Python: Pass through arguments when creating agents from specs

python/samples/getting_started_with_agents/chat_completion/step11_chat_completion_agent_declarative.py

@@ -3,19 +3,35 @@
 import asyncio
 from typing import Annotated
 
+from pydantic import BaseModel
+
 from semantic_kernel import Kernel
 from semantic_kernel.agents import AgentRegistry, ChatHistoryAgentThread
 from semantic_kernel.agents.chat_completion.chat_completion_agent import ChatCompletionAgent
-from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
-from semantic_kernel.functions import kernel_function
+from semantic_kernel.connectors.ai.open_ai import (
+    AzureChatCompletion,
+    AzureChatPromptExecutionSettings,
+)
+from semantic_kernel.functions import KernelArguments, kernel_function
 
 """
 The following sample demonstrates how to create a chat completion agent using a 
 declarative approach. The Chat Completion Agent is created from a YAML spec,
 with a specific service and plugins. The agent is then used to answer user questions.
+
+This sample also demonstrates how to properly pass execution settings (like response format)
+when using AgentRegistry.create_from_yaml().
 """
 
 
+# Example structure for structured output
+class StructuredResult(BaseModel):
+    """Example structure for demonstrating response format."""
+
+    response: str
+    category: str
+
+
 # 1. Define a Sample Plugin
 class MenuPlugin:
     """A sample Menu Plugin used for the concept sample."""
@@ -66,24 +82,44 @@ async def main():
     kernel = Kernel()
     kernel.add_plugin(MenuPlugin(), plugin_name="MenuPlugin")
 
-    # 5. Create the agent from YAML + inject the AI service
+    # 5. Create execution settings with structured output
+    execution_settings = AzureChatPromptExecutionSettings()
+    execution_settings.response_format = StructuredResult
+
+    # 6. Create KernelArguments with the execution settings
+    arguments = KernelArguments(settings=execution_settings)
+
+    # 7. Create the agent from YAML + inject the AI service
     agent: ChatCompletionAgent = await AgentRegistry.create_from_yaml(
-        AGENT_YAML, kernel=kernel, service=OpenAIChatCompletion()
+        AGENT_YAML, kernel=kernel, service=AzureChatCompletion(), arguments=arguments
     )
 
-    # 6. Create a thread to hold the conversation
+    # 8. Create a thread to hold the conversation
     thread: ChatHistoryAgentThread | None = None
 
     for user_input in USER_INPUTS:
         print(f"# User: {user_input}")
-        # 7. Invoke the agent for a response
+        # 9. Invoke the agent for a response
         response = await agent.get_response(messages=user_input, thread=thread)
         print(f"# {response.name}: {response}")
         thread = response.thread
 
-    # 8. Cleanup the thread
+    # 10. Cleanup the thread
     await thread.delete() if thread else None
 
+    """
+    # Sample output:
+
+    # User: Hello
+    # Assistant: {"response":"Hello! How can I help you today? If you have any questions about the menu, feel free to ask!","category":"Greeting"}
+    # User: What is the special soup?
+    # Assistant: {"response":"Today's special soup is Clam Chowder. Would you like to know more about it or see other specials?","category":"Menu Specials"}
+    # User: What does that cost?
+    # Assistant: {"response":"The Clam Chowder special soup costs $9.99.","category":"Menu Pricing"}
+    # User: Thank you
+    # Assistant: {"response":"You're welcome! If you have any more questions or need assistance with the menu, just let me know. Enjoy your meal!","category":"Polite Closing"}
+    """  # noqa: E501
+
 
 if __name__ == "__main__":
     asyncio.run(main())

python/semantic_kernel/agents/azure_ai/azure_ai_agent.py

@@ -501,6 +501,11 @@ async def _from_dict(
         if args:
             arguments = KernelArguments(**args)
 
+        # Handle arguments from kwargs, merging with any arguments from data
+        if "arguments" in kwargs and kwargs["arguments"] is not None:
+            incoming_args = kwargs["arguments"]
+            arguments = arguments | incoming_args if arguments is not None else incoming_args
+
         if spec.id:
             existing_definition = await client.agents.get_agent(spec.id)
 

python/semantic_kernel/agents/chat_completion/chat_completion_agent.py

@@ -255,6 +255,15 @@ async def _from_dict(
         if "function_choice_behavior" in kwargs:
             fields["function_choice_behavior"] = kwargs["function_choice_behavior"]
 
+        # Handle arguments from kwargs, merging with any arguments from _normalize_spec_fields
+        if "arguments" in kwargs and kwargs["arguments"] is not None:
+            incoming_args = kwargs["arguments"]
+            if fields.get("arguments") is not None:
+                # Use KernelArguments' built-in merge operator, with incoming_args taking precedence
+                fields["arguments"] = fields["arguments"] | incoming_args
+            else:
+                fields["arguments"] = incoming_args
+
         return cls(**fields, kernel=kernel)
 
     # endregion

python/semantic_kernel/agents/open_ai/openai_assistant_agent.py

@@ -466,6 +466,11 @@ async def _from_dict(
         if args:
             arguments = KernelArguments(**args)
 
+        # Handle arguments from kwargs, merging with any arguments from data
+        if "arguments" in kwargs and kwargs["arguments"] is not None:
+            incoming_args = kwargs["arguments"]
+            arguments = arguments | incoming_args if arguments is not None else incoming_args
+
         if spec.id:
             existing_definition = await client.beta.assistants.retrieve(spec.id)
 

python/semantic_kernel/agents/open_ai/openai_responses_agent.py

@@ -537,6 +537,11 @@ async def _from_dict(
         if args:
             arguments = KernelArguments(**args)
 
+        # Handle arguments from kwargs, merging with any arguments from data
+        if "arguments" in kwargs and kwargs["arguments"] is not None:
+            incoming_args = kwargs["arguments"]
+            arguments = arguments | incoming_args if arguments is not None else incoming_args
+
         if not (spec.model and spec.model.id):
             raise AgentInitializationException("model.id required when creating a new OpenAI Responses Agent.")