univllm 0.1.14

Universal interface for different LLM providers

univllm

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

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
• MCP Tool Calling: Compatible with Model Context Protocol (MCP) for function/tool calling
• 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.x & GPT-5.2 family models
• Anthropic: Claude 3.x, 4.x & 4.5 family models
• Deepseek: DeepSeek V3.2, Chat, Reasoner, Coder & VL models
• Mistral: Mistral Large 3, Ministral 3, Magistral, Codestral & specialized models
• Gemini: Google Gemini 1.5, 2.0 & 2.5 family 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-5.2, gpt-5.1, gpt-5, gpt-5-mini, gpt-5-nano, gpt-5-codex, gpt-oss-120b, gpt-oss-20b, gpt-vision-1, gpt-4o, gpt-4	GPT-5.2 is the latest flagship model (Dec 2025). Any extended suffix (e.g. gpt-5.2-2025-12-11) will pass if it starts with a listed prefix.
Anthropic	claude-opus-4-5, claude-sonnet-4-5, claude-haiku-4-5, claude-opus-4.1, claude-sonnet-4-, claude-haiku-4-, claude-opus-4-, claude-code, claude-3-7-sonnet-, claude-3-5-sonnet-	Claude 4.5 series launched Sep-Nov 2025. Older variants (e.g. dated claude-3-* forms) can be added by extending the list in supported_models.py.
Deepseek	deepseek-chat, deepseek-reasoner, deepseek-coder, deepseek-vl, deepseek-v3	DeepSeek V3.2 models. deepseek-reasoner for advanced reasoning tasks. deepseek-vl for vision-language.
Mistral	mistral-large-3, mistral-medium-3, mistral-small-3, ministral-3-, magistral-medium-, magistral-small-, codestral-, devstral-, voxtral-, mistral-ocr-, ocr-3-	Mistral Large 3 (Dec 2025) flagship multimodal model. Ministral for edge, Codestral for code generation.
Gemini	gemini-2.5-pro, gemini-2.5-flash, gemini-2.0-flash, gemini-1.5-pro, gemini-1.5-flash	Google's Gemini models across multiple versions.

Note: If you need additional model prefixes, you can locally extend the corresponding SUPPORTED_MODELS list in univllm/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-5.2"
    )

    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"
export GEMINI_API_KEY="your-gemini-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-5.2",
        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="claude-sonnet-4-5",
            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-5.2")

    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())

Tool Calling (MCP Compatible)

univllm supports function/tool calling following the Model Context Protocol (MCP) format:

import asyncio
from univllm import UniversalLLMClient, ToolDefinition


async def main():
    client = UniversalLLMClient()

    # Define a tool using MCP format
    weather_tool = ToolDefinition(
        name="get_weather",
        description="Get current weather for a location",
        input_schema={
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "City name or zip code"
                }
            },
            "required": ["location"]
        }
    )

    # Request with tools
    response = await client.complete(
        messages=[{"role": "user", "content": "What's the weather in Paris?"}],
        model="gpt-4o",
        tools=[weather_tool],
        tool_choice="auto"  # Let the model decide when to use tools
    )

    # Check if model wants to call a tool
    if response.tool_calls:
        tool_call = response.tool_calls[0]
        print(f"Tool: {tool_call.name}")
        print(f"Arguments: {tool_call.arguments}")
        
        # Execute the tool and get result
        # ... your tool execution logic ...
        
        # Continue conversation with tool result
        # ... send tool result back to model ...


asyncio.run(main())

You can also pass tools as dictionaries:

tools = [
    {
        "name": "calculate",
        "description": "Perform arithmetic calculations",
        "input_schema": {
            "type": "object",
            "properties": {
                "expression": {"type": "string"}
            },
            "required": ["expression"]
        }
    }
]

response = await client.complete(
    messages=[{"role": "user", "content": "Calculate 15 * 23"}],
    model="gpt-4o",
    tools=tools
)

See examples_tool_calling.py for more comprehensive examples.

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-5.2"
    )

    # Anthropic  
    anthropic_response = await client.complete(
        messages=[question],
        model="claude-sonnet-4-5"
    )

    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