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Add OpenTelemetry + Aspire Dashboard example
- Add agent_otel_aspire.py: tool-calling agent that exports traces, metrics, and structured logs to the .NET Aspire Dashboard via OTLP/gRPC - Telemetry conditionally enabled when OTEL_EXPORTER_OTLP_ENDPOINT is set - Add opentelemetry-exporter-otlp-proto-grpc dependency - Add Aspire Dashboard setup instructions to README
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‎README.md‎

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| [agent_mcp_local.py](examples/agent_mcp_local.py) | An agent connected to a local MCP server (e.g. for expense logging). |
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| [openai_tool_calling.py](examples/openai_tool_calling.py) | Tool calling with the low-level OpenAI SDK, showing manual tool dispatch. |
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| [workflow_basic.py](examples/workflow_basic.py) | A workflow-based agent. |
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| [agent_otel_aspire.py](examples/agent_otel_aspire.py) | An agent with OpenTelemetry tracing, metrics, and structured logs exported to the [.NET Aspire Dashboard](https://aspire.dev/dashboard/standalone/). |
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## Using the Aspire Dashboard for telemetry
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The [agent_otel_aspire.py](examples/agent_otel_aspire.py) example can export OpenTelemetry traces, metrics, and structured logs to the [.NET Aspire Dashboard](https://aspire.dev/dashboard/standalone/), a free standalone container for visualizing telemetry.
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1. Start the Aspire Dashboard:
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```shell
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docker run --rm -it -d \
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-p 18888:18888 \
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-p 4317:18889 \
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--name aspire-dashboard \
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mcr.microsoft.com/dotnet/aspire-dashboard:latest
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```
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2. Get the login token from the container logs:
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```shell
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docker logs aspire-dashboard
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```
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Look for the line containing `Login to the dashboard at http://localhost:18888/login?t=<TOKEN>`. Copy the token or open the URL directly.
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3. Run the example with telemetry export enabled:
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```shell
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OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 uv run python examples/agent_otel_aspire.py
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```
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4. Open the dashboard at <http://localhost:18888> and explore:
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* **Traces**: See the full span tree — agent invocation → chat completion → tool execution
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* **Metrics**: View token usage and operation duration histograms
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* **Structured Logs**: Browse conversation messages (system, user, assistant, tool)
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* **GenAI visualizer**: Select a chat completion span to see the rendered conversation
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Without `OTEL_EXPORTER_OTLP_ENDPOINT` set, the example runs normally with no telemetry export and no errors.
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5. When done, stop the dashboard:
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```shell
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docker stop aspire-dashboard
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```
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For the full Python + Aspire guide, see [Use the Aspire dashboard with Python apps](https://aspire.dev/dashboard/standalone-for-python/).
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## Resources
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‎examples/agent_otel_aspire.py‎

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"""
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OpenTelemetry + Aspire Dashboard example.
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Demonstrates a tool-calling agent that exports OpenTelemetry traces, metrics,
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and structured logs to the .NET Aspire Dashboard via OTLP/gRPC.
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Telemetry is only exported when the OTEL_EXPORTER_OTLP_ENDPOINT environment
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variable is set. Without it, the agent runs normally with no telemetry export.
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To start the Aspire Dashboard:
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docker run --rm -it -d \
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-p 18888:18888 \
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-p 4317:18889 \
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--name aspire-dashboard \
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mcr.microsoft.com/dotnet/aspire-dashboard:latest
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The dashboard UI is at http://localhost:18888.
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Get the login token from the container logs:
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docker logs aspire-dashboard
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Look for: "Login to the dashboard at http://localhost:18888/login?t=<TOKEN>"
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Then run this example with telemetry export enabled:
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OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 python examples/agent_otel_aspire.py
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In the Aspire Dashboard you will see:
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- Traces: agent -> chat completion -> tool execution spans
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- Metrics: token usage and operation duration histograms
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- Structured Logs: conversation messages (system, user, assistant, tool)
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- GenAI telemetry visualizer: full conversation view on chat spans
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To stop the dashboard:
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docker stop aspire-dashboard
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For the full Python + Aspire guide, see:
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https://aspire.dev/dashboard/standalone-for-python/
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"""
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import asyncio
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import logging
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import os
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import random
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from datetime import datetime, timezone
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from typing import Annotated
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from agent_framework import ChatAgent
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from agent_framework.observability import configure_otel_providers
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from agent_framework.openai import OpenAIChatClient
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from azure.identity.aio import DefaultAzureCredential, get_bearer_token_provider
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from dotenv import load_dotenv
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from pydantic import Field
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from rich import print
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from rich.logging import RichHandler
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# Setup logging
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handler = RichHandler(show_path=False, rich_tracebacks=True, show_level=False)
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logging.basicConfig(level=logging.WARNING, handlers=[handler], force=True, format="%(message)s")
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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# Configure OpenTelemetry export to the Aspire Dashboard (if endpoint is set)
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otlp_endpoint = os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT")
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if otlp_endpoint:
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os.environ.setdefault("OTEL_EXPORTER_OTLP_PROTOCOL", "grpc")
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os.environ.setdefault("OTEL_SERVICE_NAME", "agent-framework-demo")
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configure_otel_providers(enable_sensitive_data=True)
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logger.info(f"OpenTelemetry export enabled — sending to {otlp_endpoint}")
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else:
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logger.info("Set OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 to export telemetry to the Aspire Dashboard")
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# Configure OpenAI client based on environment
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST", "github")
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async_credential = None
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if API_HOST == "azure":
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async_credential = DefaultAzureCredential()
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token_provider = get_bearer_token_provider(async_credential, "https://cognitiveservices.azure.com/.default")
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client = OpenAIChatClient(
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base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
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api_key=token_provider,
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model_id=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
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)
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elif API_HOST == "github":
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client = OpenAIChatClient(
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base_url="https://models.github.ai/inference",
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api_key=os.environ["GITHUB_TOKEN"],
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model_id=os.getenv("GITHUB_MODEL", "openai/gpt-5-mini"),
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)
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else:
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client = OpenAIChatClient(
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api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-5-mini")
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)
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def get_weather(
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city: Annotated[str, Field(description="City name, spelled out fully")],
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) -> dict:
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"""Returns weather data for a given city, a dictionary with temperature and description."""
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logger.info(f"Getting weather for {city}")
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weather_options = [
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{"temperature": 72, "description": "Sunny"},
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{"temperature": 60, "description": "Rainy"},
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{"temperature": 55, "description": "Cloudy"},
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{"temperature": 45, "description": "Windy"},
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]
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return random.choice(weather_options)
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def get_current_time(
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timezone_name: Annotated[str, Field(description="Timezone name, e.g. 'US/Eastern', 'Asia/Tokyo', 'UTC'")],
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) -> str:
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"""Returns the current date and time in UTC (timezone_name is for display context only)."""
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logger.info(f"Getting current time for {timezone_name}")
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now = datetime.now(timezone.utc)
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return f"The current time in {timezone_name} is approximately {now.strftime('%Y-%m-%d %H:%M:%S')} UTC"
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agent = ChatAgent(
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name="weather-time-agent",
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chat_client=client,
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instructions="You are a helpful assistant that can look up weather and time information.",
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tools=[get_weather, get_current_time],
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)
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async def main():
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response = await agent.run("What's the weather in Seattle and what time is it in Tokyo?")
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print(response.text)
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if async_credential:
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await async_credential.close()
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if __name__ == "__main__":
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asyncio.run(main())

‎pyproject.toml‎

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"aiohttp",
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"faker",
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"fastmcp",
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"opentelemetry-exporter-otlp-proto-grpc",
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"agent-framework-core @ git+https://github.com/microsoft/agent-framework.git@98cd72839e4057d661a58092a3b013993264d834#subdirectory=python/packages/core",
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"agent-framework-devui @ git+https://github.com/microsoft/agent-framework.git@98cd72839e4057d661a58092a3b013993264d834#subdirectory=python/packages/devui",
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]

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