Pydantic AI

A Pydantic AI agent runs on Rebuno unchanged apart from two seams. The OpenAI client under the model takes a Rebuno HTTP client, so every model call is recorded as an llm_call step, and tools are declared with @tool, so every tool call is recorded as a tool_call step. On a re-dispatch the agent runs from the top and recorded steps replay instead of calling the model or the tool again.

Install

pip install rebuno pydantic-ai

Model calls

Build the AsyncOpenAI client yourself with http_client(), and hand it to the model through OpenAIProvider:

from openai import AsyncOpenAI
from pydantic_ai.models.openai import OpenAIResponsesModel
from pydantic_ai.providers.openai import OpenAIProvider
from rebuno import http_client

client = AsyncOpenAI(http_client=http_client())
model = OpenAIResponsesModel("gpt-5.5", provider=OpenAIProvider(openai_client=client))

See LLM calls for the OpenAI SDK versions that accept the client.

Tools

@tool keeps the function’s signature and docstring, so Pydantic AI builds the tool schema from it as usual:

from rebuno import tool


@tool("lookup_orders", idempotency="safe_to_retry")
async def lookup_orders(customer_id: str) -> dict:
    """Look up a customer's recent orders."""
    ...


@tool("send_email", idempotency="at_most_once")
async def send_email(body: str) -> dict:
    """Email the support summary."""
    ...

Mark anything with a side effect, such as sending an email or creating a ticket, at_most_once. See idempotency.

The agent

Pydantic AI’s Agent and Rebuno’s Agent share a name, so import one under an alias:

from pydantic_ai import Agent as PydanticAgent
from rebuno import Agent


async def process(query: str) -> dict:
    client = AsyncOpenAI(http_client=http_client())
    model = OpenAIResponsesModel("gpt-5.5", provider=OpenAIProvider(openai_client=client))
    result = await PydanticAgent(model, tools=[lookup_orders, send_email]).run(query)
    return {"answer": result.output}


agent = Agent("support")

if __name__ == "__main__":
    agent.run(process)

What Rebuno adds

Policy. Every model and tool call is checked against policy before it runs. A denied tool returns the rule’s reason to the model as the tool result, so the agent can take a different path.
Approvals. A tool that requires approval parks the execution. Once it’s approved, the agent is dispatched again, the earlier steps replay, and the approved call runs.
Recovery. If the worker dies partway through, the next dispatch replays every completed step and continues from the first one that didn’t finish. Model calls that already ran are not paid for twice.

Full example

examples/frameworks/python/pydantic_ai_agent.py is a support agent that investigates a customer issue, creates a ticket, and emails a summary after approval.

Get started

Writing agents

Frameworks

Reference

Install

Model calls

Tools

The agent

What Rebuno adds

Full example

​Install

​Model calls

​Tools

​The agent

​What Rebuno adds

​Full example

Install

Model calls

Tools

The agent

What Rebuno adds

Full example