Pydantic AI

Trace agents built with Pydantic AI  with Rius: the framework is OpenTelemetry-native and emits its own spans for agent runs, model requests, and tool calls, so the whole structure arrives with no wrappers at all. This guide covers the Python framework; the runnable example behind it lives in agent-observability-examples .

How coverage works

This integration works the other way around from the client-library ones: no auto-instrumentation extra is involved. rius.init() installs the global OpenTelemetry tracer provider, and Agent.instrument_all(True) tells Pydantic AI to emit its spans through it. The framework provides the run root, the generations, and the tool spans itself; Rius reads its OpenTelemetry GenAI attributes natively and computes cost server-side.

Setup

pip install glassflow-rius pydantic-ai

No extra is needed. If your project uses the base SDK only, that is enough.

import rius
from pydantic_ai import Agent
 
rius.init(api_key="gf_...", service_name="my-agent")
Agent.instrument_all(True)
 
support_agent = Agent(
    "openai:gpt-5-mini",
    instructions="Answer support questions; check accounts with the tool.",
)
 
 
@support_agent.tool_plain
def account_status(email: str) -> str:
    """Fetch a customer's account standing by email."""
    return lookup(email)
 
 
with rius.session(conversation_id):
    result = support_agent.run_sync(ticket)

instrument_all switches instrumentation on for every agent; per-agent control and content settings live on the framework’s InstrumentationSettings.

What lands where

A run that calls a tool looks like this (real trace from the example, pydantic-ai-slim 2.36.0):

invoke_agent support_agent           6.0s
├─ chat gpt-5-mini                   3.3s   decides to call the tool
├─ execute_tool search_knowledge_base 0ms
└─ chat gpt-5-mini                   2.7s   answers

Compared to frameworks covered through a client-library integration, this is the richer default: spans are named after your agents, models, and tools, and tool calls appear without any wrapping.

Quirks

Verified against pydantic-ai-slim 2.36.0:

• Span statuses arrive UNSET rather than OK on successful spans; the trace status still reads Ok.
• Content capture is framework-side. What the spans carry (prompts, completions, tool arguments) is controlled by Pydantic AI’s InstrumentationSettings (for example include_content), on top of the SDK’s own privacy controls at export time.

Sessions

One conversation is one session: scope each run with rius.session(), reusing your conversation id. The framework’s spans inherit the scope like any other span.

Verify

Run one request that calls a tool, then open the trace in the console: the invoke_agent root, chat generations with model, tokens, and cost, and execute_tool spans should all be there.