refactor: Replace list_models_async with alist_models.

docs/api/list_models.md

@@ -1,4 +1,4 @@
 ## Models
 
 ::: any_llm.api.list_models
-::: any_llm.api.list_models_async
+::: any_llm.api.alist_models

src/any_llm/any_llm.py

@@ -1,7 +1,6 @@
 # Inspired by https://github.com/andrewyng/aisuite/tree/main/aisuite
 from __future__ import annotations
 
-import asyncio
 import importlib
 import os
 import warnings
@@ -473,14 +472,14 @@ async def _aembedding(self, model: str, inputs: str | list[str], **kwargs: Any)
         raise NotImplementedError(msg)
 
     def list_models(self, **kwargs: Any) -> Sequence[Model]:
-        """Return a list of Model if the provider supports listing models.
+        return run_async_in_sync(self.alist_models(**kwargs), allow_running_loop=INSIDE_NOTEBOOK)
 
-        Should be overridden by subclasses.
-        """
-        msg = "Subclasses must implement list_models method"
+    async def alist_models(self, **kwargs: Any) -> Sequence[Model]:
+        return await self._alist_models(**kwargs)
+
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         if not self.SUPPORTS_LIST_MODELS:
+            msg = "Provider doesn't support listing models."
             raise NotImplementedError(msg)
+        msg = "Subclasses must implement _alist_models method"
         raise NotImplementedError(msg)
-
-    async def list_models_async(self, **kwargs: Any) -> Sequence[Model]:
-        return await asyncio.to_thread(self.list_models, **kwargs)

src/any_llm/api.py

@@ -432,7 +432,7 @@ def list_models(
     return llm.list_models(**kwargs)
 
 
-async def list_models_async(
+async def alist_models(
     provider: str | LLMProvider,
     api_key: str | None = None,
     api_base: str | None = None,
@@ -443,4 +443,4 @@ async def list_models_async(
     llm = AnyLLM.create(
         LLMProvider.from_string(provider), ClientConfig(api_key=api_key, api_base=api_base, client_args=client_args)
     )
-    return await llm.list_models_async(**kwargs)
+    return await llm.alist_models(**kwargs)

src/any_llm/providers/anthropic/anthropic.py

@@ -7,7 +7,7 @@
 
 MISSING_PACKAGES_ERROR = None
 try:
-    from anthropic import Anthropic, AsyncAnthropic
+    from anthropic import AsyncAnthropic
 
     from .utils import (
         _convert_models_list,
@@ -19,7 +19,6 @@
     MISSING_PACKAGES_ERROR = e
 
 if TYPE_CHECKING:
-    from anthropic.pagination import SyncPage
     from anthropic.types import Message
     from anthropic.types.model_info import ModelInfo as AnthropicModelInfo
 
@@ -75,7 +74,7 @@ def _convert_embedding_response(response: Any) -> CreateEmbeddingResponse:
         raise NotImplementedError(msg)
 
     @staticmethod
-    def _convert_list_models_response(response: "SyncPage[AnthropicModelInfo]") -> Sequence[Model]:
+    def _convert_list_models_response(response: "list[AnthropicModelInfo]") -> Sequence[Model]:
         """Convert Anthropic models list to OpenAI format."""
         return _convert_models_list(response)
 
@@ -111,12 +110,11 @@ async def _acompletion(
 
         return self._convert_completion_response(message)
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
-        """List available models from Anthropic."""
-        client = Anthropic(
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
+        client = AsyncAnthropic(
             api_key=self.config.api_key,
             base_url=self.config.api_base,
             **(self.config.client_args if self.config.client_args else {}),
         )
-        models_list = client.models.list(**kwargs)
-        return self._convert_list_models_response(models_list)
+        models_list = await client.models.list(**kwargs)
+        return self._convert_list_models_response(models_list.data)

src/any_llm/providers/anthropic/utils.py

@@ -1,7 +1,6 @@
 import json
 from typing import Any
 
-from anthropic.pagination import SyncPage
 from anthropic.types import (
     ContentBlockDeltaEvent,
     ContentBlockStartEvent,
@@ -316,7 +315,7 @@ def _convert_params(params: CompletionParams, **kwargs: Any) -> dict[str, Any]:
     return result_kwargs
 
 
-def _convert_models_list(models_list: SyncPage[AnthropicModelInfo]) -> list[Model]:
+def _convert_models_list(models_list: list[AnthropicModelInfo]) -> list[Model]:
     """Convert Anthropic models list to OpenAI format."""
     return [
         Model(id=model.id, object="model", created=int(model.created_at.timestamp()), owned_by="anthropic")

src/any_llm/providers/bedrock/bedrock.py

@@ -239,7 +239,7 @@ def embedding(
         response_data = {"embedding_data": embedding_data, "model": model, "total_tokens": total_tokens}
         return self._convert_embedding_response(response_data)
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         """
         Fetch available models from the /v1/models endpoint.
         """

src/any_llm/providers/cerebras/cerebras.py

@@ -151,12 +151,12 @@ async def _acompletion(
 
         return self._convert_completion_response(response_data)
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         """
         Fetch available models from the /v1/models endpoint.
         """
-        client = cerebras.Cerebras(
+        client = cerebras.AsyncCerebras(
             api_key=self.config.api_key, **(self.config.client_args if self.config.client_args else {})
         )
-        models_list = client.models.list(**kwargs)
+        models_list = await client.models.list(**kwargs)
         return self._convert_list_models_response(models_list)

src/any_llm/providers/cohere/cohere.py

@@ -148,12 +148,12 @@ async def _acompletion(
 
         return self._convert_completion_response(response, model=params.model_id)
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         """
         Fetch available models from the /v1/models endpoint.
         """
-        client = cohere.ClientV2(
+        client = cohere.AsyncClientV2(
             api_key=self.config.api_key, **(self.config.client_args if self.config.client_args else {})
         )
-        model_list = client.models.list(**kwargs)
+        model_list = await client.models.list(**kwargs)
         return self._convert_list_models_response(model_list)

src/any_llm/providers/gemini/base.py

@@ -246,8 +246,7 @@ async def _stream() -> AsyncIterator[ChatCompletionChunk]:
         response_dict = _convert_response_to_response_dict(response)
         return self._convert_completion_response((response_dict, params.model_id))
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
-        """Fetch available models from the /v1/models endpoint."""
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         client = self._get_client(self.config)
-        models_list = client.models.list(**kwargs)
+        models_list = await client.aio.models.list(**kwargs)
         return self._convert_list_models_response(models_list)

src/any_llm/providers/groq/groq.py

@@ -171,10 +171,12 @@ async def _aresponses(
 
         return response
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         """
         Fetch available models from the /v1/models endpoint.
         """
-        client = groq.Groq(api_key=self.config.api_key, **(self.config.client_args if self.config.client_args else {}))
-        models_list = client.models.list(**kwargs)
+        client = groq.AsyncGroq(
+            api_key=self.config.api_key, **(self.config.client_args if self.config.client_args else {})
+        )
+        models_list = await client.models.list(**kwargs)
         return self._convert_list_models_response(models_list)

src/any_llm/providers/huggingface/huggingface.py

@@ -149,7 +149,7 @@ async def _acompletion(
             usage=usage,
         )
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         """
         Fetch available models from the /v1/models endpoint.
         """

src/any_llm/providers/mistral/mistral.py

@@ -156,14 +156,11 @@ async def _aembedding(
 
         return self._convert_embedding_response(result)
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
-        """
-        Fetch available models from the /v1/models endpoint.
-        """
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         client = Mistral(
             api_key=self.config.api_key,
             server_url=self.config.api_base,
             **(self.config.client_args if self.config.client_args else {}),
         )
-        models_list = client.models.list(**kwargs)
+        models_list = await client.models.list_async(**kwargs)
         return self._convert_list_models_response(models_list)

src/any_llm/providers/ollama/ollama.py

@@ -10,7 +10,7 @@
 
 MISSING_PACKAGES_ERROR = None
 try:
-    from ollama import AsyncClient, Client
+    from ollama import AsyncClient
 
     from .utils import (
         _convert_models_list,
@@ -24,7 +24,7 @@
 if TYPE_CHECKING:
     from collections.abc import AsyncIterator, Sequence
 
-    from ollama import AsyncClient, Client  # noqa: TC004
+    from ollama import AsyncClient  # noqa: TC004
     from ollama import ChatResponse as OllamaChatResponse
 
     from any_llm.config import ClientConfig
@@ -230,10 +230,7 @@ async def _aembedding(
         )
         return self._convert_embedding_response(response)
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
-        """
-        Fetch available models from the /v1/models endpoint.
-        """
-        client = Client(host=self.url, **(self.config.client_args if self.config.client_args else {}))
-        models_list = client.list(**kwargs)
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
+        client = AsyncClient(host=self.url, **(self.config.client_args if self.config.client_args else {}))
+        models_list = await client.list(**kwargs)
         return self._convert_list_models_response(models_list)

src/any_llm/providers/openai/base.py

@@ -184,13 +184,13 @@ async def _aembedding(
             )
         )
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         """
         Fetch available models from the /v1/models endpoint.
         """
         if not self.SUPPORTS_LIST_MODELS:
             message = f"{self.PROVIDER_NAME} does not support listing models."
             raise NotImplementedError(message)
-        client = cast("OpenAI", self._get_client(sync=True))
-        response = client.models.list(**kwargs)
+        client = cast("AsyncOpenAI", self._get_client())
+        response = await client.models.list(**kwargs)
         return self._convert_list_models_response(response)

src/any_llm/providers/together/together.py

@@ -42,7 +42,7 @@ class TogetherProvider(AnyLLM):
     SUPPORTS_COMPLETION_IMAGE = True
     SUPPORTS_COMPLETION_PDF = True
     SUPPORTS_EMBEDDING = False
-    SUPPORTS_LIST_MODELS = True
+    SUPPORTS_LIST_MODELS = False
 
     MISSING_PACKAGES_ERROR = MISSING_PACKAGES_ERROR
 

src/any_llm/providers/watsonx/watsonx.py

@@ -152,7 +152,7 @@ async def _acompletion(
 
         return self._convert_completion_response(response)
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         """
         Fetch available models from the /v1/models endpoint.
         """

src/any_llm/providers/xai/xai.py

@@ -8,7 +8,6 @@
 MISSING_PACKAGES_ERROR = None
 try:
     from xai_sdk import AsyncClient as XaiAsyncClient
-    from xai_sdk import Client as XaiClient
     from xai_sdk.chat import Chunk as XaiChunk
     from xai_sdk.chat import Response as XaiResponse
     from xai_sdk.chat import assistant, required_tool, system, tool_result, user
@@ -148,10 +147,12 @@ async def _stream() -> AsyncIterator[ChatCompletionChunk]:
 
         return self._convert_completion_response(response)
 
-    def list_models(self, **kwargs: Any) -> Sequence[Model]:
+    async def _alist_models(self, **kwargs: Any) -> Sequence[Model]:
         """
         Fetch available models from the /v1/models endpoint.
         """
-        client = XaiClient(api_key=self.config.api_key, **(self.config.client_args if self.config.client_args else {}))
-        models_list = client.models.list_language_models()
+        client = XaiAsyncClient(
+            api_key=self.config.api_key, **(self.config.client_args if self.config.client_args else {})
+        )
+        models_list = await client.models.list_language_models()
         return self._convert_list_models_response(models_list)

tests/unit/providers/test_openai_base_provider.py

@@ -1,11 +1,11 @@
-from unittest.mock import MagicMock, patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 from any_llm.config import ClientConfig
 from any_llm.providers.openai.base import BaseOpenAIProvider
 from any_llm.types.model import Model
 
 
-@patch("any_llm.providers.openai.base.OpenAI")
+@patch("any_llm.providers.openai.base.AsyncOpenAI")
 def test_list_models_returns_model_list_when_supported(mock_openai_class: MagicMock) -> None:
     class ListModelsProvider(BaseOpenAIProvider):
         SUPPORTS_LIST_MODELS = True
@@ -19,7 +19,7 @@ class ListModelsProvider(BaseOpenAIProvider):
         Model(id="gpt-4", object="model", created=1687882411, owned_by="openai"),
     ]
 
-    mock_client = MagicMock()
+    mock_client = AsyncMock()
     mock_client.models.list.return_value.data = mock_model_data
     mock_openai_class.return_value = mock_client
 
@@ -33,7 +33,7 @@ class ListModelsProvider(BaseOpenAIProvider):
     mock_client.models.list.assert_called_once_with()
 
 
-@patch("any_llm.providers.openai.base.OpenAI")
+@patch("any_llm.providers.openai.base.AsyncOpenAI")
 def test_list_models_uses_default_api_base_when_not_configured(mock_openai_class: MagicMock) -> None:
     class ListModelsProvider(BaseOpenAIProvider):
         SUPPORTS_LIST_MODELS = True
@@ -42,7 +42,7 @@ class ListModelsProvider(BaseOpenAIProvider):
         PROVIDER_DOCUMENTATION_URL = "https://example.com"
         API_BASE = "https://api.default.com/v1"
 
-    mock_client = MagicMock()
+    mock_client = AsyncMock()
     mock_client.models.list.return_value.data = []
     mock_openai_class.return_value = mock_client
 
@@ -54,15 +54,17 @@ class ListModelsProvider(BaseOpenAIProvider):
     mock_openai_class.assert_called_once_with(base_url="https://api.default.com/v1", api_key="test-key")
 
 
-@patch("any_llm.providers.openai.base.OpenAI")
+@patch(
+    "any_llm.providers.openai.base.AsyncOpenAI",
+)
 def test_list_models_passes_kwargs_to_client(mock_openai_class: MagicMock) -> None:
     class ListModelsProvider(BaseOpenAIProvider):
         SUPPORTS_LIST_MODELS = True
         PROVIDER_NAME = "ListModelsProvider"
         ENV_API_KEY_NAME = "TEST_API_KEY"
         PROVIDER_DOCUMENTATION_URL = "https://example.com"
 
-    mock_client = MagicMock()
+    mock_client = AsyncMock()
     mock_client.models.list.return_value.data = []
     mock_openai_class.return_value = mock_client