Simple, unified interface to multiple Generative AI providers.

aisuite is an tool designed for developers who need to evaluate and compare the responses of multiple LLMs through a standardized interface. Based on the OpenAI interface standard, aisuite makes it easy to interact with the most popular LLMs and compare the results. It is a thin wrapper around python client libraries allowing creators to seamlessly swap out or test responses from a number of LLMs without changing their code. Today, the library is primarily focussed on chat completions, but will be expanded to cover more use cases in near future.

Currently supported providers are - OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace and Ollama. Internally, aisuite uses either the HTTP endpoint or the SDK for making calls to the provider.

Users can install just the base aisuite package, or install a provider's package along with aisuite.

This installs just the base package without installing any provider's SDK.

pip install aisuite

This installs aisuite along with anthropic library.

pip install aisuite[anthropic]

This installs all the provider specific libraries

pip install aisuite[all]

To get started you will need the API Keys for the providers you intend to use. You also need to install the provider specific library either separately or when installing aisuite.

The API Keys can be set as environment variables, or can be passed as config to the aisuite Client constructor. Tools like python-dotenv or direnv can be used to set the environment variables manually. Please take a look at the examples folder to see usage.

Here is a short example of using aisuite to generate chat completion responses from gpt-4o and claude-3-5-sonnet.

Set the API keys.

export ANTHROPIC_API_KEY="your-anthropic-api-key" export OPENAI_API_KEY="your-openai-api-key"

Use the python client.

import aisuite as ai client = ai.Client() models = ["openai:gpt-4o", "anthropic:claude-3-5-sonnet-20240620"] messages = [ {"role": "system", "content": "Respond in Pirate English."}, {"role": "user", "content": "Tell me a joke."}, ] for model in models: response = client.chat.completions.create( model=model, messages=messages, temperature=0.75 ) print(response.choices[0].message.content)

Note that the model name in the create() call is of the format - <provider>:<model-name>. aisuite will call the appropriate provider with the right parameters based on the provider value. For a list of provider values, you can look at the directory - aisuite/providers/. The list of supported providers are of the format - <provider>_provider.py in that directory. We welcome any provider to add support to this library by adding an implementation file in this directory. Please see section below for the same.

For more examples, check out the examples directory where you will find several notebooks that you can run to experiment with the interface.

aisuite is released under the MIT License. You are free to use, modify, and distribute the code for both commercial and non-commercial purposes.

If you would like to contribute, please read our Contributing Guide and join our Discord server!

We have made easy for a provider or volunteer to add support for a new platform.

A convention-based approach is followed for loading providers, which relies on strict naming conventions for both the module name and the class name. The format to follow is based on the model identifier in the form of provider:model.

• The provider's module file must be named in the format <provider>_provider.py.
• The class inside this module must follow the format: the provider name with the first letter capitalized, followed by the suffix Provider.

• AWS: The provider class should be defined as:

  class AwsProvider(BaseProvider)

  in providers/aws_provider.py.

• OpenAI: The provider class should be defined as:

  class OpenaiProvider(BaseProvider)

  in providers/openai_provider.py

This convention simplifies the addition of new providers and ensures consistency across provider implementations.