agent/agent-framework
1
0
Fork 0
mirror of https://github.com/microsoft/agent-framework.git synced 2026-06-16 21:04:09 +08:00
Code Issues Packages Projects Releases Wiki Activity

Python: Improved telemetry setup (#421)

Browse Source 
* test with stack and simplified names

* quick demo of agent decorator

* moved builder to protocol to enhance functionality

* undid chatclientAgent -> agent rename

* one more

* reverted AIAgent rename

* final reverts

* fixed foundry import

* revert changes

* streamlined otel and fcc decorators

* cleanup of telemetry

* further refinement

* lots of updates

* fixed typing

* fix for mypy

* added input and output atttributes

* fix import

* initial work on baking in otel

* major update to telemetry

* final fixes after rename

* fix

* fix test

* updated tests

* fix for tests

* fixes for tests

* updated based on comments

* removed agent decorator

* fix for Python: ServiceResponseException when using multiple tools
Fixes #649

* addressed comments

* fix tests

* fix tests

* fix tools tests

* fix for conversation_id in assistants client

* fix responses test

* fix tests and mypy

* updated test

* foundry fix

---------

Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
2025-09-10 16:52:42 +02:00
committed by GitHub
Unverified
parent 6aa746d891
commit 82ca4065cb
45 changed files with 3246 additions and 2984 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/samples/getting_started/telemetry/02a-generic_chat_client.py
+134
View File
@@ -0,0 +1,134 @@
# Copyright (c) Microsoft. All rights reserved.
# type: ignore
import argparse
import asyncio
from contextlib import suppress
from random import randint
from typing import TYPE_CHECKING, Annotated, Literal
from agent_framework import __version__, ai_function
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.telemetry import setup_telemetry
from opentelemetry import trace
from opentelemetry.trace import SpanKind
from opentelemetry.trace.span import format_trace_id
from pydantic import Field
if TYPE_CHECKING:
from agent_framework import ChatClientProtocol
"""
This sample, show how you can get telemetry from a chat client and tool.
it explicitly calls the `setup_telemetry` function to set up telemetry in order to include the overall spans,
those are defined in the main and run_* functions.
"""
# Define the scenarios that can be run
SCENARIOS = ["chat_client", "chat_client_stream", "ai_function", "all"]
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 run_chat_client(client: "ChatClientProtocol", stream: bool = False) -> None:
"""Run an AI service.
This function runs an AI service and prints the output.
Telemetry will be collected for the service execution behind the scenes,
and the traces will be sent to the configured telemetry backend.
The telemetry will include information about the AI service execution.
Args:
client: The chat client to use.
stream: Whether to use streaming for the response
Remarks:
For the scenario below, you should see the following:
1 Client span, with 4 children:
2 Internal span with gen_ai.operation.name=chat
The first has finish_reason "tool_calls"
The second has finish_reason "stop"
2 Internal span with gen_ai.operation.name=execute_tool
"""
scenario_name = "Chat Client Stream" if stream else "Chat Client"
tracer = trace.get_tracer("agent_framework", __version__)
with tracer.start_as_current_span(name=f"Scenario: {scenario_name}", kind=SpanKind.CLIENT):
print("Running scenario:", scenario_name)
message = "What's the weather in Amsterdam and in Paris?"
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
if str(chunk):
print(str(chunk), end="")
print("")
else:
response = await client.get_response(message, tools=get_weather)
print(f"Assistant: {response}")
async def run_ai_function() -> None:
"""Run a AI function.
This function runs a AI function and prints the output.
Telemetry will be collected for the function execution behind the scenes,
and the traces will be sent to the configured telemetry backend.
The telemetry will include information about the AI function execution
and the AI service execution.
"""
tracer = trace.get_tracer("agent_framework", __version__)
with tracer.start_as_current_span("Scenario: AI Function", kind=SpanKind.CLIENT):
print("Running scenario: AI Function")
func = ai_function(get_weather)
weather = await func.invoke(location="Amsterdam")
print(f"Weather in Amsterdam:\n{weather}")
async def main(scenario: Literal["chat_client", "chat_client_stream", "ai_function", "all"] = "all"):
"""Run the selected scenario(s)."""
setup_telemetry()
tracer = trace.get_tracer("My application", __version__)
with tracer.start_as_current_span("Sample Scenario's", kind=SpanKind.CLIENT) as current_span:
print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
client = OpenAIResponsesClient()
# Scenarios where telemetry is collected in the SDK, from the most basic to the most complex.
if scenario == "chat_client_stream" or scenario == "all":
with suppress(Exception):
await run_chat_client(client, stream=True)
if scenario == "chat_client" or scenario == "all":
with suppress(Exception):
await run_chat_client(client, stream=False)
if scenario == "ai_function" or scenario == "all":
with suppress(Exception):
await run_ai_function()
if __name__ == "__main__":
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument(
"--scenario",
type=str,
choices=SCENARIOS,
default="all",
help="The scenario to run. Default is all.",
)
args = arg_parser.parse_args()
asyncio.run(main(args.scenario))