Python: Allow plugins via agent constructors. Update samples.

[moonbox3]

Motivation and Context

In Semantic Kernel, our goal is to simplify working with agents. When an agent is created without a specified kernel, one is automatically created on the user’s behalf. This enables developers to pass in a list of plugins that are then added to the underlying kernel. If both the agent constructor and the provided kernel include plugins, those specified via the agent constructor take precedence—since plugins are stored in a dictionary, any duplicate names result in the agent’s plugin overwriting the existing one.

For the ChatCompletionAgent, usage is further simplified by allowing a ChatCompletionClientBase service to be passed via the constructor. If a kernel is supplied and already contains a chat completion service, the new service is added to the kernel. Moreover, if no execution settings are provided via KernelArguments, the first service registered on the kernel is used.

New usage to create a ChatCompletionAgent

agent = ChatCompletionAgent(
    service=AzureChatCompletion(),
    name="<name>",
    instructions="<instructions>",
)

New usage to create a ChatCompletionAgent with plugins:

agent = ChatCompletionAgent(
    service=AzureChatCompletion(),
    name="<name>",
    instructions="<instructions>",
    plugins=[SamplePlugin()],
)

Previously, the service_id constructor argument was retained after introducing KernelArguments. However, with our transition to using the kernel’s AI service selector, the service_id parameter has become redundant. As we move from the experimental phase toward a release candidate, removing service_id is a necessary, albeit breaking, change.

The getting_started_with_agents/chat_completion samples now begin by demonstrating the simplest way to configure the agent. The subsequent step illustrates the original method for setting up the chat completion service on the kernel. Similarly, for plugins, the initial approach is the easiest to follow, while the following step shows the traditional method of managing the kernel.

Updated documentation will follow, once these changes are released in a new package.

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Description

Simplifying interactions with agents.

• Updated the ChatCompletionAgent, AzureAssistantAgent, and OpenAIAssistantAgent classes with the release_candidate decorator in place of the experimental decorator.
• Add a chat completion agent structured outputs sample
• Closes Python: ChatCompletionAgent constructor and invocation improvements #10604

Contribution Checklist

• The code builds clean without any errors or warnings
• The PR follows the SK Contribution Guidelines and the pre-submission formatting script raises no violations
• All unit tests pass, and I have added new tests where possible
• I didn't break anyone 😄

[markwallace-microsoft]

Python Test Coverage Report •

File	Stmts	Miss	Cover	Missing
semantic_kernel/agents/autogen
autogen_conversable_agent.py	74	7	91%	88, 184–185, 194–197
semantic_kernel/agents/azure_ai
azure_ai_agent.py	118	15	87%	109, 111, 117, 128–133, 135, 137, 158–161, 210, 275, 336
semantic_kernel/agents/bedrock
bedrock_agent.py	219	41	81%	119, 133, 137, 139, 188–201, 267, 291, 308, 341, 360, 376–379, 385, 387, 390, 394, 426, 456–465, 476, 508, 525–526, 528–529, 571, 582–583, 595, 631–642, 653, 670–683
semantic_kernel/agents/channels
chat_history_channel.py	68	2	97%	139, 142
semantic_kernel/agents/chat_completion
chat_completion_agent.py	148	10	93%	98, 101, 111, 119–124, 133, 215, 288, 357
semantic_kernel/agents/open_ai
azure_assistant_agent.py	35	2	94%	79, 83
open_ai_assistant_agent.py	185	6	97%	213, 226, 262, 432, 523, 611
TOTAL	19018	2380	87%

Python Unit Test Overview

Tests	Skipped	Failures	Errors	Time
3199	5 💤	0 ❌	0 🔥	1m 27s ⏱️