wehub-resource-sync
118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
import argparse
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import asyncio
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import base64
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from pathlib import Path
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from tempfile import TemporaryDirectory
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from zipfile import ZIP_DEFLATED, ZipFile
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from openai.types.responses import ResponseFunctionShellToolCall
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from openai.types.responses.response_container_reference import ResponseContainerReference
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from agents import Agent, Runner, ShellTool, ShellToolInlineSkill, trace
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from agents.items import ModelResponse
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SKILL_NAME = "csv-workbench"
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SKILL_DIR = Path(__file__).resolve().parent / "skills" / SKILL_NAME
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def build_skill_zip_bundle() -> bytes:
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with TemporaryDirectory(prefix="agents-inline-skill-") as temp_dir:
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zip_path = Path(temp_dir) / f"{SKILL_NAME}.zip"
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with ZipFile(zip_path, "w", compression=ZIP_DEFLATED) as archive:
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for path in sorted(SKILL_DIR.rglob("*")):
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if path.is_file():
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archive.write(path, f"{SKILL_NAME}/{path.relative_to(SKILL_DIR)}")
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return zip_path.read_bytes()
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def build_inline_skill() -> ShellToolInlineSkill:
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bundle = build_skill_zip_bundle()
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return {
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"type": "inline",
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"name": SKILL_NAME,
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"description": "Analyze CSV files in /mnt/data and return concise numeric summaries.",
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"source": {
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"type": "base64",
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"media_type": "application/zip",
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"data": base64.b64encode(bundle).decode("ascii"),
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},
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}
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def extract_container_id(raw_responses: list[ModelResponse]) -> str | None:
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for response in raw_responses:
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for item in response.output:
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if isinstance(item, ResponseFunctionShellToolCall) and isinstance(
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item.environment, ResponseContainerReference
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):
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return item.environment.container_id
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return None
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async def main(model: str) -> None:
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inline_skill = build_inline_skill()
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with trace("container_shell_inline_skill_example"):
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agent1 = Agent(
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name="Container Shell Agent (Inline Skill)",
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model=model,
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instructions="Use the available container skill to answer user requests.",
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tools=[
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ShellTool(
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environment={
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"type": "container_auto",
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"network_policy": {"type": "disabled"},
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"skills": [inline_skill],
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}
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)
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],
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)
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result1 = await Runner.run(
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agent1,
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(
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"Use the csv-workbench skill. Create /mnt/data/orders.csv with columns "
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"id,region,amount,status and at least 6 rows. Then report total amount by "
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"region and count failed orders."
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),
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)
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print(f"Agent: {result1.final_output}")
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container_id = extract_container_id(result1.raw_responses)
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if not container_id:
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raise RuntimeError("Container ID was not returned in shell call output.")
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print(f"[info] Reusing container_id={container_id}")
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agent2 = Agent(
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name="Container Reference Shell Agent",
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model=model,
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instructions="Reuse the existing shell container and answer concisely.",
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tools=[
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ShellTool(
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environment={
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"type": "container_reference",
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"container_id": container_id,
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}
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)
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],
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)
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result2 = await Runner.run(
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agent2,
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"Run `ls -la /mnt/data`, then summarize in one sentence.",
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)
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print(f"Agent (container reuse): {result2.final_output}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model",
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default="gpt-5.6-sol",
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help="Model name to use.",
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)
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args = parser.parse_args()
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asyncio.run(main(args.model))
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