Agno Integration

Updated on Aug 14, 2026

Use this integration when your Agno application is implemented in Python.

Setup

1. Install SDK

bash

pip install progress-observability

2. Instrument your app

python

Observability.instrument(
    app_name=os.getenv("OBSERVABILITY_APP_NAME"),
    api_key=os.getenv("OBSERVABILITY_API_KEY")
)

3. Complete example

python

import os
from dotenv import load_dotenv
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from progress.observability import Observability

load_dotenv()

# LLM INSTRUMENTATION
Observability.instrument(
    app_name=os.getenv("OBSERVABILITY_APP_NAME"),
    api_key=os.getenv("OBSERVABILITY_API_KEY")
)

model = OpenAIChat(
 api_key=os.getenv("OPENAI_API_KEY"),
 id="gpt-4o-mini"
)


def get_weather(location: str) -> str:
 """Get the weather for a given location."""
 return f"The weather in {location} is cloudy with a high of 15°C."


agent = Agent(
 name="weather_agent_agno",
 model=model,
 tools=[get_weather],
 instructions="You are a helpful AI assistant."
)


def invoke_agent(payload: dict) -> dict:
 messages = payload.get("messages", [])
 user_message = messages[-1]["content"] if messages else ""
 response = agent.run(user_message)

 assistant_message = {"role": "assistant", "content": str(response.content)}
 return {"messages": [*messages, assistant_message]}


result = invoke_agent({
  "messages": [
    {"role": "user", "content": "What's the weather in Paris?"}
  ]
})

print(result["messages"][-1]["content"])

Observability.shutdown()