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Pydantic AI is the official agent framework by the Pydantic team. It supports typed structured output via output_type and works with any OpenAI-compatible endpoint. For dottxt, output_type is the important integration point: Pydantic AI derives JSON Schema from your output model, sends that schema through the OpenAI-compatible API, and parses the result back into a typed object.

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Install

pip install pydantic-ai openai pydantic

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Configure

Create an OpenAIProvider pointed at dottxt, then wrap it in an OpenAIChatModel: 
import os
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

provider = OpenAIProvider(
    base_url="https://api.dottxt.ai/v1",
    api_key=os.environ["DOTTXT_API_KEY"],
)
model = OpenAIChatModel("openai/gpt-oss-20b", provider=provider)

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Basic usage

Pass a Pydantic model as output_type to get typed structured output: 
from pydantic import BaseModel, ConfigDict, Field
from pydantic_ai import Agent

class CityLocation(BaseModel):
    model_config = ConfigDict(extra="forbid")

    city: str = Field(description="City name")
    country: str = Field(description="Country name")

agent = Agent(model, output_type=CityLocation)

result = agent.run_sync("Where were the 2012 Olympics held?")
print(result.output)
# CityLocation(city='London', country='United Kingdom')
Pydantic AI generates the schema from output_type and parses the response back into result.output. Under the hood, this still uses the same dottxt structured generation flow described in API Overview and Pydantic Authoring.

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Agent with dependencies and tools

Use deps_type to inject runtime context, and @agent.tool to give the agent callable functions: 
from dataclasses import dataclass
from typing import Literal
from pydantic import BaseModel, ConfigDict, Field
from pydantic_ai import Agent, RunContext

@dataclass
class SupportDeps:
    customer_name: str
    account_id: int

class SupportResponse(BaseModel):
    model_config = ConfigDict(extra="forbid")

    greeting: str
    answer: str
    follow_up_question: str = Field(min_length=8)
    priority: Literal["low", "medium", "high"]

agent = Agent(
    model,
    output_type=SupportResponse,
    deps_type=SupportDeps,
    instructions="You are a helpful customer support agent. Always greet the customer by name.",
)

@agent.tool
def get_account_status(ctx: RunContext[SupportDeps]) -> str:
    """Look up the account status for the current customer."""
    return f"Account #{ctx.deps.account_id} is active and in good standing."

result = agent.run_sync(
    "What is the status of my account?",
    deps=SupportDeps(customer_name="Alice", account_id=42),
)
print(result.output.greeting)
print(result.output.answer)

​
Notes

  • Use output_type, not result_type; the latter was removed in Pydantic AI v0.6.0.
  • agent.run() is async, agent.run_sync() is synchronous, agent.run_stream() is async streaming.
  • The result is accessed via result.output, typed according to output_type.
  • ConfigDict(extra="forbid") is useful when you want additionalProperties: false in the generated schema.
  • See the Pydantic authoring guide for how to write effective schemas.