Agno

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Prerequisites

• Python 3.10+
• An Arize AX account (sign up)
• An OPENAI_API_KEY from the OpenAI Platform

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Launch Arize AX

1. Sign in to your Arize AX account.
2. From Space Settings, copy your Space ID and API Key. You will set them as ARIZE_SPACE_ID and ARIZE_API_KEY below.

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Install

pip install arize-otel openinference-instrumentation-agno agno openai

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Configure credentials

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="agno-tracing-example"
export OPENAI_API_KEY="<your-openai-api-key>"

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Setup tracing

# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.agno import AgnoInstrumentor

tracer_provider = register(
    space_id=os.environ["ARIZE_SPACE_ID"],
    api_key=os.environ["ARIZE_API_KEY"],
    project_name=os.environ["ARIZE_PROJECT_NAME"],
)

AgnoInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for Agno.")

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Run Agno

# example.py

# Importing instrumentation first ensures tracing is set up
# before `agno` is imported.
from instrumentation import tracer_provider

from agno.agent import Agent
from agno.models.openai import OpenAIChat

# OpenAIChat reads OPENAI_API_KEY from the environment.
agent = Agent(model=OpenAIChat(id="gpt-5.5"))

response = agent.run("Why is the ocean salty? Answer in two sentences.")
print(response.content)

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Expected output

Arize AX tracing initialized for Agno.
The ocean is salty because rivers continuously dissolve mineral salts from rocks and soil and carry them to the sea, where they accumulate over millions of years. Water leaves the ocean through evaporation but the salts remain, steadily concentrating until reaching today's roughly 3.5% salinity.

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Verify in Arize AX

1. Open your Arize AX space and select project agno-tracing-example.
2. You should see a new trace within ~30 seconds containing an Agent.run parent span wrapping an OpenAIChat LLM child span with the prompt, response, and token usage attached.
3. If no traces appear, see Troubleshooting.

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Check from the skill, CLI, or SDK

npx skills add Arize-ai/arize-skills

ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \
  --limit 5 --stdout | jq 'length'

import os
from arize import ArizeClient

client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
resp = client.spans.list(
    project=os.environ["ARIZE_PROJECT_NAME"],
    space=os.environ["ARIZE_SPACE_ID"],
    limit=5,
)
count = len(resp.spans)
print(
    f"{count} span(s) found" if count else "No spans yet — recheck setup"
)

// Reads ARIZE_API_KEY from the environment.
import { listSpans } from "@arizeai/ax-client";

const { data: spans } = await listSpans({
  project: process.env.ARIZE_PROJECT_NAME!,
  space: process.env.ARIZE_SPACE_ID!,
  limit: 5,
});
const count = spans.length;
console.log(
  count ? `${count} span(s) found` : "No spans yet — recheck setup",
);

client, err := arize.NewClient(
    arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")},
)
if err != nil {
    log.Fatal(err)
}
resp, err := client.Spans.List(ctx, spans.ListRequest{
    Project: os.Getenv("ARIZE_PROJECT_NAME"),
    Space:   os.Getenv("ARIZE_SPACE_ID"),
    Limit:   5,
})
if err != nil {
    log.Fatal(err)
}
fmt.Printf("%d span(s) found\n", len(resp.Spans))

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Troubleshooting

• No traces in Arize AX. Confirm ARIZE_SPACE_ID and ARIZE_API_KEY are set in the same shell that runs example.py. Enable OpenTelemetry debug logs with export OTEL_LOG_LEVEL=debug and re-run.
• Agno spans missing but other spans present. AgnoInstrumentor().instrument(...) must run before any from agno import .... Make sure instrumentation.py is the first import in your entry point.
• 401 from OpenAI. Verify OPENAI_API_KEY is set and has access to gpt-5.5. Swap for a model your key can call.
• Other LLM providers. Agno supports many models — from agno.models.anthropic import Claude, from agno.models.groq import Groq, etc. The same AgnoInstrumentor covers them.

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Resources