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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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commit
5e056b672e
@@ -88,18 +88,20 @@ configure_azure_monitor(
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# This is optional if ENABLE_INSTRUMENTATION and or ENABLE_SENSITIVE_DATA are set in env vars
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enable_instrumentation(enable_sensitive_data=False)
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```
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For Azure AI projects, use the `client.configure_azure_monitor()` method which wraps the calls to `configure_azure_monitor()` and `enable_instrumentation()`:
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For Microsoft Foundry projects, use `client.configure_azure_monitor()` which retrieves the connection string from the project and configures everything:
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```python
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from agent_framework.azure import AzureAIClient
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from azure.ai.projects.aio import AIProjectClient
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from agent_framework.foundry import FoundryChatClient
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from azure.identity import AzureCliCredential
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async with (
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AIProjectClient(...) as project_client,
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AzureAIClient(project_client=project_client) as client,
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):
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# Automatically configures Azure Monitor with connection string from project
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await client.configure_azure_monitor(enable_live_metrics=True)
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client = FoundryChatClient(
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project_endpoint="https://your-project.services.ai.azure.com",
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model="gpt-4o",
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credential=AzureCliCredential(),
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)
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# Automatically configures Azure Monitor with connection string from project
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await client.configure_azure_monitor(enable_sensitive_data=True)
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```
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Or with [Langfuse](https://langfuse.com/integrations/frameworks/microsoft-agent-framework):
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@@ -227,8 +229,7 @@ This folder contains different samples demonstrating how to use telemetry in var
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| [configure_otel_providers_with_parameters.py](./configure_otel_providers_with_parameters.py) | **Recommended starting point**: Shows how to create custom exporters with specific configuration and pass them to `configure_otel_providers()`. Useful for advanced scenarios. |
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| [configure_otel_providers_with_env_var.py](./configure_otel_providers_with_env_var.py) | Shows how to setup telemetry using standard OpenTelemetry environment variables (`OTEL_EXPORTER_OTLP_*`). |
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| [agent_observability.py](./agent_observability.py) | Shows telemetry collection for an agentic application with tool calls using environment variables. |
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| [agent_with_foundry_tracing.py](./agent_with_foundry_tracing.py) | Shows Azure Monitor integration with Foundry for any chat client. |
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| [azure_ai_agent_observability.py](./azure_ai_agent_observability.py) | Shows Azure Monitor integration for a AzureAIClient. |
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| [foundry_tracing.py](./foundry_tracing.py) | Shows Azure Monitor integration with Foundry for any chat client. |
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| [advanced_manual_setup_console_output.py](./advanced_manual_setup_console_output.py) | Advanced: Shows manual setup of exporters and providers with console output. Useful for understanding how observability works under the hood. |
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| [advanced_zero_code.py](./advanced_zero_code.py) | Advanced: Shows zero-code telemetry setup using the `opentelemetry-enable_instrumentation` CLI tool. |
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| [workflow_observability.py](./workflow_observability.py) | Shows telemetry collection for a workflow with multiple executors and message passing. |
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@@ -347,15 +348,16 @@ setup_observability(
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**After (Current):**
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```python
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# For Azure AI projects
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from agent_framework.azure import AzureAIClient
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from azure.ai.projects.aio import AIProjectClient
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# For Microsoft Foundry projects
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from agent_framework.foundry import FoundryChatClient
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from azure.identity import AzureCliCredential
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async with (
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AIProjectClient(...) as project_client,
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AzureAIClient(project_client=project_client) as client,
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):
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await client.configure_azure_monitor(enable_live_metrics=True)
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client = FoundryChatClient(
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project_endpoint="https://your-project.services.ai.azure.com",
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model="gpt-4o",
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credential=AzureCliCredential(),
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)
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await client.configure_azure_monitor(enable_live_metrics=True)
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# For non-Azure AI projects
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from azure.monitor.opentelemetry import configure_azure_monitor
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@@ -6,8 +6,8 @@ from random import randint
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from typing import Annotated
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from agent_framework import Message, tool
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.observability import enable_instrumentation
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from agent_framework.openai import OpenAIChatClient
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from dotenv import load_dotenv
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from opentelemetry._logs import set_logger_provider
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from opentelemetry.metrics import set_meter_provider
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@@ -115,7 +115,7 @@ async def run_chat_client() -> None:
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2 spans with gen_ai.operation.name=execute_tool
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"""
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client = OpenAIChatClient()
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client = FoundryChatClient()
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message = "What's the weather in Amsterdam and in Paris?"
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print(f"User: {message}")
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print("Assistant: ", end="")
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@@ -5,8 +5,8 @@ from random import randint
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from typing import TYPE_CHECKING, Annotated
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from agent_framework import Message, tool
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.observability import get_tracer
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from agent_framework.openai import OpenAIResponsesClient
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from dotenv import load_dotenv
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from opentelemetry.trace import SpanKind
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from opentelemetry.trace.span import format_trace_id
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@@ -103,7 +103,7 @@ async def main() -> None:
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with get_tracer().start_as_current_span("Zero Code", kind=SpanKind.CLIENT) as current_span:
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print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
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client = OpenAIResponsesClient()
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client = FoundryChatClient()
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await run_chat_client(client, stream=True)
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await run_chat_client(client, stream=False)
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@@ -5,8 +5,8 @@ from random import randint
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from typing import Annotated
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from agent_framework import Agent, tool
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.observability import configure_otel_providers, get_tracer
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from agent_framework.openai import OpenAIChatClient
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from dotenv import load_dotenv
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from opentelemetry.trace import SpanKind
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from opentelemetry.trace.span import format_trace_id
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@@ -47,7 +47,7 @@ async def main():
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print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
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agent = Agent(
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client=OpenAIChatClient(),
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client=FoundryChatClient(),
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tools=get_weather,
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name="WeatherAgent",
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instructions="You are a weather assistant.",
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@@ -1,107 +0,0 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "azure-monitor-opentelemetry",
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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 python/samples/02-agents/observability/agent_with_foundry_tracing.py
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import logging
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import os
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from random import randint
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from typing import Annotated
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from agent_framework import Agent, tool
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from agent_framework.observability import create_resource, enable_instrumentation, get_tracer
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from agent_framework.openai import OpenAIResponsesClient
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from azure.ai.projects.aio import AIProjectClient
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from azure.identity.aio import AzureCliCredential
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from azure.monitor.opentelemetry import configure_azure_monitor
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from dotenv import load_dotenv
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from opentelemetry.trace import SpanKind
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from opentelemetry.trace.span import format_trace_id
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from pydantic import Field
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"""
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This sample shows you can can setup telemetry in Microsoft Foundry for a custom agent.
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First ensure you have a Foundry workspace with Application Insights enabled.
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And use the Operate tab to Register an Agent.
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Set the OpenTelemetry agent ID to the value used below in the Agent creation: `weather-agent` (or change both).
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The sample uses the Azure Monitor OpenTelemetry exporter to send traces to Application Insights.
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So ensure you have the `azure-monitor-opentelemetry` package installed.
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"""
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# For loading the `AZURE_AI_PROJECT_ENDPOINT` environment variable
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load_dotenv()
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logger = logging.getLogger(__name__)
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# NOTE: approval_mode="never_require" is for sample brevity.
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# Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py
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# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
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@tool(approval_mode="never_require")
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async def get_weather(
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location: Annotated[str, Field(description="The location to get the weather for.")],
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) -> str:
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"""Get the weather for a given location."""
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await asyncio.sleep(randint(0, 10) / 10.0) # Simulate a network call
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conditions = ["sunny", "cloudy", "rainy", "stormy"]
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return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
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async def main():
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async with (
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AzureCliCredential() as credential,
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AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
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):
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# This will enable tracing and configure the application to send telemetry data to the
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# Application Insights instance attached to the Azure AI project.
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# This will override any existing configuration.
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try:
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conn_string = await project_client.telemetry.get_application_insights_connection_string()
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except Exception:
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logger.warning(
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"No Application Insights connection string found for the Azure AI Project. "
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"Please ensure Application Insights is configured in your Azure AI project, "
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"or call configure_otel_providers() manually with custom exporters."
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)
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return
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configure_azure_monitor(
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connection_string=conn_string,
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enable_live_metrics=True,
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resource=create_resource(),
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enable_performance_counters=False,
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)
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# This call is not necessary if you have the environment variable ENABLE_INSTRUMENTATION=true set
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# If not or set to false, or if you want to enable or disable sensitive data collection, call this function.
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enable_instrumentation(enable_sensitive_data=True)
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print("Observability is set up. Starting Weather Agent...")
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questions = ["What's the weather in Amsterdam?", "and in Paris, and which is better?", "Why is the sky blue?"]
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with get_tracer().start_as_current_span("Weather Agent Chat", kind=SpanKind.CLIENT) as current_span:
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print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
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agent = Agent(
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client=OpenAIResponsesClient(),
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tools=get_weather,
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name="WeatherAgent",
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instructions="You are a weather assistant.",
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id="weather-agent",
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)
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session = agent.create_session()
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for question in questions:
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print(f"\nUser: {question}")
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print(f"{agent.name}: ", end="")
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async for update in agent.run(question, session=session, stream=True):
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if update.text:
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print(update.text, end="")
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -1,78 +0,0 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from random import randint
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from typing import Annotated
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from agent_framework import Agent, tool
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from agent_framework.azure import AzureAIClient
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from agent_framework.observability import get_tracer
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from azure.ai.projects.aio import AIProjectClient
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from azure.identity.aio import AzureCliCredential
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from dotenv import load_dotenv
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from opentelemetry.trace import SpanKind
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from opentelemetry.trace.span import format_trace_id
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from pydantic import Field
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"""
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This sample shows you can setup telemetry for an Azure AI agent.
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It uses the Azure AI client to setup the telemetry, this calls out to
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Azure AI for the connection string of the attached Application Insights
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instance.
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You must add an Application Insights instance to your Azure AI project
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for this sample to work.
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"""
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# For loading the `AZURE_AI_PROJECT_ENDPOINT` environment variable
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load_dotenv()
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# NOTE: approval_mode="never_require" is for sample brevity.
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# Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py
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# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
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@tool(approval_mode="never_require")
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async def get_weather(
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location: Annotated[str, Field(description="The location to get the weather for.")],
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) -> str:
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"""Get the weather for a given location."""
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await asyncio.sleep(randint(0, 10) / 10.0) # Simulate a network call
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conditions = ["sunny", "cloudy", "rainy", "stormy"]
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return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
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async def main():
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async with (
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AzureCliCredential() as credential,
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AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
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AzureAIClient(project_client=project_client) as client,
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):
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# This will enable tracing and configure the application to send telemetry data to the
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# Application Insights instance attached to the Azure AI project.
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# This will override any existing configuration.
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await client.configure_azure_monitor(enable_live_metrics=True)
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questions = ["What's the weather in Amsterdam?", "and in Paris, and which is better?", "Why is the sky blue?"]
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with get_tracer().start_as_current_span("Single Agent Chat", kind=SpanKind.CLIENT) as current_span:
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print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
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agent = Agent(
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client=client,
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tools=get_weather,
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name="WeatherAgent",
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instructions="You are a weather assistant.",
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id="edvan-weather-agent",
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)
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session = agent.create_session()
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for question in questions:
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print(f"\nUser: {question}")
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print(f"{agent.name}: ", end="")
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async for update in agent.run(question, session=session, stream=True):
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if update.text:
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print(update.text, end="")
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -7,8 +7,8 @@ from random import randint
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from typing import TYPE_CHECKING, Annotated, Literal
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from agent_framework import Message, tool
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.observability import configure_otel_providers, get_tracer
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from agent_framework.openai import OpenAIResponsesClient
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from dotenv import load_dotenv
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from opentelemetry import trace
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from opentelemetry.trace.span import format_trace_id
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@@ -114,7 +114,7 @@ async def main(scenario: Literal["client", "client_stream", "tool", "all"] = "al
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with get_tracer().start_as_current_span("Sample Scenarios", kind=trace.SpanKind.CLIENT) as current_span:
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print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
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client = OpenAIResponsesClient()
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client = FoundryChatClient()
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# Scenarios where telemetry is collected in the SDK, from the most basic to the most complex.
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if scenario == "tool" or scenario == "all":
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@@ -8,8 +8,8 @@ from random import randint
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from typing import TYPE_CHECKING, Annotated, Literal
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from agent_framework import Message, tool
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.observability import configure_otel_providers, get_tracer
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from agent_framework.openai import OpenAIResponsesClient
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from dotenv import load_dotenv
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from opentelemetry import trace
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from opentelemetry.trace.span import format_trace_id
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@@ -153,7 +153,7 @@ async def main(scenario: Literal["client", "client_stream", "tool", "all"] = "al
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with get_tracer().start_as_current_span("Sample Scenarios", kind=trace.SpanKind.CLIENT) as current_span:
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print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
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client = OpenAIResponsesClient()
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client = FoundryChatClient()
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# Scenarios where telemetry is collected in the SDK, from the most basic to the most complex.
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if scenario == "tool" or scenario == "all":
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@@ -0,0 +1,94 @@
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# /// script
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# requires-python = ">=3.10"
|
||||
# dependencies = [
|
||||
# "azure-monitor-opentelemetry",
|
||||
# ]
|
||||
# ///
|
||||
# Run with any PEP 723 compatible runner, e.g.:
|
||||
# uv run python/samples/02-agents/observability/foundry_tracing.py
|
||||
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
from random import randint
|
||||
from typing import Annotated
|
||||
|
||||
from agent_framework import Agent, tool
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.observability import get_tracer
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
from opentelemetry.trace import SpanKind
|
||||
from opentelemetry.trace.span import format_trace_id
|
||||
from pydantic import Field
|
||||
|
||||
"""
|
||||
This sample shows how to setup telemetry in Microsoft Foundry for a custom agent
|
||||
using ``FoundryChatClient.configure_azure_monitor()``.
|
||||
|
||||
First ensure you have a Foundry workspace with Application Insights enabled.
|
||||
And use the Operate tab to Register an Agent.
|
||||
Set the OpenTelemetry agent ID to the value used below in the Agent creation: ``weather-agent``
|
||||
(or change both).
|
||||
|
||||
Environment variables:
|
||||
FOUNDRY_PROJECT_ENDPOINT — Microsoft Foundry project endpoint
|
||||
FOUNDRY_MODEL — Model deployment name (e.g. gpt-4o)
|
||||
"""
|
||||
|
||||
load_dotenv()
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# NOTE: approval_mode="never_require" is for sample brevity.
|
||||
@tool(approval_mode="never_require")
|
||||
async def get_weather(
|
||||
location: Annotated[str, Field(description="The location to get the weather for.")],
|
||||
) -> str:
|
||||
"""Get the weather for a given location."""
|
||||
await asyncio.sleep(randint(0, 10) / 10.0) # Simulate a network call
|
||||
conditions = ["sunny", "cloudy", "rainy", "stormy"]
|
||||
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
|
||||
|
||||
|
||||
async def main():
|
||||
client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["FOUNDRY_MODEL"],
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
|
||||
# configure_azure_monitor() retrieves the Application Insights connection string
|
||||
# from the project client and sets up tracing automatically.
|
||||
await client.configure_azure_monitor(
|
||||
enable_sensitive_data=True,
|
||||
enable_live_metrics=True,
|
||||
)
|
||||
print("Observability is set up. Starting Weather Agent...")
|
||||
|
||||
questions = ["What's the weather in Amsterdam?", "and in Paris, and which is better?", "Why is the sky blue?"]
|
||||
|
||||
with get_tracer().start_as_current_span("Weather Agent Chat", kind=SpanKind.CLIENT) as current_span:
|
||||
print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
|
||||
|
||||
agent = Agent(
|
||||
client=client,
|
||||
tools=[get_weather],
|
||||
name="WeatherAgent",
|
||||
instructions="You are a weather assistant.",
|
||||
id="weather-agent",
|
||||
)
|
||||
session = agent.create_session()
|
||||
for question in questions:
|
||||
print(f"\nUser: {question}")
|
||||
print(f"{agent.name}: ", end="")
|
||||
async for update in agent.run(question, session=session, stream=True):
|
||||
if update.text:
|
||||
print(update.text, end="")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user