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Python: Tracing for workflows (#480)
* workflow tracing design doc * add tracing implementation for workflow * fix bug caused by double wrapping of sub workflow request * add unit tests for tracing * add documentation for workflow tracing * remove unnecessary file * update aspire command * fix tests * proper serialization of subworkflows and add workflow.definition * add serialization test * fix subworkflow serialization * workflow_id --> id * update workflow sample to address comments * update naming; use costant * use NoOpTracer instead of nullcontext * use span event instead of attribtutes for status * fix typing * add workflow.build span * rename methods for clarity * ensure all source trace contexts are propagated in fan in
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Eric Zhu
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# Copyright (c) Microsoft. All rights reserved.
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# type: ignore
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import asyncio
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import logging
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from typing import Any
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from agent_framework.workflow import (
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Executor,
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WorkflowBuilder,
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WorkflowCompletedEvent,
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WorkflowContext,
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handler,
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)
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from azure.monitor.opentelemetry import configure_azure_monitor
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from opentelemetry import trace
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from opentelemetry._logs import set_logger_provider
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from opentelemetry.exporter.otlp.proto.grpc._log_exporter import OTLPLogExporter
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from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
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from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
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from opentelemetry.metrics import set_meter_provider
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from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
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from opentelemetry.sdk._logs.export import BatchLogRecordProcessor, ConsoleLogExporter
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from opentelemetry.sdk.metrics import MeterProvider
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from opentelemetry.sdk.metrics.export import ConsoleMetricExporter, PeriodicExportingMetricReader
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from opentelemetry.sdk.metrics.view import DropAggregation, View
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from opentelemetry.sdk.resources import Resource
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter
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from opentelemetry.semconv.attributes import service_attributes
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from opentelemetry.trace import SpanKind, set_tracer_provider
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from opentelemetry.trace.span import format_trace_id
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from pydantic_settings import BaseSettings
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"""Telemetry sample demonstrating OpenTelemetry integration with Agent Framework workflows.
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This sample runs a simple sequential workflow with telemetry collection,
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showing telemetry collection for workflow execution, executor processing,
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and message publishing between executors.
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"""
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class TelemetrySampleSettings(BaseSettings):
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"""Settings for the telemetry sample application.
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Optional settings are:
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- connection_string: str - The connection string for the Application Insights resource.
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This value can be found in the Overview section when examining
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your resource from the Azure portal.
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(Env var CONNECTION_STRING)
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- otlp_endpoint: str - The OTLP endpoint to send telemetry data to.
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Depending on the exporter used, you may find this value in different places.
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(Env var OTLP_ENDPOINT)
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If no connection string or OTLP endpoint is provided, the telemetry data will be
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exported to the console.
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"""
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connection_string: str | None = None
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otlp_endpoint: str | None = None
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# Load settings
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settings = TelemetrySampleSettings()
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# Create a resource to represent the service/sample
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resource = Resource.create({service_attributes.SERVICE_NAME: "WorkflowTelemetryExample"})
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if settings.connection_string:
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configure_azure_monitor(
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connection_string=settings.connection_string,
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enable_live_metrics=True,
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logger_name="agent_framework",
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)
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def set_up_logging():
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class LogFilter(logging.Filter):
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"""A filter to not process records from several subpackages."""
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# These are the namespaces that we want to exclude from logging for the purposes of this demo.
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namespaces_to_exclude: list[str] = [
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"httpx",
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"openai",
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]
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def filter(self, record):
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return not any([record.name.startswith(namespace) for namespace in self.namespaces_to_exclude])
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exporters = []
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if settings.otlp_endpoint:
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exporters.append(OTLPLogExporter(endpoint=settings.otlp_endpoint))
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if not exporters:
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exporters.append(ConsoleLogExporter())
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# Create and set a global logger provider for the application.
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logger_provider = LoggerProvider(resource=resource)
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# Log processors are initialized with an exporter which is responsible
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# for sending the telemetry data to a particular backend.
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for log_exporter in exporters:
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logger_provider.add_log_record_processor(BatchLogRecordProcessor(log_exporter))
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# Sets the global default logger provider
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set_logger_provider(logger_provider)
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# Create a logging handler to write logging records, in OTLP format, to the exporter.
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handler = LoggingHandler()
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handler.addFilter(LogFilter())
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# Attach the handler to the root logger. `getLogger()` with no arguments returns the root logger.
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# Events from all child loggers will be processed by this handler.
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logger = logging.getLogger()
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logger.addHandler(handler)
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# Set the logging level to NOTSET to allow all records to be processed by the handler.
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logger.setLevel(logging.NOTSET)
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def set_up_tracing():
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exporters = []
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if settings.otlp_endpoint:
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exporters.append(OTLPSpanExporter(endpoint=settings.otlp_endpoint))
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if not exporters:
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exporters.append(ConsoleSpanExporter())
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# Initialize a trace provider for the application. This is a factory for creating tracers.
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tracer_provider = TracerProvider(resource=resource)
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# Span processors are initialized with an exporter which is responsible
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# for sending the telemetry data to a particular backend.
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for exporter in exporters:
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tracer_provider.add_span_processor(BatchSpanProcessor(exporter))
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# Sets the global default tracer provider
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set_tracer_provider(tracer_provider)
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def set_up_metrics():
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exporters = []
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if settings.otlp_endpoint:
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exporters.append(OTLPMetricExporter(endpoint=settings.otlp_endpoint))
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if not exporters:
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exporters.append(ConsoleMetricExporter())
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# Initialize a metric provider for the application. This is a factory for creating meters.
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metric_readers = [
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PeriodicExportingMetricReader(metric_exporter, export_interval_millis=5000) for metric_exporter in exporters
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]
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meter_provider = MeterProvider(
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metric_readers=metric_readers,
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resource=resource,
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views=[
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# Dropping all instrument names except for those starting with "agent_framework"
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View(instrument_name="*", aggregation=DropAggregation()),
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View(instrument_name="agent_framework*"),
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],
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)
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# Sets the global default meter provider
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set_meter_provider(meter_provider)
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# Executors for sequential workflow
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class UpperCaseExecutor(Executor):
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"""An executor that converts text to uppercase."""
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@handler
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async def to_upper_case(self, text: str, ctx: WorkflowContext[str]) -> None:
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"""Execute the task by converting the input string to uppercase."""
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print(f"UpperCaseExecutor: Processing '{text}'")
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result = text.upper()
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print(f"UpperCaseExecutor: Result '{result}'")
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# Send the result to the next executor in the workflow.
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await ctx.send_message(result)
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class ReverseTextExecutor(Executor):
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"""An executor that reverses text."""
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@handler
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async def reverse_text(self, text: str, ctx: WorkflowContext[Any]) -> None:
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"""Execute the task by reversing the input string."""
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print(f"ReverseTextExecutor: Processing '{text}'")
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result = text[::-1]
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print(f"ReverseTextExecutor: Result '{result}'")
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# Send the result with a workflow completion event.
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await ctx.add_event(WorkflowCompletedEvent(result))
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async def run_sequential_workflow() -> None:
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"""Run a simple sequential workflow demonstrating telemetry collection.
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This workflow processes a string through two executors in sequence:
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1. UpperCaseExecutor converts the input to uppercase
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2. ReverseTextExecutor reverses the string and completes the workflow
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Telemetry data collected includes:
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- Overall workflow execution spans
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- Individual executor processing spans
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- Message publishing between executors
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- Workflow completion events
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"""
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tracer = trace.get_tracer(__name__)
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with tracer.start_as_current_span("Scenario: Sequential Workflow", kind=SpanKind.CLIENT) as current_span:
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print("Running scenario: Sequential Workflow")
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try:
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# Step 1: Create the executors.
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upper_case_executor = UpperCaseExecutor(id="upper_case_executor")
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reverse_text_executor = ReverseTextExecutor(id="reverse_text_executor")
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# Step 2: Build the workflow with the defined edges.
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workflow = (
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WorkflowBuilder()
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.add_edge(upper_case_executor, reverse_text_executor)
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.set_start_executor(upper_case_executor)
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.build()
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)
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# Step 3: Run the workflow with an initial message.
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input_text = "hello world"
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print(f"Starting workflow with input: '{input_text}'")
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completion_event = None
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async for event in workflow.run_streaming(input_text):
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print(f"Event: {event}")
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if isinstance(event, WorkflowCompletedEvent):
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# The WorkflowCompletedEvent contains the final result.
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completion_event = event
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if completion_event:
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print(f"Workflow completed with result: '{completion_event.data}'")
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else:
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print("Workflow completed without a completion event")
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except Exception as e:
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current_span.record_exception(e)
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print(f"Error running workflow: {e}")
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async def main():
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"""Run the telemetry sample with a simple sequential workflow."""
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# Set up the providers
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# This must be done before any other telemetry calls
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set_up_logging()
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set_up_tracing()
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set_up_metrics()
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tracer = trace.get_tracer("agent_framework")
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with tracer.start_as_current_span("Sequential Workflow Scenario", 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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# Run the sequential workflow scenario
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await run_sequential_workflow()
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if __name__ == "__main__":
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asyncio.run(main())
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