Pydantic AI

Pydantic AI is an agent framework from the Pydantic team that emphasises type-safe agents, Pydantic-validated outputs, and tool use. Arize AX captures every Pydantic AI agent run — model calls, tool invocations, and structured-output validation — via the openinference-instrumentation-pydantic-ai package. Pydantic AI emits OpenTelemetry spans natively once instrumentation is enabled with Agent.instrument_all(); the OpenInference span processor reshapes them into the OpenInference format that Arize AX understands.

Prerequisites

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

Launch Arize AX

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

Install

pip install arize-otel \
  openinference-instrumentation-pydantic-ai \
  pydantic-ai openai

Configure credentials

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

Setup tracing

# instrumentation.py
import os

from arize.otel import BatchSpanProcessor, GRPCSpanExporter, register
from openinference.instrumentation.pydantic_ai import OpenInferenceSpanProcessor

space_id = os.environ["ARIZE_SPACE_ID"]
api_key = os.environ["ARIZE_API_KEY"]

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

# add_span_processor replaces register()'s default OTLP processor, so we
# add it back explicitly. The two-call pattern gives us:
# OpenInferenceSpanProcessor (reshapes spans) →
# GRPCSpanExporter (sends to Arize).
tracer_provider.add_span_processor(OpenInferenceSpanProcessor())
tracer_provider.add_span_processor(
    BatchSpanProcessor(GRPCSpanExporter(space_id=space_id, api_key=api_key))
)
print("Arize AX tracing initialized for Pydantic AI.")

Run Pydantic AI

# example.py

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

from pydantic import BaseModel
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel


class CityFact(BaseModel):
    city: str
    fact: str


# Enable instrumentation for every agent. In pydantic-ai 2.x this replaces
# the old `Agent(..., instrument=True)` constructor argument.
Agent.instrument_all()

# OpenAIChatModel reads OPENAI_API_KEY from the environment.
model = OpenAIChatModel("gpt-5.5")
agent = Agent(model, output_type=CityFact)

result = agent.run_sync("Give me one short fact about Paris.")
print(f"city={result.output.city} fact={result.output.fact}")

Expected output

Arize AX tracing initialized for Pydantic AI.
city=Paris fact=The Eiffel Tower was completed in 1889 for the World's Fair.

Verify in Arize AX

Open your Arize AX space and select project pydantic-ai-tracing-example.
You should see a new trace within ~30 seconds containing an agent run parent span wrapping a nested OpenAI chat-completion LLM span, with the prompt, response, and the validated CityFact output attached.
If no traces appear, see Troubleshooting.

Check from the skill, CLI, or SDK

Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language.

Arize skill (agent)
AX CLI
SDK

Install the Arize Skills plugin and let your coding agent check for you:

npx skills add Arize-ai/arize-skills

Then prompt your agent:

Use the arize-trace skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues.

Export recent spans for your project — any rows mean traces are landing:

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

A non-zero count confirms spans reached Arize AX. Run ax auth login first if you have not authenticated. See the ax spans reference.

Query the project’s spans and check that at least one came back.

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))

SDK span references: Python · TypeScript · Go.

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.
Agent ran but no spans appear. Pydantic AI only emits OTel spans once instrumentation is enabled. Call Agent.instrument_all() before running any agent. (In pydantic-ai 1.x this was the per-agent Agent(..., instrument=True) argument, which was removed in 2.0 — Agent.instrument_all() is the replacement.)
401 from OpenAI. Verify OPENAI_API_KEY is set and has access to gpt-5.5. Swap for a model your key can call.
Output validation errors. When the model returns content that doesn’t satisfy the output_type Pydantic model, Pydantic AI raises a validation error and may retry. Both the failed and successful attempts surface as separate spans.

OpenTelemetry

LLM Providers

Agent Frameworks

Coding Agents

Platforms

Orchestration

Evaluation

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run Pydantic AI

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Troubleshooting

Resources

Pydantic AI Documentation

OpenInference Pydantic AI Instrumentor

Pydantic AI GitHub

​Prerequisites

​Launch Arize AX

​Install

​Configure credentials

​Setup tracing

​Run Pydantic AI

​Expected output

​Verify in Arize AX

​Check from the skill, CLI, or SDK

​Troubleshooting

​Resources

Pydantic AI Documentation

OpenInference Pydantic AI Instrumentor

Pydantic AI GitHub

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run Pydantic AI

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Troubleshooting

Resources