feat(minimax): Add Minimax provider support

.github/workflows/tests-integration.yaml

@@ -23,7 +23,7 @@ jobs:
     steps:
       - name: Set expected providers for integration tests
         id: set_providers
-        run: echo "expected_providers=anthropic,bedrock,cerebras,cohere,databricks,deepseek,fireworks,gemini,groq,huggingface,inception,llama,mistral,moonshot,nebius,openai,openrouter,perplexity,portkey,together,sambanova,voyage,xai,zai" >> $GITHUB_OUTPUT
+        run: echo "expected_providers=anthropic,bedrock,cerebras,cohere,databricks,deepseek,fireworks,gemini,groq,huggingface,inception,llama,mistral,moonshot,nebius,openai,openrouter,perplexity,portkey,together,sambanova,voyage,xai,zai,minimax" >> $GITHUB_OUTPUT
 
       - name: Set expected providers for local integration tests
         id: set_local_providers
@@ -79,6 +79,7 @@ jobs:
           VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
           XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
           ZAI_API_KEY: ${{ secrets.ZAI_API_KEY }}
+          MINIMAX_API_KEY: ${{ secrets.MINIMAX_API_KEY }}
           EXPECTED_PROVIDERS: ${{ needs.expected-providers.outputs.providers }}
           INCLUDE_LOCAL_PROVIDERS: "false"
         run: |

pyproject.toml

@@ -19,7 +19,7 @@ dependencies = [
 [project.optional-dependencies]
 
 all = [
-  "any-llm-sdk[mistral,anthropic,huggingface,gemini,vertexai,cohere,cerebras,fireworks,groq,bedrock,azure,azureopenai,watsonx,together,sambanova,ollama,moonshot,nebius,xai,databricks,deepseek,inception,openai,openrouter,portkey,lmstudio,llama,voyage,perplexity,llamafile,llamacpp,sagemaker,gateway,zai]"
+  "any-llm-sdk[mistral,anthropic,huggingface,gemini,vertexai,cohere,cerebras,fireworks,groq,bedrock,azure,azureopenai,watsonx,together,sambanova,ollama,moonshot,nebius,xai,databricks,deepseek,inception,openai,openrouter,portkey,lmstudio,llama,voyage,perplexity,llamafile,llamacpp,sagemaker,gateway,zai,minimax]"
 ]
 
 perplexity = []
@@ -105,6 +105,7 @@ openai = []
 openrouter = []
 portkey = []
 sambanova = []
+minimax = []
 gateway = []
 zai = []
 

src/any_llm/constants.py

@@ -48,6 +48,7 @@ class LLMProvider(StrEnum):
     WATSONX = "watsonx"
     XAI = "xai"
     PERPLEXITY = "perplexity"
+    MINIMAX = "minimax"
     ZAI = "zai"
     GATEWAY = "gateway"
 

src/any_llm/providers/minimax/__init__.py

@@ -0,0 +1,3 @@
+from .minimax import MinimaxProvider
+
+__all__ = ["MinimaxProvider"]

src/any_llm/providers/minimax/minimax.py

@@ -0,0 +1,65 @@
+from collections.abc import AsyncIterator
+from typing import Any
+
+from openai._streaming import AsyncStream
+from openai.types.chat.chat_completion import ChatCompletion as OpenAIChatCompletion
+from openai.types.chat.chat_completion_chunk import ChatCompletionChunk as OpenAIChatCompletionChunk
+
+from any_llm.exceptions import UnsupportedParameterError
+from any_llm.providers.openai.base import BaseOpenAIProvider
+from any_llm.types.completion import ChatCompletion, ChatCompletionChunk, CompletionParams, Reasoning
+from any_llm.utils.reasoning import process_streaming_reasoning_chunks
+
+
+class MinimaxProvider(BaseOpenAIProvider):
+    API_BASE = "https://api.minimax.io/v1"
+    ENV_API_KEY_NAME = "MINIMAX_API_KEY"
+    PROVIDER_NAME = "minimax"
+    PROVIDER_DOCUMENTATION_URL = "https://www.minimax.io/platform_overview"
+
+    SUPPORTS_COMPLETION_PDF = False
+    SUPPORTS_COMPLETION_REASONING = True
+    SUPPORTS_COMPLETION_IMAGE = False
+    SUPPORTS_LIST_MODELS = False
+    SUPPORTS_EMBEDDING = False
+
+    def _convert_completion_response_async(
+        self, response: OpenAIChatCompletion | AsyncStream[OpenAIChatCompletionChunk]
+    ) -> ChatCompletion | AsyncIterator[ChatCompletionChunk]:
+        """Convert an OpenAI completion response with streaming reasoning support."""
+        if isinstance(response, OpenAIChatCompletion):
+            return self._convert_completion_response(response)
+
+        async def chunk_iterator() -> AsyncIterator[ChatCompletionChunk]:
+            async for chunk in response:
+                yield self._convert_completion_chunk_response(chunk)
+
+        def get_content(chunk: ChatCompletionChunk) -> str | None:
+            return chunk.choices[0].delta.content if len(chunk.choices) > 0 else None
+
+        def set_content(chunk: ChatCompletionChunk, content: str | None) -> ChatCompletionChunk:
+            chunk.choices[0].delta.content = content
+            return chunk
+
+        def set_reasoning(chunk: ChatCompletionChunk, reasoning: str) -> ChatCompletionChunk:
+            chunk.choices[0].delta.reasoning = Reasoning(content=reasoning)
+            return chunk
+
+        return process_streaming_reasoning_chunks(
+            chunk_iterator(),
+            get_content=get_content,
+            set_content=set_content,
+            set_reasoning=set_reasoning,
+        )
+
+    @staticmethod
+    def _convert_completion_params(params: CompletionParams, **kwargs: Any) -> dict[str, Any]:
+        # response_format is supported in the z.ai SDK, but the SDK doesn't yet have an async client
+        # so we can't use it in any-llm
+        if params.response_format is not None:
+            param = "response_format"
+            raise UnsupportedParameterError(param, "minimax")
+        # Copy of the logic from the base implementation because you can't use super() in a static method
+        converted_params = params.model_dump(exclude_none=True, exclude={"model_id", "messages"})
+        converted_params.update(kwargs)
+        return converted_params

tests/conftest.py

@@ -36,6 +36,7 @@ def provider_reasoning_model_map() -> dict[LLMProvider, str]:
         LLMProvider.SAMBANOVA: "DeepSeek-R1-Distill-Llama-70B",
         LLMProvider.TOGETHER: "OpenAI/gpt-oss-20B",
         LLMProvider.PORTKEY: "@anthropic/claude-3-7-sonnet-latest",
+        LLMProvider.MINIMAX: "MiniMax-M2",
         LLMProvider.ZAI: "glm-4.5-flash",
     }
 
@@ -76,6 +77,7 @@ def provider_model_map() -> dict[LLMProvider, str]:
         LLMProvider.PERPLEXITY: "sonar",
         LLMProvider.OPENROUTER: "meta-llama/llama-3.3-8b-instruct:free",
         LLMProvider.LLAMACPP: "N/A",
+        LLMProvider.MINIMAX: "MiniMax-M2",
         LLMProvider.ZAI: "glm-4-32b-0414-128k",
     }