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.

Why this is valuable

This demo shows a retrieval-first agent pattern over a bounded financial corpus where each metric and explanation should stay tied to source files.

Setup

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.

uv run python examples/sandbox/tutorials/data/dataroom/setup.py
uv run python examples/sandbox/tutorials/dataroom_qa/main.py

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.

To run the same manifest in Docker, build the shared tutorial image once and pass --docker:

docker build --tag sandbox-tutorials:latest examples/sandbox/tutorials
uv run python examples/sandbox/tutorials/dataroom_qa/main.py --docker

Expected artifacts

A direct cited answer in the streamed agent response.
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.

Demo shape

Inputs: 5 synthetic filing text docs and 3 simple filing PDFs from examples/sandbox/tutorials/data/dataroom/.
Runtime primitives: sandbox-local bash/file search.

How instructions are loaded

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/....