OpenAI Agents SDK

The OpenAI Agents SDK is best paired with Xenovia through the Responses API (POST /v1/responses). Xenovia supports that endpoint natively, so each agent turn is traced and policy-checked without changing your orchestration flow.

Setup

pip install openai-agents
export XENOVIA_API_KEY=xe_...
export XENOVIA_AGENT_ID=your-agent-id

import os
from openai import AsyncOpenAI
from agents import Agent, Runner, OpenAIResponsesModel

client = AsyncOpenAI(
    api_key=os.environ["XENOVIA_API_KEY"],
    base_url=f"https://runtime.xenovia.io/{os.environ['XENOVIA_AGENT_ID']}/v1"
)

model = OpenAIResponsesModel(
    model="gpt-4o-mini",
    openai_client=client
)

Single agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    model=model
)

result = await Runner.run(agent, "What is the capital of France?")
print(result.final_output)

Each Runner.run call may produce multiple Responses API turns. Each turn creates an independent trace in Xenovia. Use a session ID to group them.

Sessions via `previous_response_id`

The Responses API natively chains turns using previous_response_id. Xenovia’s session plugin recognises this field and automatically resolves sessions across chained calls, so you do not need an explicit session ID header if the SDK manages previous_response_id for you. To also group runs explicitly:

import uuid

session_id = str(uuid.uuid4())

client = AsyncOpenAI(
    api_key=os.environ["XENOVIA_API_KEY"],
    base_url=f"https://runtime.xenovia.io/{os.environ['XENOVIA_AGENT_ID']}/v1",
    default_headers={"X-Xenovia-Session-Id": session_id}
)

Explicit X-Xenovia-Session-Id takes priority over the previous_response_id chain. Use explicit IDs when you need grouping across separate Runner.run calls.

Multi-agent handoff

research_agent = Agent(
    name="Researcher",
    instructions="Search and summarise information on the given topic.",
    model=model
)

writer_agent = Agent(
    name="Writer",
    instructions="Write a clear report based on provided research.",
    model=model,
    handoffs=[research_agent]
)

result = await Runner.run(writer_agent, "Write a report on AI governance")
print(result.final_output)

Each agent in the handoff chain calls the Responses API independently. All calls are traced individually. Open the session in Sessions to see the full conversation in order.

Agents with tools

from agents import function_tool

@function_tool
def get_order_status(order_id: str) -> str:
    """Look up the status of an order."""
    return f"Order {order_id} has shipped"

agent = Agent(
    name="OrderDesk",
    instructions="Look up order status when asked.",
    tools=[get_order_status],
    model=model
)

Tool names defined in the agent are included in input.tool_names in the Rego policy. A block rule matching the tool name returns 403 before the model is called.

Handling policy blocks

from openai import PermissionDeniedError

try:
    result = await Runner.run(agent, "Delete all user records")
except PermissionDeniedError as e:
    print(f"Blocked by policy: {e.message}")

The X-Xenovia-Trace-Id header on the 403 response identifies the blocking trace in the platform.

Start Here

Integrations

Platform

OpenAI Agents SDK

Setup

Single agent

Sessions via `previous_response_id`

Multi-agent handoff

Agents with tools

Handling policy blocks

​Setup

​Single agent

​Sessions via previous_response_id

​Multi-agent handoff

​Agents with tools

​Handling policy blocks

Setup

Single agent

Sessions via `previous_response_id`

Multi-agent handoff

Agents with tools

Handling policy blocks