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Gpt Oss

Runnable example (intermediate) for script using pydantic.

Key Facts

Level
intermediate
Runtime
Python • Pydantic
Pattern
Inspectable flow with visible system boundaries
Interaction
Live sandbox • Script
Updated
14 March 2026

Navigate this example

High-level flow

How this example moves from input to execution and reviewable output
Gpt Oss -> Constrain output schema -> Render the visible result

Trigger

Gpt Oss

Runtime

Constrain output schema

Outcome

Render the visible result

Why this page exists

This example is shown as both real source code and a product-facing interaction pattern so learners can connect implementation, UX, and doctrine without leaving the library.

Visual flowReal sourceSandbox or walkthroughMCP access
How should this example be used in the platform?

Use the sandbox to understand the experience pattern first, then inspect the source to see how the product boundary, model boundary, and doctrine boundary are actually implemented.

UX pattern: Inspectable flow with visible system boundaries
Source references
Library entry
models-openai-07-gpt-oss-gpt-oss
Source path
content/example-library/sources/models/openai/07-gpt-oss/gpt-oss.py
Libraries
pydantic
Runtime requirements
Local repo environment
Related principles
—

gpt-oss.py

python
from pydantic import BaseModel
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider
import nest_asyncio

nest_asyncio.apply()


class CityLocation(BaseModel):
    city: str
    country: str


ollama_model = OpenAIChatModel(
    model_name="gpt-oss:20b",
    provider=OpenAIProvider(base_url="http://localhost:11434/v1"),
)

agent = Agent(
    ollama_model,
    output_type=CityLocation,
)

result = agent.run_sync("Where were the olympics held in 2012?")
print(result.output)
What should the learner inspect in the code?

Look for the exact place where system scope is bounded: schema definitions, prompt framing, runtime configuration, and the call site that turns user intent into a concrete model or workflow action.

Look for output contracts and validation
Look for the exact execution call
Look for what the product could expose to the user
How does the sandbox relate to the source?

The sandbox should make the UX legible: what the user sees, what the system is deciding, and how the result becomes reviewable. The source then shows how that behavior is actually implemented.

Read the implementation summary.
Step through the user and system states.
Inspect the source code with the highlighted doctrine decisions in mind.
SandboxInspectable flow with visible system boundaries
Interaction walkthrough

Use the sandbox to step through the user-visible experience, the system work behind it, and the doctrine choice the example is making.

UX explanation

The sandbox explains what the user should see, what the system is doing, and where control or inspectability must remain explicit.

AI design explanation

The page turns raw source into a product-facing pattern: what the model is allowed to decide, what the product should expose, and where deterministic code or review should take over.

Interaction walkthrough

  1. 1Read the implementation summary.
  2. 2Step through the user and system states.
  3. 3Inspect the source code with the highlighted doctrine decisions in mind.

Visible to the user

A script demonstrating pydantic.

System work

The product prepares a bounded model or workflow task.

Why it matters

The interface should make the delegated task legible before automation happens.

Used in courses and paths

This example currently stands on its own in the library, but it still connects to the principle system and the broader example family.

Related implementation patterns

Related principles

    Runtime architecture

    Use this example in your agents

    This example is also available through the blueprint’s agent-ready layer. Use the For agents page for the public MCP, deterministic exports, and Claude/Cursor setup.

    Define triggers, context, and boundaries before increasing autonomy
    Make control, observability, and recovery explicit in the runtime
    Choose the right operational patterns before delegating to workflows
    Open For agents