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Python: [BREAKING] Python: Provider-leading client design & OpenAI package extraction (#4818)
* Python: Provider-leading client design & OpenAI package extraction Major refactoring of the Python Agent Framework client architecture: - Extract OpenAI clients into new `agent-framework-openai` package - Core package no longer depends on openai, azure-identity, azure-ai-projects - Rename clients for discoverability: OpenAIResponsesClient → OpenAIChatClient, OpenAIChatClient → OpenAIChatCompletionClient - Unify `model_id`/`deployment_name`/`model_deployment_name` → `model` param - New FoundryChatClient for Azure AI Foundry Responses API - New FoundryAgent/FoundryAgentClient for connecting to pre-configured Foundry agents - Remove OpenAIBase/OpenAIConfigMixin from non-deprecated client MRO - Deprecate AzureOpenAI* clients, AzureAIClient, OpenAIAssistantsClient - Reorganize samples: azure_openai+azure_ai+azure_ai_agent → azure/ - ADR-0020: Provider-Leading Client Design Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: missing Agent imports in samples, .model_id → .model in foundry_local sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: CI failures — mypy errors, coverage targets, sample imports - azure-ai mypy: add type ignores for TypedDict total=, model arg, forward ref - Coverage: replace core.azure/openai targets with openai package target - project_provider: add type annotation for opts dict Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: populate openai .pyi stub, fix broken README links, coverage targets Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fixes * updated observabilitty * reset azure init.pyi * fix errors * updated adr number * fix foundry local * fixed not renamed docstrings and comments, and added deprecated markers to old classes * fix tests and pyprojects * fix test vars * updated function tests * update durable * updated test setup for functions * Fix Foundry auth in workflow samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Stabilize Python integration workflows Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Update hosting samples for Foundry Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger full CI rerun Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger CI rerun again Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * trigger rerun * trigger rerun * fix for litellm * undo durabletask changes * Move Foundry APIs into foundry namespace Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix Foundry pyproject formatting Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Split provider samples by Foundry surface Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Restore hosting sample requirements Also fix the Foundry Local sample link after the provider sample move. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated tests * udpated foundry integration tests * removed dist from azurefunctions tests * Use separate Foundry clients for concurrent agents Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix client setup in azfunc and durable * disabled two tests * updated setup for some function and durable tests * improved azure openai setup with new clients * ignore deprecated * fixes * skip 11 * remove openai assistants int tests --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
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5e056b672e
@@ -12,9 +12,6 @@ This gallery helps Semantic Kernel (SK) developers move to the Microsoft Agent F
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- [03_chat_completion_thread_and_stream.py](chat_completion/03_chat_completion_thread_and_stream.py) — Demonstrates session reuse and streaming prompts.
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### Azure AI agent parity
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- [01_basic_azure_ai_agent.py](azure_ai_agent/01_basic_azure_ai_agent.py) — Create and run an Azure AI agent end to end.
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- [02_azure_ai_agent_with_code_interpreter.py](azure_ai_agent/02_azure_ai_agent_with_code_interpreter.py) — Enable hosted code interpreter/tool execution.
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- [03_azure_ai_agent_threads_and_followups.py](azure_ai_agent/03_azure_ai_agent_threads_and_followups.py) — Persist sessions and follow-ups across invocations.
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### OpenAI Assistants API parity
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- [01_basic_openai_assistant.py](openai_assistant/01_basic_openai_assistant.py) — Baseline assistant comparison.
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@@ -1,65 +0,0 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "semantic-kernel",
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# ]
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# ///
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# Run with any PEP 723 compatible runner, e.g.:
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# uv run samples/semantic-kernel-migration/azure_ai_agent/01_basic_azure_ai_agent.py
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# Copyright (c) Microsoft. All rights reserved.
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"""Create an Azure AI agent using both Semantic Kernel and Agent Framework.
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Prerequisites:
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- Azure AI agent resource with a deployed model.
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- Logged-in Azure CLI or other credential supported by AzureCliCredential.
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"""
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import asyncio
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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async def run_semantic_kernel() -> None:
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from azure.identity.aio import AzureCliCredential
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from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings
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async with AzureCliCredential() as credential, AzureAIAgent.create_client(credential=credential) as client:
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settings = AzureAIAgentSettings() # Reads env vars for region/deployment.
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# SK builds the remote agent definition then wraps it with AzureAIAgent.
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definition = await client.agents.create_agent(
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model=settings.model_deployment_name,
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name="Support",
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instructions="Answer customer questions in one paragraph.",
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)
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agent = AzureAIAgent(client=client, definition=definition)
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response = await agent.get_response("How do I upgrade my plan?")
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print("[SK]", response.message.content)
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async def run_agent_framework() -> None:
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from agent_framework.azure import AzureAIAgentClient
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from azure.identity.aio import AzureCliCredential
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentClient(credential=credential).as_agent(
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name="Support",
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instructions="Answer customer questions in one paragraph.",
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) as agent,
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):
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# AF client returns an asynchronous context manager for remote agents.
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reply = await agent.run("How do I upgrade my plan?")
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print("[AF]", reply.text)
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async def main() -> None:
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await run_semantic_kernel()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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-76
@@ -1,76 +0,0 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "semantic-kernel",
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# ]
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# ///
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# Run with any PEP 723 compatible runner, e.g.:
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# uv run samples/semantic-kernel-migration/azure_ai_agent/02_azure_ai_agent_with_code_interpreter.py
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# Copyright (c) Microsoft. All rights reserved.
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"""Enable the hosted code interpreter for Azure AI agents in SK and AF.
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The Azure AI service natively executes the code interpreter tool. Provide the
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resource details via AzureAIAgentSettings (SK) or environment variables consumed
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by AzureAIAgentClient (AF).
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"""
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import asyncio
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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async def run_semantic_kernel() -> None:
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from azure.identity.aio import AzureCliCredential
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from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings
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async with AzureCliCredential() as credential, AzureAIAgent.create_client(credential=credential) as client:
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settings = AzureAIAgentSettings()
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# Register the hosted code interpreter tool with the remote agent.
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definition = await client.agents.create_agent(
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model=settings.model_deployment_name,
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name="Analyst",
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instructions="Use the code interpreter for numeric work.",
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tools=[{"type": "code_interpreter"}],
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)
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agent = AzureAIAgent(client=client, definition=definition)
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response = await agent.get_response(
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"Use Python to compute 42 ** 2 and explain the result.",
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)
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print("[SK]", response.message.content)
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async def run_agent_framework() -> None:
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from agent_framework.azure import AzureAIAgentClient, AzureAIAgentsProvider
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from azure.identity.aio import AzureCliCredential
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentsProvider(credential=credential) as provider,
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):
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code_interpreter_tool = AzureAIAgentClient.get_code_interpreter_tool()
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agent = await provider.create_agent(
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name="Analyst",
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instructions="Use the code interpreter for numeric work.",
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tools=[code_interpreter_tool],
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)
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# Code interpreter tool mirrors the built-in Azure AI capability.
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reply = await agent.run(
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"Use Python to compute 42 ** 2 and explain the result.",
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tool_choice="auto",
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)
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print("[AF]", reply.text)
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async def main() -> None:
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await run_semantic_kernel()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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-81
@@ -1,81 +0,0 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "semantic-kernel",
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# ]
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# ///
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# Run with any PEP 723 compatible runner, e.g.:
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# uv run samples/semantic-kernel-migration/azure_ai_agent/03_azure_ai_agent_threads_and_followups.py
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# Copyright (c) Microsoft. All rights reserved.
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"""Maintain Azure AI agent conversation state across turns in SK and AF."""
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import asyncio
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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async def run_semantic_kernel() -> None:
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from azure.identity.aio import AzureCliCredential
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from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings, AzureAIAgentThread
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async with AzureCliCredential() as credential, AzureAIAgent.create_client(credential=credential) as client:
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settings = AzureAIAgentSettings()
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definition = await client.agents.create_agent(
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model=settings.model_deployment_name,
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name="Planner",
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instructions="Track follow-up questions within the same thread.",
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)
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agent = AzureAIAgent(client=client, definition=definition)
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thread: AzureAIAgentThread | None = None
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# SK returns the updated AzureAIAgentThread on each response.
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first = await agent.get_response("Outline the onboarding checklist.", thread=thread)
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thread = first.thread
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print("[SK][turn1]", first.message.content)
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second = await agent.get_response(
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"Highlight the items that require legal review.",
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thread=thread,
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)
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print("[SK][turn2]", second.message.content)
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if thread is not None:
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print("[SK][thread-id]", thread.id)
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async def run_agent_framework() -> None:
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from agent_framework.azure import AzureAIAgentClient
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from azure.identity.aio import AzureCliCredential
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentClient(credential=credential).as_agent(
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name="Planner",
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instructions="Track follow-up questions within the same thread.",
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) as agent,
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):
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session = agent.create_session()
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# AF sessions are explicit and can be serialized for external storage.
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first = await agent.run("Outline the onboarding checklist.", session=session)
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print("[AF][turn1]", first.text)
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second = await agent.run(
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"Highlight the items that require legal review.",
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session=session,
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)
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print("[AF][turn2]", second.text)
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serialized = session.to_dict()
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print("[AF][session-json]", serialized)
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async def main() -> None:
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await run_semantic_kernel()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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+3
-1
@@ -17,6 +17,7 @@ model of choice before running.
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import asyncio
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from agent_framework import Agent
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from dotenv import load_dotenv
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# Load environment variables from .env file
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@@ -44,7 +45,8 @@ async def run_agent_framework() -> None:
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from agent_framework.openai import OpenAIChatClient
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# AF constructs a lightweight Agent backed by OpenAIChatClient.
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chat_agent = OpenAIChatClient().as_agent(
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chat_agent = Agent(
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client=OpenAIChatClient(),
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name="Support",
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instructions="Answer in one sentence.",
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)
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+3
-2
@@ -48,7 +48,7 @@ async def run_semantic_kernel() -> None:
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async def run_agent_framework() -> None:
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from agent_framework import tool
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from agent_framework import Agent, tool
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from agent_framework.openai import OpenAIChatClient
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@tool(name="specials", description="List daily specials")
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@@ -56,7 +56,8 @@ async def run_agent_framework() -> None:
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return "Clam chowder, Cobb salad, Chai tea"
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# AF tools are provided as callables on each agent instance.
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chat_agent = OpenAIChatClient().as_agent(
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chat_agent = Agent(
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client=OpenAIChatClient(),
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name="Host",
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instructions="Answer menu questions accurately.",
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tools=[specials],
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+3
-1
@@ -16,6 +16,7 @@ for the second turn.
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import asyncio
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from agent_framework import Agent
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from dotenv import load_dotenv
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# Load environment variables from .env file
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@@ -54,7 +55,8 @@ async def run_agent_framework() -> None:
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from agent_framework.openai import OpenAIChatClient
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# AF session objects are requested explicitly from the agent.
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chat_agent = OpenAIChatClient().as_agent(
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chat_agent = Agent(
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client=OpenAIChatClient(),
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name="Writer",
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instructions="Keep answers short and friendly.",
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)
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+3
-9
@@ -9,21 +9,19 @@
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# Copyright (c) Microsoft. All rights reserved.
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"""Create an OpenAI Assistant using SK and Agent Framework."""
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import asyncio
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import os
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from agent_framework import Agent
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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ASSISTANT_MODEL = os.environ.get("OPENAI_ASSISTANT_MODEL", "gpt-4o-mini")
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async def run_semantic_kernel() -> None:
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from semantic_kernel.agents import AssistantAgentThread, OpenAIAssistantAgent
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client = OpenAIAssistantAgent.create_client()
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# Provision the assistant on the OpenAI Assistants service.
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definition = await client.beta.assistants.create(
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@@ -32,7 +30,6 @@ async def run_semantic_kernel() -> None:
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instructions="Answer questions in one concise paragraph.",
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)
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agent = OpenAIAssistantAgent(client=client, definition=definition)
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thread: AssistantAgentThread | None = None
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response = await agent.get_response("What is the capital of Denmark?", thread=thread)
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thread = response.thread
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@@ -43,13 +40,10 @@ async def run_semantic_kernel() -> None:
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async def run_agent_framework() -> None:
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from agent_framework.openai import OpenAIAssistantsClient
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assistants_client = OpenAIAssistantsClient()
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# AF wraps the assistant lifecycle with an async context manager.
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async with assistants_client.as_agent(
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name="Helper",
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instructions="Answer questions in one concise paragraph.",
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model=ASSISTANT_MODEL,
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async with Agent(
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client=assistants_client,
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) as assistant_agent:
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session = assistant_agent.create_session()
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reply = await assistant_agent.run("What is the capital of Denmark?", session=session)
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+3
-1
@@ -1,4 +1,5 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "semantic-kernel",
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@@ -12,6 +13,7 @@
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import asyncio
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from agent_framework import Agent
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from dotenv import load_dotenv
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# Load environment variables from .env file
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@@ -50,7 +52,7 @@ async def run_agent_framework() -> None:
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code_interpreter_tool = OpenAIAssistantsClient.get_code_interpreter_tool()
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# AF exposes the same tool configuration via create_agent.
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async with assistants_client.as_agent(
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async with Agent(client=assistants_client,
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name="CodeRunner",
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instructions="Use the code interpreter when calculations are required.",
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model="gpt-4.1",
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+2
-2
@@ -69,7 +69,7 @@ async def run_semantic_kernel() -> None:
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async def run_agent_framework() -> None:
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from agent_framework import tool
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from agent_framework import Agent, tool
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from agent_framework.openai import OpenAIAssistantsClient
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@tool(
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@@ -81,7 +81,7 @@ async def run_agent_framework() -> None:
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assistants_client = OpenAIAssistantsClient()
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# AF converts the decorated function into an assistant-compatible tool.
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async with assistants_client.as_agent(
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async with Agent(client=assistants_client,
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name="WeatherHelper",
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instructions="Call get_forecast to fetch weather details.",
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model=ASSISTANT_MODEL,
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+1
-1
@@ -25,7 +25,7 @@ async def run_semantic_kernel() -> None:
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client = OpenAIResponsesAgent.create_client()
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# SK response agents wrap OpenAI's hosted Responses API.
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agent = OpenAIResponsesAgent(
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ai_model_id=OpenAISettings().responses_model_id,
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ai_model=OpenAISettings().responses_model_id,
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client=client,
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instructions="Answer in one concise sentence.",
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name="Expert",
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+1
-1
@@ -31,7 +31,7 @@ async def run_semantic_kernel() -> None:
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client = OpenAIResponsesAgent.create_client()
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# Plugins advertise callable tools to the Responses agent.
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agent = OpenAIResponsesAgent(
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ai_model_id=OpenAISettings().responses_model_id,
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ai_model=OpenAISettings().responses_model_id,
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client=client,
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instructions="Use the add tool when math is required.",
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name="MathExpert",
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+1
-1
@@ -32,7 +32,7 @@ async def run_semantic_kernel() -> None:
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client = OpenAIResponsesAgent.create_client()
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# response_format requests schema-constrained output from the model.
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agent = OpenAIResponsesAgent(
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ai_model_id=OpenAISettings().responses_model_id,
|
||||
ai_model=OpenAISettings().responses_model_id,
|
||||
client=client,
|
||||
instructions="Return launch briefs as structured JSON.",
|
||||
name="ProductMarketer",
|
||||
|
||||
@@ -15,7 +15,7 @@ import asyncio
|
||||
from collections.abc import Sequence
|
||||
from typing import cast
|
||||
|
||||
from agent_framework import Message
|
||||
from agent_framework import Agent, Message
|
||||
from agent_framework.azure import AzureOpenAIChatClient
|
||||
from agent_framework.orchestrations import ConcurrentBuilder
|
||||
from azure.identity import AzureCliCredential
|
||||
@@ -91,12 +91,12 @@ def _print_semantic_kernel_outputs(outputs: Sequence[ChatMessageContent]) -> Non
|
||||
async def run_agent_framework_example(prompt: str) -> Sequence[list[Message]]:
|
||||
client = AzureOpenAIChatClient(credential=AzureCliCredential())
|
||||
|
||||
physics = client.as_agent(
|
||||
physics = Agent(client=client,
|
||||
instructions=("You are an expert in physics. Answer questions from a physics perspective."),
|
||||
name="physics",
|
||||
)
|
||||
|
||||
chemistry = client.as_agent(
|
||||
chemistry = Agent(client=client,
|
||||
instructions=("You are an expert in chemistry. Answer questions from a chemistry perspective."),
|
||||
name="chemistry",
|
||||
)
|
||||
|
||||
@@ -17,7 +17,7 @@ from collections.abc import Sequence
|
||||
from typing import Any, cast
|
||||
|
||||
from agent_framework import Agent, Message
|
||||
from agent_framework.azure import AzureOpenAIChatClient, AzureOpenAIResponsesClient
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import GroupChatBuilder
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
@@ -130,8 +130,7 @@ class ChatCompletionGroupChatManager(GroupChatManager):
|
||||
chat_history,
|
||||
settings=PromptExecutionSettings(response_format=BooleanResult),
|
||||
)
|
||||
result = BooleanResult.model_validate_json(response.content)
|
||||
return result
|
||||
return BooleanResult.model_validate_json(response.content)
|
||||
|
||||
@override
|
||||
async def select_next_agent(
|
||||
@@ -235,19 +234,19 @@ async def run_agent_framework_example(task: str) -> str:
|
||||
"Gather concise facts or considerations that help plan a community hackathon. "
|
||||
"Keep your responses factual and scannable."
|
||||
),
|
||||
client=AzureOpenAIChatClient(credential=credential),
|
||||
client=FoundryChatClient(credential=credential),
|
||||
)
|
||||
|
||||
planner = Agent(
|
||||
name="Planner",
|
||||
description="Turns the collected notes into a concrete action plan.",
|
||||
instructions=("Propose a structured action plan that accounts for logistics, roles, and timeline."),
|
||||
client=AzureOpenAIResponsesClient(credential=credential),
|
||||
client=FoundryChatClient(credential=credential),
|
||||
)
|
||||
|
||||
workflow = GroupChatBuilder(
|
||||
participants=[researcher, planner],
|
||||
orchestrator_agent=AzureOpenAIChatClient(credential=credential).as_agent(),
|
||||
orchestrator_agent=Agent(client=FoundryChatClient(credential=credential)),
|
||||
).build()
|
||||
|
||||
final_response = ""
|
||||
|
||||
@@ -13,17 +13,18 @@
|
||||
import asyncio
|
||||
import sys
|
||||
from collections.abc import AsyncIterable, Iterator, Sequence
|
||||
from typing import cast
|
||||
|
||||
from agent_framework import (
|
||||
Agent,
|
||||
Message,
|
||||
WorkflowEvent,
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIChatClient
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import HandoffAgentUserRequest, HandoffBuilder
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
from semantic_kernel.agents import Agent, ChatCompletionAgent, HandoffOrchestration, OrchestrationHandoffs
|
||||
from semantic_kernel.agents import Agent as SKAgent
|
||||
from semantic_kernel.agents import ChatCompletionAgent, HandoffOrchestration, OrchestrationHandoffs
|
||||
from semantic_kernel.agents.runtime import InProcessRuntime
|
||||
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
|
||||
from semantic_kernel.contents import (
|
||||
@@ -74,7 +75,7 @@ class OrderReturnPlugin:
|
||||
return f"Return for order {order_id} has been processed successfully (reason: {reason})."
|
||||
|
||||
|
||||
def build_semantic_kernel_agents() -> tuple[list[Agent], OrchestrationHandoffs]:
|
||||
def build_semantic_kernel_agents() -> tuple[list[SKAgent], OrchestrationHandoffs]:
|
||||
credential = AzureCliCredential()
|
||||
|
||||
triage = ChatCompletionAgent(
|
||||
@@ -189,8 +190,9 @@ async def run_semantic_kernel_example(initial_task: str, scripted_responses: Seq
|
||||
######################################################################
|
||||
|
||||
|
||||
def _create_af_agents(client: AzureOpenAIChatClient):
|
||||
triage = client.as_agent(
|
||||
def _create_af_agents(client: FoundryChatClient):
|
||||
triage = Agent(
|
||||
client=client,
|
||||
name="triage_agent",
|
||||
instructions=(
|
||||
"You are a customer support triage agent. Route requests:\n"
|
||||
@@ -199,19 +201,22 @@ def _create_af_agents(client: AzureOpenAIChatClient):
|
||||
"- handoff_to_order_return_agent for returns"
|
||||
),
|
||||
)
|
||||
refund = client.as_agent(
|
||||
refund = Agent(
|
||||
client=client,
|
||||
name="refund_agent",
|
||||
instructions=(
|
||||
"Handle refunds. Ask for order id and reason. If shipping info is needed, hand off to order_status_agent."
|
||||
),
|
||||
)
|
||||
status = client.as_agent(
|
||||
status = Agent(
|
||||
client=client,
|
||||
name="order_status_agent",
|
||||
instructions=(
|
||||
"Provide order status, tracking, and timelines. If billing questions appear, hand off to refund_agent."
|
||||
),
|
||||
)
|
||||
returns = client.as_agent(
|
||||
returns = Agent(
|
||||
client=client,
|
||||
name="order_return_agent",
|
||||
instructions=(
|
||||
"Coordinate returns, confirm addresses, and summarize next steps. Hand off to triage_agent if unsure."
|
||||
@@ -235,13 +240,12 @@ def _collect_handoff_requests(events: list[WorkflowEvent]) -> list[WorkflowEvent
|
||||
def _extract_final_conversation(events: list[WorkflowEvent]) -> list[Message]:
|
||||
for event in events:
|
||||
if event.type == "output":
|
||||
data = cast(list[Message], event.data)
|
||||
return data
|
||||
return event.data
|
||||
return []
|
||||
|
||||
|
||||
async def run_agent_framework_example(initial_task: str, scripted_responses: Sequence[str]) -> str:
|
||||
client = AzureOpenAIChatClient(credential=AzureCliCredential())
|
||||
client = FoundryChatClient(credential=AzureCliCredential())
|
||||
triage, refund, status, returns = _create_af_agents(client)
|
||||
|
||||
workflow = (
|
||||
|
||||
@@ -137,7 +137,7 @@ async def run_agent_framework_example(prompt: str) -> str | None:
|
||||
instructions=(
|
||||
"You are a Researcher. You find information without additional computation or quantitative analysis."
|
||||
),
|
||||
client=OpenAIChatClient(model_id="gpt-4o-search-preview"),
|
||||
client=OpenAIChatClient(model="gpt-4o-search-preview"),
|
||||
)
|
||||
|
||||
# Create code interpreter tool using static method
|
||||
|
||||
@@ -15,12 +15,13 @@ import asyncio
|
||||
from collections.abc import Sequence
|
||||
from typing import cast
|
||||
|
||||
from agent_framework import Message
|
||||
from agent_framework import Agent, Message
|
||||
from agent_framework.azure import AzureOpenAIChatClient
|
||||
from agent_framework.orchestrations import SequentialBuilder
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
from semantic_kernel.agents import Agent, ChatCompletionAgent, SequentialOrchestration
|
||||
from semantic_kernel.agents import Agent as SKAgent
|
||||
from semantic_kernel.agents import ChatCompletionAgent, SequentialOrchestration
|
||||
from semantic_kernel.agents.runtime import InProcessRuntime
|
||||
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
|
||||
from semantic_kernel.contents import ChatMessageContent
|
||||
@@ -36,7 +37,7 @@ PROMPT = "Write a tagline for a budget-friendly eBike."
|
||||
######################################################################
|
||||
|
||||
|
||||
def build_semantic_kernel_agents() -> list[Agent]:
|
||||
def build_semantic_kernel_agents() -> list[SKAgent]:
|
||||
credential = AzureCliCredential()
|
||||
|
||||
writer_agent = ChatCompletionAgent(
|
||||
@@ -77,12 +78,12 @@ async def sk_agent_response_callback(
|
||||
async def run_agent_framework_example(prompt: str) -> list[Message]:
|
||||
client = AzureOpenAIChatClient(credential=AzureCliCredential())
|
||||
|
||||
writer = client.as_agent(
|
||||
writer = Agent(client=client,
|
||||
instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
|
||||
name="writer",
|
||||
)
|
||||
|
||||
reviewer = client.as_agent(
|
||||
reviewer = Agent(client=client,
|
||||
instructions=("You are a thoughtful reviewer. Give brief feedback on the previous assistant message."),
|
||||
name="reviewer",
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user