feat: add MiniMax as LLM provider for LangGraph and Semantic Kernel agents

samples/python/agents/langgraph/README.md

@@ -65,16 +65,19 @@ sequenceDiagram
 2. Create an environment file with your API key:
 
    ```bash
-   If you're using a Google Gemini model (gemini-pro, etc.):
+   # If you're using a Google Gemini model (gemini-pro, etc.):
    echo "GOOGLE_API_KEY=your_api_key_here" > .env
-
-
-   If you're using OpenAI or any compatible API (e.g., local LLM via Ollama, LM Studio, etc.):
 
-   echo "API_KEY=your_api_key_here" > .env  (not neccessary if have no api key)
-   echo "TOOL_LLM_URL=your_llm_url" > .env
-   echo "TOOL_LLM_NAME=your_llm_name" > .env
+   # If you're using MiniMax (https://platform.minimax.io):
+   echo "model_source=minimax" >> .env
+   echo "MINIMAX_API_KEY=your_minimax_api_key_here" >> .env
+   # Optionally override the model (default: MiniMax-M2.7):
+   # echo "TOOL_LLM_NAME=MiniMax-M2.7-highspeed" >> .env
 
+   # If you're using OpenAI or any compatible API (e.g., local LLM via Ollama, LM Studio, etc.):
+   echo "API_KEY=your_api_key_here" > .env  # not necessary if have no api key
+   echo "TOOL_LLM_URL=your_llm_url" >> .env
+   echo "TOOL_LLM_NAME=your_llm_name" >> .env
    ```
 
 3. Run the agent:
@@ -480,6 +483,7 @@ data: {"id":"6d12d159-ec67-46e6-8d43-18480ce7f6ca","jsonrpc":"2.0","result":{"co
 - [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)
 - [Frankfurter API](https://www.frankfurter.app/docs/)
 - [Google Gemini API](https://ai.google.dev/gemini-api)
+- [MiniMax API (OpenAI Compatible)](https://platform.minimax.io/docs/api-reference/text-openai-api)
 
 
 ## Disclaimer

samples/python/agents/langgraph/app/agent.py

@@ -1,11 +1,13 @@
 import os
+import re
 
 from collections.abc import AsyncIterable
 from typing import Any, Literal
 
 import httpx
 
-from langchain_core.messages import AIMessage, ToolMessage
+from langchain_core.messages import AIMessage, BaseMessage, ToolMessage
+from langchain_core.outputs import ChatResult
 from langchain_core.tools import tool
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_openai import ChatOpenAI
@@ -14,6 +16,41 @@
 from pydantic import BaseModel
 
 
+_THINK_TAG_RE = re.compile(r'<think>[\s\S]*?</think>\s*', re.DOTALL)
+
+
+class ChatMiniMax(ChatOpenAI):
+    """ChatOpenAI subclass for MiniMax.
+
+    MiniMax models may produce <think>...</think> reasoning tags.
+    This subclass strips those tags from responses and uses
+    function_calling for structured output (MiniMax does not support
+    json_schema response_format).
+    """
+
+    def with_structured_output(self, schema, *, method=None, **kwargs):
+        return super().with_structured_output(
+            schema, method='function_calling', **kwargs
+        )
+
+    def _generate(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager=None,
+        **kwargs,
+    ) -> ChatResult:
+        result = super()._generate(
+            messages, stop=stop, run_manager=run_manager, **kwargs
+        )
+        for gen in result.generations:
+            if gen.message and isinstance(gen.message.content, str):
+                gen.message.content = _THINK_TAG_RE.sub(
+                    '', gen.message.content
+                ).strip()
+        return result
+
+
 memory = MemorySaver()
 
 
@@ -80,6 +117,17 @@ def __init__(self):
         model_source = os.getenv('model_source', 'google')
         if model_source == 'google':
             self.model = ChatGoogleGenerativeAI(model='gemini-2.0-flash')
+        elif model_source == 'minimax':
+            self.model = ChatMiniMax(
+                model=os.getenv(
+                    'TOOL_LLM_NAME', 'MiniMax-M2.7'
+                ),
+                openai_api_key=os.getenv('MINIMAX_API_KEY', 'EMPTY'),
+                openai_api_base=os.getenv(
+                    'TOOL_LLM_URL', 'https://api.minimax.io/v1'
+                ),
+                temperature=1.0,
+            )
         else:
             self.model = ChatOpenAI(
                 model=os.getenv('TOOL_LLM_NAME'),