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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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@@ -9,6 +9,7 @@ from pathlib import Path
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from typing import Any
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from agent_framework import (
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Agent,
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AgentExecutor,
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AgentExecutorRequest,
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AgentExecutorResponse,
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@@ -21,7 +22,7 @@ from agent_framework import (
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handler,
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response_handler,
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)
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework.foundry import FoundryChatClient
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -178,11 +179,12 @@ def create_workflow(checkpoint_storage: FileCheckpointStorage) -> Workflow:
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# Wire the workflow DAG. Edges mirror the numbered steps described in the
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# module docstring. Because `WorkflowBuilder` is declarative, reading these
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# edges is often the quickest way to understand execution order.
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writer_agent = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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).as_agent(
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writer_agent = Agent(
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client=FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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),
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instructions="Write concise, warm release notes that sound human and helpful.",
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name="writer",
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)
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@@ -20,18 +20,19 @@ Key concepts:
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- These are complementary: sessions track conversation, checkpoints track workflow state
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Prerequisites:
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- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Environment variables configured for AzureOpenAIResponsesClient
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Environment variables configured for FoundryChatClient
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"""
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import asyncio
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import os
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from agent_framework import (
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Agent,
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InMemoryCheckpointStorage,
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InMemoryHistoryProvider,
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)
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import SequentialBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -47,24 +48,26 @@ async def basic_checkpointing() -> None:
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print("Basic Checkpointing with Workflow as Agent")
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print("=" * 60)
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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assistant = client.as_agent(
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assistant = Agent(
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client=client,
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name="assistant",
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instructions="You are a helpful assistant. Keep responses brief.",
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)
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reviewer = client.as_agent(
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reviewer = Agent(
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client=client,
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name="reviewer",
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instructions="You are a reviewer. Provide a one-sentence summary of the assistant's response.",
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)
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workflow = SequentialBuilder(participants=[assistant, reviewer]).build()
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agent = workflow.as_agent(name="CheckpointedAgent")
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agent = Agent(client=workflow, name="CheckpointedAgent")
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# Create checkpoint storage
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checkpoint_storage = InMemoryCheckpointStorage()
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@@ -92,19 +95,20 @@ async def checkpointing_with_thread() -> None:
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print("Checkpointing with Thread Conversation History")
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print("=" * 60)
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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assistant = client.as_agent(
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assistant = Agent(
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client=client,
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name="memory_assistant",
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instructions="You are a helpful assistant with good memory. Reference previous conversation when relevant.",
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)
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workflow = SequentialBuilder(participants=[assistant]).build()
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agent = workflow.as_agent(name="MemoryAgent")
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agent = Agent(client=workflow, name="MemoryAgent")
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# Create both session (for conversation) and checkpoint storage (for workflow state)
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session = agent.create_session()
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@@ -139,19 +143,20 @@ async def streaming_with_checkpoints() -> None:
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print("Streaming with Checkpointing")
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print("=" * 60)
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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assistant = client.as_agent(
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assistant = Agent(
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client=client,
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name="streaming_assistant",
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instructions="You are a helpful assistant.",
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)
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workflow = SequentialBuilder(participants=[assistant]).build()
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agent = workflow.as_agent(name="StreamingCheckpointAgent")
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agent = Agent(client=workflow, name="StreamingCheckpointAgent")
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checkpoint_storage = InMemoryCheckpointStorage()
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