chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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"""Show how a sandbox agent can keep using the same interactive Python process.
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This example uses the Unix-local sandbox with the `Shell` capability. The task only asks
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for a stateful interaction, but the streamed output shows the actual shell tools the agent
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chooses, including the follow-up writes that keep the same process alive.
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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import sys
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from pathlib import Path
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from openai.types.responses import ResponseTextDeltaEvent
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from agents import ModelSettings, Runner
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from agents.run import RunConfig
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from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig
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from agents.sandbox.capabilities import Shell
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from agents.sandbox.entries import File
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from agents.sandbox.sandboxes.unix_local import UnixLocalSandboxClient
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if __package__ is None or __package__ == "":
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sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
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from examples.sandbox.misc.example_support import tool_call_name
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DEFAULT_MODEL = "gpt-5.6-sol"
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DEFAULT_QUESTION = (
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"Start an interactive Python session. In that same session, compute `5 + 5`, then add "
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"5 more to the previous result. Briefly report the outputs and confirm that you stayed "
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"in one Python process."
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)
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def _build_manifest() -> Manifest:
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return Manifest(
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entries={
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"README.md": File(
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content=(
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b"# Unix-local PTY Agent Example\n\n"
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b"This workspace is used by examples/sandbox/unix_local_pty.py.\n"
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)
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),
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}
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)
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def _build_agent(model: str) -> SandboxAgent:
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return SandboxAgent(
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name="Unix-local PTY Demo",
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model=model,
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instructions=(
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"Complete the task by inspecting and interacting with the sandbox through the shell "
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"capability. Keep the final answer concise. "
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"Preserve process state when the task depends on it. If you start an interactive "
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"program, continue using that same process instead of launching a second one."
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),
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default_manifest=_build_manifest(),
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capabilities=[Shell()],
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model_settings=ModelSettings(tool_choice="required"),
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)
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def _stream_event_banner(event_name: str, raw_item: object) -> str | None:
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_ = raw_item
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if event_name == "tool_called":
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return "[tool call]"
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if event_name == "tool_output":
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return "[tool output]"
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return None
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def _raw_item_call_id(raw_item: object) -> str | None:
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if isinstance(raw_item, dict):
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call_id = raw_item.get("call_id") or raw_item.get("id")
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else:
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call_id = getattr(raw_item, "call_id", None) or getattr(raw_item, "id", None)
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return call_id if isinstance(call_id, str) and call_id else None
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async def main(model: str, question: str) -> None:
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agent = _build_agent(model)
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client = UnixLocalSandboxClient()
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sandbox = await client.create(manifest=agent.default_manifest)
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try:
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async with sandbox:
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result = Runner.run_streamed(
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agent,
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question,
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run_config=RunConfig(
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sandbox=SandboxRunConfig(session=sandbox),
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tracing_disabled=True,
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workflow_name="Unix-local PTY example",
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),
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)
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saw_text_delta = False
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saw_any_text = False
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tool_names_by_call_id: dict[str, str] = {}
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async for event in result.stream_events():
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if event.type == "raw_response_event" and isinstance(
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event.data, ResponseTextDeltaEvent
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):
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if not saw_text_delta:
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print("assistant> ", end="", flush=True)
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saw_text_delta = True
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print(event.data.delta, end="", flush=True)
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saw_any_text = True
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continue
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if event.type != "run_item_stream_event":
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continue
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raw_item = event.item.raw_item
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banner = _stream_event_banner(event.name, raw_item)
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if banner is None:
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continue
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if saw_text_delta:
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print()
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saw_text_delta = False
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if event.name == "tool_called":
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tool_name = tool_call_name(raw_item)
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call_id = _raw_item_call_id(raw_item)
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if call_id is not None and tool_name:
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tool_names_by_call_id[call_id] = tool_name
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if tool_name:
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banner = f"{banner} {tool_name}"
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elif event.name == "tool_output":
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call_id = _raw_item_call_id(raw_item)
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output_tool_name = tool_names_by_call_id.get(call_id or "")
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if output_tool_name:
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banner = f"{banner} {output_tool_name}"
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print(banner)
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if saw_text_delta:
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print()
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if not saw_any_text:
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print(result.final_output)
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finally:
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await client.delete(sandbox)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description=(
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"Run a Unix-local sandbox agent that demonstrates PTY interaction through the "
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"shell capability."
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)
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)
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parser.add_argument("--model", default=DEFAULT_MODEL, help="Model name to use.")
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parser.add_argument(
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"--question",
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default=DEFAULT_QUESTION,
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help="Prompt to send to the agent.",
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)
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args = parser.parse_args()
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asyncio.run(main(args.model, args.question))
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