Autogen

1
# First we will configure the OpenTelemetry
2
from opentelemetry import trace
3
from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
4
    OTLPSpanExporter
5
)
6
from opentelemetry.sdk.resources import Resource
7
from opentelemetry.sdk.trace import TracerProvider
8
from opentelemetry.sdk.trace.export import BatchSpanProcessor
9
from opentelemetry.instrumentation.openai import OpenAIInstrumentor
10
11
def setup_telemetry():
12
    """Configure OpenTelemetry with HTTP exporter"""
13
    # Create a resource with service name and other metadata
14
    resource = Resource.create({
15
        "service.name": "autogen-demo",
16
        "service.version": "1.0.0",
17
        "deployment.environment": "development"
18
    })
19
20
    # Create TracerProvider with the resource
21
    provider = TracerProvider(resource=resource)
22
23
    # Create BatchSpanProcessor with OTLPSpanExporter
24
    processor = BatchSpanProcessor(
25
        OTLPSpanExporter()
26
    )
27
    provider.add_span_processor(processor)
28
29
    # Set the TracerProvider
30
    trace.set_tracer_provider(provider)
31
32
    # Instrument OpenAI calls
33
    OpenAIInstrumentor().instrument()
34
35
# Now we can define and call the Agent
36
import asyncio
37
from autogen_agentchat.agents import AssistantAgent
38
from autogen_agentchat.ui import Console
39
from autogen_ext.models.openai import OpenAIChatCompletionClient
40
41
42
# Define a model client. You can use other model client that implements
43
# the `ChatCompletionClient` interface.
44
model_client = OpenAIChatCompletionClient(
45
    model="gpt-4o",
46
    # api_key="YOUR_API_KEY",
47
)
48
49
# Define a simple function tool that the agent can use.
50
# For this example, we use a fake weather tool for demonstration purposes.
51
async def get_weather(city: str) -> str:
52
    """Get the weather for a given city."""
53
    return f"The weather in {city} is 73 degrees and Sunny."
54
55
56
# Define an AssistantAgent with the model, tool, system message, and reflection
57
# enabled. The system message instructs the agent via natural language.
58
agent = AssistantAgent(
59
    name="weather_agent",
60
    model_client=model_client,
61
    tools=[get_weather],
62
    system_message="You are a helpful assistant.",
63
    reflect_on_tool_use=True,
64
    model_client_stream=True,  # Enable streaming tokens from the model client.
65
)
66
67
68
# Run the agent and stream the messages to the console.
69
async def main() -> None:
70
    tracer = trace.get_tracer(__name__)
71
    with tracer.start_as_current_span("agent_conversation") as span:
72
        task = "What is the weather in New York?"
73
74
        span.set_attribute("input", task) # Manually log the question
75
        res = await Console(agent.run_stream(task=task))
76
77
        # Manually log the response
78
        span.set_attribute("output", res.messages[-1].content)
79
80
        # Close the connection to the model client.
81
        await model_client.close()
82
83
84
if __name__ == "__main__":
85
    setup_telemetry()
86
    asyncio.run(main())