers
• Auto-Detection: Automatically detect the appropriate provider based on model name
• Streaming Support: Stream completions from all supported providers
• Model Capabilities: Query model capabilities like context window, function calling support, etc.
• Error Handling: Comprehensive error handling with provider-specific exceptions
• Async Support: Fully asynchronous API for better performance

Supported Providers

• OpenAI: GPT-4o & GPT-5 family models
• Anthropic: Claude 3.x / 4.x family models
• Deepseek: Deepseek Chat, Deepseek Coder
• Mistral: Mistral, Magistral & Codestral models

Supported Model Prefixes

The library validates models using simple prefix matching (see SUPPORTED_MODELS lists). Any model string that begins with one of these prefixes will be accepted. Provider-specific suffixes or date/version tags (e.g. -20240229, -latest, -0125, minor patch tags) are allowed but not individually validated.

Provider	Accepted Prefixes (Exact / Prefix Match)	Notes
OpenAI	gpt-5, gpt-5, gpt-5, gpt-oss-120b, gpt-oss-20b, gpt-vision-1, gpt-4o	Any extended suffix (e.g. gpt-4o-mini-2024-xx) will pass if it starts with a listed prefix.
Anthropic	claude-3-7-sonnet-, claude-4-opus-, claude-4-sonnet-, claude-opus-4.1, claude-code	Older variants (e.g. dated claude-3-* forms) can be added by extending the list in supported_models.py.
Deepseek	deepseek-chat, deepseek-coder	Code vs chat optimized.
Mistral	mistral-small-, mistral-medium-, magistral-small-, magistral-medium-, codestral-, mistral-ocr-	E.g. mistral-small-latest

Note: If you need additional model prefixes, you can locally extend the corresponding SUPPORTED_MODELS list in univllm/providers/supported_models.py or contribute a PR.

Installation

pip install univllm

Quick Start

import asyncio
from univllm import UniversalLLMClient


async def main():
    client = UniversalLLMClient()

    # Auto-detects provider based on model name
    response = await client.complete(
        messages=["What is the capital of France?"],
        model="gpt-4o"
    )

    print(response.content)


asyncio.run(main())

Configuration

Set your API keys as environment variables:

export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"
export DEEPSEEK_API_KEY="your-deepseek-key"
export MISTRAL_API_KEY="your-mistral-key"

Or pass them directly:

from univllm import UniversalLLMClient, ProviderType

client = UniversalLLMClient(
    provider=ProviderType.OPENAI,
    api_key="your-api-key"
)

Usage Examples

Basic Completion

import asyncio
from univllm import UniversalLLMClient


async def main():
    client = UniversalLLMClient()

    response = await client.complete(
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Explain quantum computing briefly."}
        ],
        model="gpt-4o",
        max_tokens=150,
        temperature=0.7
    )

    print(f"Response: {response.content}")
    print(f"Provider: {response.provider}")
    print(f"Model: {response.model}")
    print(f"Usage: {response.usage}")


asyncio.run(main())

Streaming Completion

import asyncio
from univllm import UniversalLLMClient


async def main():
    client = UniversalLLMClient()

    async for chunk in client.stream_complete(
            messages=["Tell me a short story about a robot."],
            model="gpt-4o",
            max_tokens=200
    ):
        print(chunk, end="", flush=True)


asyncio.run(main())

Model Capabilities

import asyncio
from univllm import UniversalLLMClient


async def main():
    client = UniversalLLMClient()

    # Get capabilities for a specific model
    capabilities = client.get_model_capabilities("gpt-4o")

    print(f"Supports function calling: {capabilities.supports_function_calling}")
    print(f"Context window: {capabilities.context_window}")
    print(f"Max tokens: {capabilities.max_tokens}")

    # Get all supported models
    all_models = client.get_supported_models()
    for provider, models in all_models.items():
        print(f"{provider}: {len(models)} models")


asyncio.run(main())

Multiple Providers

import asyncio
from univllm import UniversalLLMClient
from univllm.models import ProviderType


async def main():
    client = UniversalLLMClient()

    question = "What is machine learning?"

    # OpenAI
    openai_response = await client.complete(
        messages=[question],
        model="gpt-4o"
    )

    # Anthropic  
    anthropic_response = await client.complete(
        messages=[question],
        model="claude-4-sonnet"
    )

    print(f"OpenAI: {openai_response.content[:100]}...")
    print(f"Anthropic: {anthropic_response.content[:100]}...")


asyncio.run(main())

API Reference

UniversalLLMClient

Main client class for interacting with LLM providers.

Methods

• complete(): Generate a completion
• stream_complete(): Generate a streaming completion
• get_model_capabilities(): Get model capabilities
• get_supported_models(): Get supported models for all providers
• set_provider(): Set or change the provider

Models

• CompletionRequest: Request object for completions
• CompletionResponse: Response object from completions
• ModelCapabilities: Information about model capabilities
• Message: Individual message in a conversation

Providers

• ProviderType: Enum of supported providers
• BaseLLMProvider: Base class for provider implementations

Exceptions

• UniversalLLMError: Base exception
• ProviderError: Provider-related errors
• ModelNotSupportedError: Unsupported model errors
• AuthenticationError: Authentication failures
• ConfigurationError: Configuration issues

Development

git clone https://github.com/nihilok/univllm.git
cd univllm
pip install -e ".[dev]"

Run tests:

pytest

Licence

MIT Licence