  Autonomous agents that address day-to-day digital tasks (e.g., ordering
groceries for a household), must not only operate multiple apps (e.g., notes,
messaging, shopping app) via APIs, but also generate rich code with complex
control flow in an iterative manner based on their interaction with the
environment. However, existing benchmarks for tool use are inadequate, as they
only cover tasks that require a simple sequence of API calls.
  To remedy this gap, we built $\textbf{AppWorld Engine}$, a high-quality
execution environment (60K lines of code) of 9 day-to-day apps operable via 457
APIs and populated with realistic digital activities simulating the lives of
~100 fictitious users. We then created $\textbf{AppWorld Benchmark}$ (40K lines
of code), a suite of 750 natural, diverse, and challenging autonomous agent
tasks requiring rich and interactive code generation. It supports robust
programmatic evaluation with state-based unit tests, allowing for different
ways of completing a task while also checking for unexpected changes, i.e.,
collateral damage. The state-of-the-art LLM, GPT-4o, solves only ~49% of our
'normal' tasks and ~30% of 'challenge' tasks, while other models solve at least
16% fewer. This highlights the benchmark's difficulty and AppWorld's potential
to push the frontiers of interactive coding agents. The project website is
available at https://appworld.dev/.
