Phoenix provides seamless observability and tracing for Agno agents through the OpenInference instrumentation package. This integration automatically captures agent interactions, tool usage, reasoning steps, and multi-agent conversations, giving you complete visibility into your Agno applications. Monitor performance, debug issues, and evaluate agent behavior in real-time as your agents execute complex workflows and collaborate in teams.
Agno is a lightweight, high-performance Python framework for building AI agents with tools, memory, and reasoning capabilities. It enables developers to create autonomous agents that can perform complex tasks, access knowledge bases, and collaborate in multi-agent teams. With support for 23+ model providers and lightning-fast performance (~3μs instantiation), Agno is designed for production-ready AI applications.
Model Agnostic: Connect to OpenAI, Anthropic, Google, and 20+ other providers
Lightning Fast: Agents instantiate in ~3μs with minimal memory footprint
Built-in Reasoning: First-class support for chain-of-thought and reasoning models
Multi-Modal: Native support for text, image, audio, and video processing
Agentic RAG: Advanced retrieval-augmented generation with hybrid search
Multi-Agent Teams: Coordinate multiple agents for complex workflows
Production Ready: Pre-built FastAPI routes and monitoring capabilities
pip install openinference-instrumentation-agno agnoUse the register function to connect your application to Phoenix:
from phoenix.otel import register
# configure the Phoenix tracer
tracer_provider = register(
project_name="my-llm-app", # Default is 'default'
auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
tools=[DuckDuckGoTools()],
markdown=True,
debug_mode=True,
)
agent.run("What is currently trending on Twitter?")Now that you have tracing setup, all invocations of Agno agents will be streamed to Phoenix for observability and evaluation.
Agno is an open-source Python framework for building lightweight, model-agnostic AI agents with built-in memory, knowledge, tools, and reasoning capabilities
Website: https://www.agno.com/
Sign up for Phoenix:
Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
Click Create Space, then follow the prompts to create and launch your space.
Install packages:
pip install arize-phoenix-otelSet your Phoenix endpoint and API Key:
From your new Phoenix Space
Create your API key from the Settings page
Copy your Hostname from the Settings page
In your code, set your endpoint and API key:
import os
os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX HOSTNAME"
# If you created your Phoenix Cloud instance before June 24th, 2025,
# you also need to set the API key as a header:
# os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('PHOENIX_API_KEY')}"Launch your local Phoenix instance:
pip install arize-phoenix
phoenix serveFor details on customizing a local terminal deployment, see Terminal Setup.
Install packages:
pip install arize-phoenix-otelSet your Phoenix endpoint:
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"See Terminal for more details.
Pull latest Phoenix image from Docker Hub:
docker pull arizephoenix/phoenix:latestRun your containerized instance:
docker run -p 6006:6006 arizephoenix/phoenix:latestThis will expose the Phoenix on localhost:6006
Install packages:
pip install arize-phoenix-otelSet your Phoenix endpoint:
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"For more info on using Phoenix with Docker, see Docker.
Install packages:
pip install arize-phoenixLaunch Phoenix:
import phoenix as px
px.launch_app()