OpenAI

The OpenAI plugin provides a drop-in replacement for the OpenAI Agents SDK function_tool decorator. It lets you use Flyte tasks as tools in agentic workflows so that tool calls run as tracked, reproducible Flyte task executions.

When to use this plugin

  • Building agentic workflows with the OpenAI Agents SDK on Flyte
  • You want tool calls to run as Flyte tasks with full observability, retries, and caching
  • You want to combine LLM agents with existing Flyte pipelines

Installation 

pip install flyteplugins-openai

Requires openai-agents >= 0.2.4.

Usage

The plugin provides a single decorator, function_tool, that wraps Flyte tasks as OpenAI agent tools.

function_tool

When applied to a Flyte task (a function decorated with @env.task), function_tool makes that task available as an OpenAI FunctionTool. The agent can call it like any other tool, and the call executes as a Flyte task.

When applied to a regular function or a @flyte.trace-decorated function, it delegates directly to the OpenAI Agents SDK’s built-in function_tool.

Basic pattern

  1. Define a TaskEnvironment with your image and secrets
  2. Decorate your task functions with @function_tool and @env.task
  3. Pass the tools to an Agent
  4. Run the agent from another Flyte task
from agents import Agent, Runner
from flyteplugins.openai.agents import function_tool

env = flyte.TaskEnvironment(
    name="openai_agents",
    resources=flyte.Resources(cpu=1, memory="250Mi"),
    image=flyte.Image.from_uv_script(__file__, name="openai_agents_image"),
    secrets=flyte.Secret("openai_api_key", as_env_var="OPENAI_API_KEY"),
)

@function_tool
@env.task
async def get_weather(city: str) -> Weather:
    """Get the weather for a given city."""
    return Weather(city=city, temperature_range="14-20C", conditions="Sunny")

agent = Agent(
    name="Weather Agent",
    instructions="You are a helpful agent.",
    tools=[get_weather],
)

@env.task
async def main() -> str:
    result = await Runner.run(agent, input="What's the weather in Tokyo?")
    return result.final_output

The docstring on each @function_tool task is sent to the LLM as the tool description. Write clear, concise docstrings that describe what the tool does and what its parameters mean.

Secrets

Store your OpenAI API key as a Flyte secret and expose it as an environment variable:

secrets=flyte.Secret("openai_api_key", as_env_var="OPENAI_API_KEY")

Example

agents_tools.py 
"""OpenAI Agents with Flyte, basic tool example.

Usage:

Create secret:

```
flyte create secret openai_api_key
uv run agents_tools.py
```
"""

# /// script
# requires-python = "==3.13"
# dependencies = [
#    "flyte>=2.0.0b52",
#    "flyteplugins-openai>=2.0.0b7",
#    "openai-agents>=0.2.4",
#    "pydantic>=2.10.6",
# ]
# main = "main"
# params = ""
# ///


from agents import Agent, Runner
from pydantic import BaseModel

import flyte
from flyteplugins.openai.agents import function_tool


env = flyte.TaskEnvironment(
    name="openai_agents_tools",
    resources=flyte.Resources(cpu=1, memory="250Mi"),
    image=flyte.Image.from_uv_script(__file__, name="openai_agents_image"),
    secrets=flyte.Secret("openai_api_key", as_env_var="OPENAI_API_KEY"),
)


class Weather(BaseModel):
    city: str
    temperature_range: str
    conditions: str


@function_tool
@env.task
async def get_weather(city: str) -> Weather:
    """Get the weather for a given city."""
    return Weather(city=city, temperature_range="14-20C", conditions="Sunny with wind.")


agent = Agent(
    name="Hello world",
    instructions="You are a helpful agent.",
    tools=[get_weather],
)


@env.task
async def main() -> str:
    result = await Runner.run(agent, input="What's the weather in Tokyo?")
    print(result.final_output)
    return result.final_output


if __name__ == "__main__":
    flyte.init_from_config()
    run = flyte.run(main)
    print(run.url)
    run.wait()

API reference

See the OpenAI API reference for full details.