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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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GitHub
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5e056b672e
@@ -14,7 +14,7 @@ from agent_framework import ( # Core chat primitives used to build requests
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WorkflowContext, # Per-run context and event bus
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executor, # Decorator to declare a Python function as a workflow executor
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)
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from agent_framework.azure import AzureOpenAIResponsesClient # Thin client wrapper for Azure OpenAI chat models
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from agent_framework.foundry import FoundryChatClient # Thin client wrapper for Azure OpenAI chat models
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from azure.identity import AzureCliCredential # Uses your az CLI login for credentials
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from dotenv import load_dotenv
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from pydantic import BaseModel # Structured outputs for safer parsing
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@@ -36,10 +36,10 @@ Purpose:
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- Illustrate how to transform one agent's structured result into a new AgentExecutorRequest for a downstream agent.
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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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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- You understand the basics of WorkflowBuilder, executors, and events in this framework.
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- You know the concept of edge conditions and how they gate routes using a predicate function.
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- Azure OpenAI access is configured for AzureOpenAIResponsesClient. You should be logged in with Azure CLI (AzureCliCredential)
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- Azure OpenAI access is configured for FoundryChatClient. You should be logged in with Azure CLI (AzureCliCredential)
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and have the Foundry V2 Project environment variables set as documented in the getting started chat client README.
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- The sample email resource file exists at workflow/resources/email.txt.
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@@ -136,11 +136,12 @@ async def to_email_assistant_request(
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def create_spam_detector_agent() -> Agent:
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"""Helper to create a spam detection agent."""
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# AzureCliCredential uses your current az login. This avoids embedding secrets in code.
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return 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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return 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=(
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"You are a spam detection assistant that identifies spam emails. "
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"Always return JSON with fields is_spam (bool), reason (string), and email_content (string). "
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@@ -154,11 +155,12 @@ def create_spam_detector_agent() -> Agent:
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def create_email_assistant_agent() -> Agent:
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"""Helper to create an email assistant agent."""
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# AzureCliCredential uses your current az login. This avoids embedding secrets in code.
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return 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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return 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=(
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"You are an email assistant that helps users draft professional responses to emails. "
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"Your input may be a JSON object that includes 'email_content'; base your reply on that content. "
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@@ -20,7 +20,7 @@ from agent_framework import (
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WorkflowEvent,
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executor,
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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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from pydantic import BaseModel
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@@ -47,7 +47,7 @@ Show how to:
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- Apply conditional persistence logic (short vs long emails).
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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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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Familiarity with WorkflowBuilder, executors, edges, and events.
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- Understanding of multi-selection edge groups and how their selection function maps to target ids.
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- Experience with workflow state for persisting and reusing objects.
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@@ -188,11 +188,12 @@ async def database_access(analysis: AnalysisResult, ctx: WorkflowContext[Never,
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def create_email_analysis_agent() -> Agent:
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"""Creates the email analysis agent."""
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return 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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return 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=(
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"You are a spam detection assistant that identifies spam emails. "
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"Always return JSON with fields 'spam_decision' (one of NotSpam, Spam, Uncertain) "
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@@ -205,11 +206,12 @@ def create_email_analysis_agent() -> Agent:
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def create_email_assistant_agent() -> Agent:
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"""Creates the email assistant agent."""
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return 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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return 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=("You are an email assistant that helps users draft responses to emails with professionalism."),
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name="email_assistant_agent",
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default_options={"response_format": EmailResponse},
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@@ -218,11 +220,12 @@ def create_email_assistant_agent() -> Agent:
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def create_email_summary_agent() -> Agent:
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"""Creates the email summary agent."""
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return 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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return 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=("You are an assistant that helps users summarize emails."),
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name="email_summary_agent",
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default_options={"response_format": EmailSummaryModel},
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@@ -16,7 +16,7 @@ from agent_framework import (
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WorkflowContext,
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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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@@ -32,8 +32,8 @@ What it does:
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- The workflow completes when the correct number is guessed.
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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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- Azure AI/ Azure OpenAI for `AzureOpenAIResponsesClient` agent.
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure AI/ Azure OpenAI for `FoundryChatClient` agent.
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- Authentication via `azure-identity` — uses `AzureCliCredential()` (run `az login`).
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"""
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@@ -123,11 +123,12 @@ class ParseJudgeResponse(Executor):
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def create_judge_agent() -> Agent:
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"""Create a judge agent that evaluates guesses."""
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return 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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return 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=("You strictly respond with one of: MATCHED, ABOVE, BELOW based on the given target and guess."),
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name="judge_agent",
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)
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@@ -18,7 +18,7 @@ from agent_framework import ( # Core chat primitives used to form LLM requests
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WorkflowContext, # Per-run context and event bus
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executor, # Decorator to turn a function into a workflow executor
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)
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from agent_framework.azure import AzureOpenAIResponsesClient # Thin client for Azure OpenAI chat models
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from agent_framework.foundry import FoundryChatClient # Thin client for Azure OpenAI chat models
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from azure.identity import AzureCliCredential # Uses your az CLI login for credentials
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from dotenv import load_dotenv
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from pydantic import BaseModel # Structured outputs with validation
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@@ -43,10 +43,10 @@ on that type.
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- Use ctx.yield_output() to provide workflow results - the workflow completes when idle with no pending work.
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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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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Familiarity with WorkflowBuilder, executors, edges, and events.
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- Understanding of switch-case edge groups and how Case and Default are evaluated in order.
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- Working Azure OpenAI configuration for AzureOpenAIResponsesClient, with Azure CLI login and required environment variables.
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- Working Azure OpenAI configuration for FoundryChatClient, with Azure CLI login and required environment variables.
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- Access to workflow/resources/ambiguous_email.txt, or accept the inline fallback string.
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"""
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@@ -159,11 +159,12 @@ async def handle_uncertain(detection: DetectionResult, ctx: WorkflowContext[Neve
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def create_spam_detection_agent() -> Agent:
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"""Create and return the spam detection agent."""
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return 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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return 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=(
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"You are a spam detection assistant that identifies spam emails. "
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"Be less confident in your assessments. "
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@@ -177,11 +178,12 @@ def create_spam_detection_agent() -> Agent:
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def create_email_assistant_agent() -> Agent:
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"""Create and return the email assistant agent."""
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return 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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return 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=("You are an email assistant that helps users draft responses to emails with professionalism."),
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name="email_assistant_agent",
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default_options={"response_format": EmailResponse},
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