# AnyLLM

The `AnyLLM` class is the provider interface at the core of any-llm. Use it when you need to make multiple requests against the same provider without re-instantiating on every call.

### Creating an Instance

#### `AnyLLM.create()`

Factory method that returns a configured `AnyLLM` instance for the given provider.

```
def create(
    provider: str | LLMProvider,
    api_key: str | None = None,
    api_base: str | None = None,
    **kwargs: Any,
) -> AnyLLM
```

| Parameter  | Type                 | Default    | Description                                     |
| ---------- | -------------------- | ---------- | ----------------------------------------------- |
| `provider` | `str \| LLMProvider` | *required* | The provider name (e.g., 'openai', 'anthropic') |
| `api_key`  | `str \| None`        | None       | API key for the provider                        |
| `api_base` | `str \| None`        | None       | Base URL for the provider API                   |
| `**kwargs` | `Any`                | *required* | Additional provider-specific arguments          |

**Returns:** An `AnyLLM` instance bound to the specified provider.

```python
from any_llm import AnyLLM

llm = AnyLLM.create("openai", api_key="sk-...")

response = llm.completion(
    model="gpt-4.1-mini",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)
```

### Static Methods

#### `AnyLLM.split_model_provider()`

Parses a combined `"provider:model"` string into its components.

```
def split_model_provider(
    model: str,
) -> tuple[LLMProvider, str]
```

| Parameter | Type  | Description                                                                                                                                             |
| --------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model`   | `str` | Combined identifier in `"provider:model"` format (e.g., `"openai:gpt-4.1-mini"`). The legacy `"provider/model"` format is also accepted but deprecated. |

**Returns:** A `(LLMProvider, model_name)` tuple.

**Raises:** `ValueError` if the string does not contain a `:` or `/` delimiter.

```python
provider, model_name = AnyLLM.split_model_provider("anthropic:claude-sonnet-4-20250514")
# provider = LLMProvider.ANTHROPIC
# model_name = "claude-sonnet-4-20250514"
```

#### `AnyLLM.get_all_provider_metadata()`

Returns metadata for every supported provider, sorted alphabetically by name.

```
def get_all_provider_metadata() -> list[ProviderMetadata]
```

**Returns:** A list of [`ProviderMetadata`](/api-reference/completion-1/provider.md) objects.

```python
for meta in AnyLLM.get_all_provider_metadata():
    print(f"{meta.name}: streaming={meta.streaming}, embedding={meta.embedding}")
```

#### `AnyLLM.get_supported_providers()`

Returns a list of all supported provider key strings.

```
def get_supported_providers() -> list[str]
```

**Returns:** `list[str]` of provider keys (e.g., `["anthropic", "openai", ...]`).

### Instance Methods

All instance methods below are called on an `AnyLLM` object returned by `AnyLLM.create()`.

#### `completion()` / `acompletion()`

Create a chat completion. See the [Completion](/api-reference/completion.md) reference for the full parameter list.

```
def completion(self, model, messages, *, stream=None, response_format=None, **kwargs)
    -> ChatCompletion | Iterator[ChatCompletionChunk] | ParsedChatCompletion

async def acompletion(self, model, messages, *, stream=None, response_format=None, **kwargs)
    -> ChatCompletion | AsyncIterator[ChatCompletionChunk] | ParsedChatCompletion
```

#### `responses()` / `aresponses()`

Create a response using the OpenResponses API. See the [Responses](/api-reference/responses.md) reference.

```
def responses(self, **kwargs)
    -> ResponseResource | Response | Iterator[ResponseStreamEvent]

async def aresponses(self, **kwargs)
    -> ResponseResource | Response | AsyncIterator[ResponseStreamEvent]
```

#### `messages()` / `amessages()`

Create a message using the Anthropic Messages API format. All providers support this through automatic conversion.

```
def messages(self, **kwargs)
    -> MessageResponse | Iterator[MessageStreamEvent]

async def amessages(self, model, messages, max_tokens, **kwargs)
    -> MessageResponse | AsyncIterator[MessageStreamEvent]
```

#### `list_models()` / `alist_models()`

List available models for this provider. See the [List Models](/api-reference/list-models.md) reference.

```
def list_models(self, **kwargs) -> Sequence[Model]
async def alist_models(self, **kwargs) -> Sequence[Model]
```

#### `create_batch()` / `acreate_batch()`

Create a batch job. See the [Batch](/api-reference/batch.md) reference.

```
def create_batch(self, **kwargs) -> Batch
async def acreate_batch(self, input_file_path, endpoint, completion_window="24h", metadata=None, **kwargs) -> Batch
```

#### `get_provider_metadata()`

Returns metadata for this provider instance's class.

```
def get_provider_metadata() -> ProviderMetadata
```

**Returns:** A [`ProviderMetadata`](/api-reference/completion-1/provider.md) object describing the provider's capabilities.

```python
llm = AnyLLM.create("mistral")
meta = llm.get_provider_metadata()
print(f"Supports streaming: {meta.streaming}")
print(f"Supports embedding: {meta.embedding}")
print(f"Supports responses: {meta.responses}")
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.mozilla.ai/api-reference/any-llm.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.