# any-llm [![Read the Blog Post](https://img.shields.io/badge/Read%20the%20Blog%20Post-red.svg)](https://blog.mozilla.ai/introducing-any-llm-a-unified-api-to-access-any-llm-provider/) [![Docs](https://github.com/mozilla-ai/any-llm/actions/workflows/docs.yaml/badge.svg)](https://github.com/mozilla-ai/any-llm/actions/workflows/docs.yaml/) [![Linting](https://github.com/mozilla-ai/any-llm/actions/workflows/lint.yaml/badge.svg)](https://github.com/mozilla-ai/any-llm/actions/workflows/lint.yaml/) [![Unit Tests](https://github.com/mozilla-ai/any-llm/actions/workflows/tests-unit.yaml/badge.svg)](https://github.com/mozilla-ai/any-llm/actions/workflows/tests-unit.yaml/) [![Integration Tests](https://github.com/mozilla-ai/any-llm/actions/workflows/tests-integration.yaml/badge.svg)](https://github.com/mozilla-ai/any-llm/actions/workflows/tests-integration.yaml/) ![Python 3.11+](https://img.shields.io/badge/python-3.11%2B-blue.svg) [![PyPI](https://img.shields.io/pypi/v/any-llm-sdk)](https://pypi.org/project/any-llm-sdk/)

**Communicate with any LLM provider using a single, unified interface.** Switch between OpenAI, Anthropic, Azure / Microsoft Foundry, Mistral, Ollama, and more without changing your code. [Documentation](https://docs.mozilla.ai/any-llm/) | [otari.ai](https://otari.ai/) | [Try the Demos](#-try-it) | [Contributing](#-contributing)

## Quickstart ```python pip install 'any-llm-sdk[mistral,ollama]' export MISTRAL_API_KEY="YOUR_KEY_HERE" # or OPENAI_API_KEY, etc from any_llm import completion import os # Make sure you have the appropriate environment variable set assert os.environ.get('MISTRAL_API_KEY') response = completion( model="mistral-small-latest", provider="mistral", messages=[{"role": "user", "content": "Hello!"}] ) print(response.choices[0].message.content) ``` **That's it!** Change the provider name and add provider-specific keys to switch between LLM providers. > **Coming from LiteLLM?** Your API keys and environment variables carry over unchanged. Install the SDK with extras for the providers you need, then update your import and model strings: > > ```bash > pip install 'any-llm-sdk[openai,anthropic]' # or [all] for everything > ``` > ```python > # before > from litellm import completion > response = completion(model="openai/gpt-4o", messages=[...]) > > # after > from any_llm import completion > response = completion(model="openai:gpt-4o", messages=[...]) > ``` > > See [Supported Providers](https://docs.mozilla.ai/any-llm/providers/) to map your existing model strings. That's the full migration — no proxy, no extra config. ## Installation ### Requirements - Python 3.11 or newer - API keys for whichever LLM providers you want to use ### Basic Installation Install support for specific providers: ```bash pip install 'any-llm-sdk[openai]' # Just OpenAI pip install 'any-llm-sdk[mistral,ollama]' # Multiple providers pip install 'any-llm-sdk[all]' # All supported providers ``` See our [list of supported providers](https://docs.mozilla.ai/any-llm/providers/) to choose which ones you need. Using an OpenAI-compatible gateway or local server that isn't listed? You don't need a dedicated provider entry: see [Custom OpenAI-compatible Endpoints](https://docs.mozilla.ai/any-llm/quickstart#custom-openai-compatible-endpoints). ### Setting Up API Keys Set environment variables for your chosen providers: ```bash export OPENAI_API_KEY="your-key-here" export ANTHROPIC_API_KEY="your-key-here" export MISTRAL_API_KEY="your-key-here" # ... etc ``` Alternatively, pass API keys directly in your code (see [Usage](#usage) examples). ## Otari Gateway For budget management, API key management, usage analytics, and multi-tenant support, see [mozilla-ai/otari](https://github.com/mozilla-ai/otari). ## Why choose `any-llm`? - **Simple, unified interface** - Single function for all providers, switch models with just a string change - **Developer friendly** - Full type hints for better IDE support and clear, actionable error messages - **Leverages official provider SDKs** - Ensures maximum compatibility - **Stays framework-agnostic** so it can be used across different projects and use cases - **Battle-tested** - Powers our own production tools ([any-agent](https://github.com/mozilla-ai/any-agent)) ## Usage `any-llm` offers two main approaches for interacting with LLM providers: #### Option 1: Direct API Functions (Recommended for Bootstrapping and Experimentation) **Recommended approach:** Use separate `provider` and `model` parameters: ```python from any_llm import completion import os # Make sure you have the appropriate environment variable set assert os.environ.get('MISTRAL_API_KEY') response = completion( model="mistral-small-latest", provider="mistral", messages=[{"role": "user", "content": "Hello!"}] ) print(response.choices[0].message.content) ``` **Alternative syntax:** Use combined `provider:model` format: ```python response = completion( model="mistral:mistral-small-latest", # : messages=[{"role": "user", "content": "Hello!"}] ) ``` #### Option 2: AnyLLM Class (Recommended for Production) For applications that need to reuse providers, perform multiple operations, or require more control: ```python from any_llm import AnyLLM llm = AnyLLM.create("mistral", api_key="your-mistral-api-key") response = llm.completion( model="mistral-small-latest", messages=[{"role": "user", "content": "Hello!"}] ) ``` #### When to Use Which Approach | Approach | Best For | Connection Handling | |----------|----------|---------------------| | **Direct API Functions** (`completion`) | Scripts, notebooks, single requests | New client per call (stateless) | | **AnyLLM Class** (`AnyLLM.create`) | Production apps, multiple requests | Reuses client (connection pooling) | Both approaches support identical features: streaming, tools, responses API, etc. ### Responses API For providers that implement the OpenAI-style Responses API, use [`responses`](https://docs.mozilla.ai/any-llm/api/responses/) or `aresponses`: ```python from any_llm import responses result = responses( model="gpt-4o-mini", provider="openai", input_data=[ {"role": "user", "content": [ {"type": "text", "text": "Summarize this in one sentence."} ]} ], ) # Non-streaming returns an OpenAI-compatible Responses object alias print(result.output_text) ``` ### Finding the Right Model The `provider_id` should match our [supported provider names](https://docs.mozilla.ai/any-llm/providers/). The `model_id` is passed directly to the provider. To find available models: - Check the provider's documentation - Use our `list_models` API (if the provider supports it) ## Motivation The landscape of LLM provider interfaces is fragmented. While OpenAI's API has become the de facto standard, providers implement slight variations in parameter names, response formats, and feature sets. This creates a need for light wrappers that gracefully handle these differences while maintaining a consistent interface. **Existing Solutions and Their Limitations:** - **[LiteLLM](https://github.com/BerriAI/litellm)**: Popular but reimplements provider interfaces rather than leveraging official SDKs, leading to potential compatibility issues. - **[AISuite](https://github.com/andrewyng/aisuite/issues)**: Clean, modular approach but lacks active maintenance, comprehensive testing, and modern Python typing standards. - **[Framework-specific solutions](https://github.com/agno-agi/agno/tree/main/libs/agno/agno/models)**: Some agent frameworks either depend on LiteLLM or implement their own provider integrations, creating fragmentation - **[Proxy Only Solutions](https://openrouter.ai/)**: solutions like [OpenRouter](https://openrouter.ai/) and [Portkey](https://github.com/Portkey-AI/portkey-python-sdk) require a hosted proxy between your code and the LLM provider. `any-llm` addresses these challenges by leveraging official SDKs when available, maintaining framework-agnostic design, and requiring no proxy servers. ## Documentation - **[Full Documentation](https://docs.mozilla.ai/any-llm/)** - Complete guides and API reference - **[Supported Providers](https://docs.mozilla.ai/any-llm/providers/)** - List of all supported LLM providers - **[Cookbook Examples](https://docs.mozilla.ai/any-llm/cookbooks/any-llm-getting-started)** - In-depth usage examples ## Contributing We welcome contributions from developers of all skill levels! Please see our [Contributing Guide](CONTRIBUTING.md) or open an issue to discuss changes. ## License This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE.md) file for details.