Small fixes (#969)

python/packages/main/agent_framework/_clients.py

@@ -212,6 +212,17 @@ class BaseChatClient(AFBaseModel, ABC):
         """Turn the allowed input into a list of chat messages."""
         return prepare_messages(messages)
 
+    def _filter_internal_kwargs(self, kwargs: dict[str, Any]) -> dict[str, Any]:
+        """Filter out internal framework parameters that shouldn't be passed to chat client implementations.
+
+        Args:
+            kwargs: The original kwargs dictionary.
+
+        Returns:
+            A filtered kwargs dictionary without internal parameters.
+        """
+        return {k: v for k, v in kwargs.items() if not k.startswith("_")}
+
     @staticmethod
     def _normalize_tools(
         tools: ToolProtocol
@@ -360,7 +371,8 @@ class BaseChatClient(AFBaseModel, ABC):
         prepped_messages = self.prepare_messages(messages)
         self._prepare_tool_choice(chat_options=chat_options)
 
-        return await self._inner_get_response(messages=prepped_messages, chat_options=chat_options, **kwargs)
+        filtered_kwargs = self._filter_internal_kwargs(kwargs)
+        return await self._inner_get_response(messages=prepped_messages, chat_options=chat_options, **filtered_kwargs)
 
     async def get_streaming_response(
         self,
@@ -440,8 +452,9 @@ class BaseChatClient(AFBaseModel, ABC):
         prepped_messages = self.prepare_messages(messages)
         self._prepare_tool_choice(chat_options=chat_options)
 
+        filtered_kwargs = self._filter_internal_kwargs(kwargs)
         async for update in self._inner_get_streaming_response(
-            messages=prepped_messages, chat_options=chat_options, **kwargs
+            messages=prepped_messages, chat_options=chat_options, **filtered_kwargs
         ):
             yield update
 

python/samples/getting_started/agents/README.md

@@ -1,6 +1,6 @@
 # Agent Examples
 
-This folder contains examples demonstrating how to create and use agents with different chat clients from the Agent Framework. Each sub-folder focuses on a specific provider and client type, showing various capabilities like function tools, code interpreter, thread management, and more.
+This folder contains examples demonstrating how to create and use agents with different chat clients from the Agent Framework. Each sub-folder focuses on a specific provider and client type, showing various capabilities like function tools, code interpreter, thread management, structured outputs, image processing, web search, Model Context Protocol (MCP) integration, and more.
 
 ## Examples by Provider
 
@@ -8,29 +8,34 @@ This folder contains examples demonstrating how to create and use agents with di
 
 | Folder | Description |
 |--------|-------------|
-| **[`azure_ai/`](azure_ai/)** | Create agents using Azure AI Foundry Agent Service |
+| **[`azure_ai/`](azure_ai/)** | Create agents using Azure AI Foundry Agent Service with various tools including function tools, code interpreter, MCP integration, and thread management |
 
 ### Microsoft Copilot Studio Examples
 
 | Folder | Description |
 |--------|-------------|
-| **[`copilotstudio/`](copilotstudio/)** | Create agents using Microsoft Copilot Studio |
+| **[`copilotstudio/`](copilotstudio/)** | Create agents using Microsoft Copilot Studio with streaming and non-streaming responses, authentication handling, and explicit configuration options |
 
 ### Azure OpenAI Examples
 
 | Folder | Description |
 |--------|-------------|
-| **[`azure_openai/`](azure_openai/)** | Create agents using Azure OpenAI APIs |
+| **[`azure_openai/`](azure_openai/)** | Create agents using Azure OpenAI APIs with multiple client types (Assistants, Chat, and Responses clients) supporting function tools, code interpreter, thread management, and more |
 
 ### OpenAI Examples
 
 | Folder | Description |
 |--------|-------------|
-| **[`openai/`](openai/)** | Create agents using OpenAI APIs |
+| **[`openai/`](openai/)** | Create agents using OpenAI APIs with comprehensive examples including Assistants, Chat, and Responses clients featuring function tools, code interpreter, file search, web search, MCP integration, image analysis/generation, structured outputs, reasoning, and thread management |
 
-### Custom Client Examples
+### Anthropic Examples
 
 | Folder | Description |
 |--------|-------------|
-| **[`custom_client/`](custom_client/)** | Create agents using a custom chat client or a custom agent |
-| **[`anthropic/`](anthropic/)** | Create agents using Anthropic APIs |
+| **[`anthropic/`](anthropic/)** | Create agents using Anthropic models through OpenAI Chat Client configuration, demonstrating tool calling capabilities |
+
+### Custom Implementation Examples
+
+| Folder | Description |
+|--------|-------------|
+| **[`custom/`](custom/)** | Create custom agents and chat clients by extending the base framework classes, showing complete control over agent behavior and backend integration |

python/samples/getting_started/agents/azure_ai/azure_ai_with_existing_agent.py

@@ -37,7 +37,7 @@ async def main() -> None:
             async with ChatAgent(
                 # passing in the client is optional here, so if you take the agent_id from the portal
                 # you can use it directly without the two lines above.
-                chat_client=AzureAIAgentClient(client=client, agent_id=created_agent.id),
+                chat_client=AzureAIAgentClient(project_client=client, agent_id=created_agent.id),
                 instructions="You are a helpful weather agent.",
                 tools=get_weather,
             ) as agent:

python/samples/getting_started/multimodal_input/azure_chat_multimodal.py

@@ -6,11 +6,12 @@ import base64
 import requests
 from agent_framework import ChatMessage, DataContent, Role, TextContent
 from agent_framework.azure import AzureOpenAIChatClient
+from azure.identity import AzureCliCredential
 
 
-async def test_image():
+async def test_image() -> None:
     """Test image analysis with Azure."""
-    client = AzureOpenAIChatClient()
+    client = AzureOpenAIChatClient(credential=AzureCliCredential())
 
     # Fetch image from httpbin
     image_url = "https://httpbin.org/image/jpeg"
@@ -27,7 +28,7 @@ async def test_image():
     print(f"Image Response: {response}")
 
 
-async def main():
+async def main() -> None:
     print("=== Testing Azure Multimodal ===")
     await test_image()
 

python/samples/getting_started/multimodal_input/openai_chat_multimodal.py

@@ -2,12 +2,14 @@
 
 import asyncio
 import base64
-import requests
 import struct
+
+import requests
 from agent_framework import ChatMessage, DataContent, Role, TextContent
 from agent_framework.openai import OpenAIChatClient
 
-async def test_image():
+
+async def test_image() -> None:
     """Test image analysis with OpenAI."""
     client = OpenAIChatClient(ai_model_id="gpt-4o")
 
@@ -19,26 +21,27 @@ async def test_image():
 
     message = ChatMessage(
         role=Role.USER,
-        contents=[
-            TextContent(text="What's in this image?"),
-            DataContent(uri=image_uri, media_type="image/jpeg")
-        ]
+        contents=[TextContent(text="What's in this image?"), DataContent(uri=image_uri, media_type="image/jpeg")],
     )
 
     response = await client.get_response(message)
     print(f"Image Response: {response}")
 
-async def test_audio():
+
+async def test_audio() -> None:
     """Test audio analysis with OpenAI."""
     client = OpenAIChatClient(ai_model_id="gpt-4o-audio-preview")
 
     # Create minimal WAV file (0.1 seconds of silence)
     wav_header = (
-        b'RIFF' + struct.pack('<I', 44) +  # file size
-        b'WAVEfmt ' + struct.pack('<I', 16) +  # fmt chunk
-        struct.pack('<HHIIHH', 1, 1, 8000, 16000, 2, 16) +  # PCM, mono, 8kHz
-        b'data' + struct.pack('<I', 1600) +  # data chunk
-        b'\x00' * 1600  # 0.1 sec silence
+        b"RIFF"
+        + struct.pack("<I", 44)  # file size
+        + b"WAVEfmt "
+        + struct.pack("<I", 16)  # fmt chunk
+        + struct.pack("<HHIIHH", 1, 1, 8000, 16000, 2, 16)  # PCM, mono, 8kHz
+        + b"data"
+        + struct.pack("<I", 1600)  # data chunk
+        + b"\x00" * 1600  # 0.1 sec silence
     )
     audio_b64 = base64.b64encode(wav_header).decode()
     audio_uri = f"data:audio/wav;base64,{audio_b64}"
@@ -47,17 +50,19 @@ async def test_audio():
         role=Role.USER,
         contents=[
             TextContent(text="What do you hear in this audio?"),
-            DataContent(uri=audio_uri, media_type="audio/wav")
-        ]
+            DataContent(uri=audio_uri, media_type="audio/wav"),
+        ],
     )
 
     response = await client.get_response(message)
     print(f"Audio Response: {response}")
 
-async def main():
+
+async def main() -> None:
     print("=== Testing OpenAI Multimodal ===")
     await test_image()
     await test_audio()
 
+
 if __name__ == "__main__":
     asyncio.run(main())