feat(cerebras): enable reasoning support in CerebrasProvider

src/any_llm/providers/cerebras/cerebras.py

@@ -38,7 +38,7 @@ class CerebrasProvider(AnyLLM):
     SUPPORTS_COMPLETION_STREAMING = True
     SUPPORTS_COMPLETION = True
     SUPPORTS_RESPONSES = False
-    SUPPORTS_COMPLETION_REASONING = False
+    SUPPORTS_COMPLETION_REASONING = True
     SUPPORTS_COMPLETION_IMAGE = False
     SUPPORTS_COMPLETION_PDF = False
     SUPPORTS_EMBEDDING = False

src/any_llm/providers/cerebras/utils.py

@@ -12,6 +12,7 @@
     Choice,
     CompletionUsage,
     Function,
+    Reasoning,
 )
 from any_llm.types.model import Model
 
@@ -75,6 +76,10 @@ def _create_openai_chunk_from_cerebras_chunk(chunk: ChatChunkResponse) -> ChatCo
                     tool_calls_list.append(tool_call_dict)
                 delta["tool_calls"] = tool_calls_list
 
+            reasoning_content = getattr(choice_delta, "reasoning", None)
+            if reasoning_content:
+                delta["reasoning"] = {"content": reasoning_content}
+
     usage = getattr(chunk, "usage", None)
     if usage:
         chunk_dict["usage"] = {
@@ -117,10 +122,13 @@ def _convert_response(response_data: dict[str, Any]) -> ChatCompletion:
                     )
                 )
             tool_calls = tool_calls_list
+
+        reasoning_content = message_data.get("reasoning", None)
         message = ChatCompletionMessage(
             role=message_data.get("role", "assistant"),
             content=message_data.get("content"),
             tool_calls=tool_calls,
+            reasoning=Reasoning(content=reasoning_content) if reasoning_content else None,
         )
         from typing import Literal, cast
 

tests/conftest.py

@@ -24,6 +24,7 @@ def provider_reasoning_model_map() -> dict[LLMProvider, str]:
         LLMProvider.LLAMACPP: "N/A",
         LLMProvider.LMSTUDIO: "openai/gpt-oss-20b",  # You must have LM Studio running and the server enabled
         LLMProvider.AZUREOPENAI: "azure/<your_deployment_name>",
+        LLMProvider.CEREBRAS: "gpt-oss-120b",
         LLMProvider.COHERE: "command-a-reasoning-08-2025",
         LLMProvider.DEEPSEEK: "deepseek-reasoner",
     }

tests/unit/providers/test_cerebras_provider.py

@@ -2,6 +2,7 @@
 
 from any_llm.exceptions import UnsupportedParameterError
 from any_llm.providers.cerebras.cerebras import CerebrasProvider
+from any_llm.providers.cerebras.utils import _convert_response, _create_openai_chunk_from_cerebras_chunk
 
 
 @pytest.mark.asyncio
@@ -20,3 +21,113 @@ async def test_stream_with_response_format_raises() -> None:
     with pytest.raises(UnsupportedParameterError):
         async for _ in chunks:
             pass
+
+
+def test_convert_response_extracts_reasoning() -> None:
+    response_data = {
+        "id": "test-id",
+        "model": "llama-3.3-70b",
+        "created": 1234567890,
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": "Hello!",
+                    "reasoning": "The user asked me to say hello, so I will respond with a greeting.",
+                    "tool_calls": None,
+                },
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+    }
+
+    result = _convert_response(response_data)
+
+    assert result.choices[0].message.content == "Hello!"
+    assert result.choices[0].message.reasoning is not None
+    assert (
+        result.choices[0].message.reasoning.content
+        == "The user asked me to say hello, so I will respond with a greeting."
+    )
+
+
+def test_convert_response_without_reasoning() -> None:
+    response_data = {
+        "id": "test-id",
+        "model": "llama-3.3-70b",
+        "created": 1234567890,
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": "Hello!",
+                    "tool_calls": None,
+                },
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+    }
+
+    result = _convert_response(response_data)
+
+    assert result.choices[0].message.content == "Hello!"
+    assert result.choices[0].message.reasoning is None
+
+
+def test_convert_chunk_extracts_reasoning() -> None:
+    from unittest.mock import Mock
+
+    mock_chunk = Mock()
+    mock_chunk.id = "test-chunk-id"
+    mock_chunk.model = "llama-3.3-70b"
+    mock_chunk.created = 1234567890
+
+    mock_delta = Mock()
+    mock_delta.content = "Hello!"
+    mock_delta.role = "assistant"
+    mock_delta.reasoning = "Thinking about the greeting..."
+    mock_delta.tool_calls = None
+
+    mock_choice = Mock()
+    mock_choice.delta = mock_delta
+    mock_choice.finish_reason = None
+
+    mock_chunk.choices = [mock_choice]
+    mock_chunk.usage = None
+
+    result = _create_openai_chunk_from_cerebras_chunk(mock_chunk)
+
+    assert result.choices[0].delta.content == "Hello!"
+    assert result.choices[0].delta.reasoning is not None
+    assert result.choices[0].delta.reasoning.content == "Thinking about the greeting..."
+
+
+def test_convert_chunk_without_reasoning() -> None:
+    from unittest.mock import Mock
+
+    mock_chunk = Mock()
+    mock_chunk.id = "test-chunk-id"
+    mock_chunk.model = "llama-3.3-70b"
+    mock_chunk.created = 1234567890
+
+    mock_delta = Mock()
+    mock_delta.content = "Hello!"
+    mock_delta.role = "assistant"
+    mock_delta.reasoning = None
+    mock_delta.tool_calls = None
+
+    mock_choice = Mock()
+    mock_choice.delta = mock_delta
+    mock_choice.finish_reason = None
+
+    mock_chunk.choices = [mock_choice]
+    mock_chunk.usage = None
+
+    result = _create_openai_chunk_from_cerebras_chunk(mock_chunk)
+
+    assert result.choices[0].delta.content == "Hello!"
+    assert "reasoning" not in result.choices[0].delta.model_dump(exclude_none=True)