chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Dataroom Q&A
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## Goal
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Answer grounded financial questions over a synthetic 10-K packet.
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The packet uses synthetic company data, but the documents are shaped like annual report excerpts: MD&A text uses 10-K `Part II, Item 7`, while statement PDFs and footnote text use `Part II, Item 8`.
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## Why this is valuable
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This demo shows a retrieval-first agent pattern over a bounded financial corpus where each metric and explanation should stay tied to source files.
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## Setup
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Run the fixture generator and then the Unix-local example from the repository root. Set `OPENAI_API_KEY` in your shell environment before running the example.
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```bash
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uv run python examples/sandbox/tutorials/data/dataroom/setup.py
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uv run python examples/sandbox/tutorials/dataroom_qa/main.py
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```
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After the initial answer, the demo keeps the sandbox session open for Rich-rendered follow-up prompts. Pass `--no-interactive` for a one-shot run.
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To run the same manifest in Docker, build the shared tutorial image once and pass
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`--docker`:
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```bash
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docker build --tag sandbox-tutorials:latest examples/sandbox/tutorials
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uv run python examples/sandbox/tutorials/dataroom_qa/main.py --docker
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```
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## Expected artifacts
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- A direct cited answer in the streamed agent response.
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- Citations use `[n](data/source-file.txt:line:14)` for text excerpts and `[n](data/source-file.pdf:page:1)` for the one-page synthetic PDFs.
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## Demo shape
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- Inputs: 5 synthetic filing text docs and 3 simple filing PDFs from `examples/sandbox/tutorials/data/dataroom/`.
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- Runtime primitives: sandbox-local bash/file search.
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## How instructions are loaded
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At startup, the wrapper loads this folder's `AGENTS.md` into the agent instructions and builds a hard-coded manifest that maps the shared SEC packet from `examples/sandbox/tutorials/data/dataroom/` into the sandbox as `data/...`.
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"""
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Answer questions over a synthetic dataroom.
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"""
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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 textwrap import dedent
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from agents import Runner, RunResultStreaming, TResponseInputItem
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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, LocalDir
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if __package__ is None or __package__ == "":
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sys.path.insert(0, str(Path(__file__).resolve().parents[4]))
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from examples.sandbox.tutorials.misc import (
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DEFAULT_SANDBOX_IMAGE,
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create_sandbox_client_and_session,
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load_env_defaults,
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print_event,
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run_interactive_loop,
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)
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DEMO_DIR = Path(__file__).resolve().parent
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DATAROOM_DATA_DIR = DEMO_DIR.parent / "data" / "dataroom"
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DEFAULT_QUESTION = (
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"How did revenue, gross margin, operating income, and operating cash flow change in "
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"FY2025 versus FY2024, and which segment contributed the most revenue?"
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)
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AGENTS_MD = dedent(
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"""\
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# AGENTS.md
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Answer the user's financial question using only the synthetic 10-K packet in `data/`.
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## Evidence & citations
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- Cite every material claim with markdown links in these formats (no bare links):
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- `[1](data/source-file.txt:line:14)` for text sources
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- `[2](data/source-file.pdf:page:1)` for PDF sources (each synthetic PDF is one page)
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- Use `rg` and `sed` to find and quote exact evidence; do not use `data/setup.py`.
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Keep the final answer direct and finance-oriented.
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"""
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)
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async def print_streamed_result(result: RunResultStreaming) -> list[TResponseInputItem]:
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async for event in result.stream_events():
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print_event(event)
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print_event(str(result.final_output).strip())
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return result.to_input_list()
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async def main(
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model: str, question: str, use_docker: bool, image: str, no_interactive: bool
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) -> None:
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if not (DATAROOM_DATA_DIR / "10-k-mdna-overview.txt").exists():
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raise SystemExit(
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"Run `uv run python examples/sandbox/tutorials/data/dataroom/setup.py` "
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"before starting this demo."
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)
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manifest = Manifest(
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entries={
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"AGENTS.md": File(content=AGENTS_MD.encode("utf-8")),
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"data": LocalDir(src=DATAROOM_DATA_DIR),
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}
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)
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agent = SandboxAgent(
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name="Dataroom Analyst",
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model=model,
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instructions=AGENTS_MD,
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capabilities=[Shell()],
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)
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client, sandbox = await create_sandbox_client_and_session(
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manifest=manifest,
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use_docker=use_docker,
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image=image,
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)
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try:
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async with sandbox:
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async def run_turn(
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conversation: list[TResponseInputItem],
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) -> list[TResponseInputItem]:
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result = Runner.run_streamed(
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agent,
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conversation,
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max_turns=20,
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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="Dataroom Q&A example",
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),
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)
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return await print_streamed_result(result)
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conversation: list[TResponseInputItem] = [{"role": "user", "content": question}]
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conversation = await run_turn(conversation)
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await run_interactive_loop(
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conversation=conversation,
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no_interactive=no_interactive,
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run_turn=run_turn,
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)
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finally:
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await client.delete(sandbox)
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if __name__ == "__main__":
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load_env_defaults(DEMO_DIR / ".env")
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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-5.4-mini",
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help="Model name to use.",
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)
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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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parser.add_argument(
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"--docker",
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action="store_true",
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help="Run this example in Docker instead of Unix-local.",
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)
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parser.add_argument(
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"--image",
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default=DEFAULT_SANDBOX_IMAGE,
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help="Docker image to use when --docker is set.",
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)
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parser.add_argument(
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"--no-interactive",
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action="store_true",
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help="Run the scripted turn and skip follow-up terminal input.",
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)
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args = parser.parse_args()
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asyncio.run(main(args.model, args.question, args.docker, args.image, args.no_interactive))
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