wehub-resource-sync
113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
import argparse
|
|
import asyncio
|
|
import os
|
|
|
|
from openai.types.responses import ResponseFunctionShellToolCall
|
|
from openai.types.responses.response_container_reference import ResponseContainerReference
|
|
|
|
from agents import Agent, Runner, ShellTool, ShellToolSkillReference, trace
|
|
from agents.items import ModelResponse
|
|
|
|
SHELL_SKILL_ID_ENV = "OPENAI_SHELL_SKILL_ID"
|
|
SHELL_SKILL_VERSION_ENV = "OPENAI_SHELL_SKILL_VERSION"
|
|
DEFAULT_SKILL_REFERENCE: ShellToolSkillReference = {
|
|
"type": "skill_reference",
|
|
"skill_id": "skill_698bbe879adc81918725cbc69dcae7960bc5613dadaed377",
|
|
"version": "1",
|
|
}
|
|
|
|
|
|
def resolve_skill_reference() -> ShellToolSkillReference:
|
|
skill_id = os.environ.get(SHELL_SKILL_ID_ENV)
|
|
if not skill_id:
|
|
return DEFAULT_SKILL_REFERENCE
|
|
|
|
reference: ShellToolSkillReference = {"type": "skill_reference", "skill_id": skill_id}
|
|
skill_version = os.environ.get(SHELL_SKILL_VERSION_ENV)
|
|
if skill_version:
|
|
reference["version"] = skill_version
|
|
return reference
|
|
|
|
|
|
def extract_container_id(raw_responses: list[ModelResponse]) -> str | None:
|
|
for response in raw_responses:
|
|
for item in response.output:
|
|
if isinstance(item, ResponseFunctionShellToolCall) and isinstance(
|
|
item.environment, ResponseContainerReference
|
|
):
|
|
return item.environment.container_id
|
|
|
|
return None
|
|
|
|
|
|
async def main(model: str) -> None:
|
|
skill_reference = resolve_skill_reference()
|
|
print(
|
|
"[info] Using skill reference:",
|
|
skill_reference["skill_id"],
|
|
f"(version {skill_reference.get('version', 'default')})",
|
|
)
|
|
|
|
with trace("container_shell_skill_reference_example"):
|
|
agent1 = Agent(
|
|
name="Container Shell Agent (Skill Reference)",
|
|
model=model,
|
|
instructions="Use the available container skill to answer user requests.",
|
|
tools=[
|
|
ShellTool(
|
|
environment={
|
|
"type": "container_auto",
|
|
"network_policy": {"type": "disabled"},
|
|
"skills": [skill_reference],
|
|
}
|
|
)
|
|
],
|
|
)
|
|
|
|
result1 = await Runner.run(
|
|
agent1,
|
|
(
|
|
"Use the csv-workbench skill. Create /mnt/data/orders.csv with columns "
|
|
"id,region,amount,status and at least 6 rows. Then report total amount by "
|
|
"region and count failed orders."
|
|
),
|
|
)
|
|
print(f"Agent: {result1.final_output}")
|
|
|
|
container_id = extract_container_id(result1.raw_responses)
|
|
if not container_id:
|
|
raise RuntimeError("Container ID was not returned in shell call output.")
|
|
|
|
print(f"[info] Reusing container_id={container_id}")
|
|
|
|
agent2 = Agent(
|
|
name="Container Reference Shell Agent",
|
|
model=model,
|
|
instructions="Reuse the existing shell container and answer concisely.",
|
|
tools=[
|
|
ShellTool(
|
|
environment={
|
|
"type": "container_reference",
|
|
"container_id": container_id,
|
|
}
|
|
)
|
|
],
|
|
)
|
|
|
|
result2 = await Runner.run(
|
|
agent2,
|
|
"Run `ls -la /mnt/data`, then summarize in one sentence.",
|
|
)
|
|
print(f"Agent (container reuse): {result2.final_output}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--model",
|
|
default="gpt-5.6-sol",
|
|
help="Model name to use.",
|
|
)
|
|
args = parser.parse_args()
|
|
asyncio.run(main(args.model))
|