Scenario-lab offers a Python-driven approach to managing scenario simulations, focusing on reproducible workflows and CLI-driven interactions. It is designed to support users in running and parsing complex scenario prompts with artifacts generated for later analysis.
A Python CLI framework for scenario simulation
At its core, scenario-lab is a Python project structured around a modular package layout, with a main core package located under packages/core. The use of a dedicated Python 3.12 virtual environment hints at a modern Python codebase, likely relying on language features and dependencies compatible with the latest Python standards.
The project leverages a command-line interface (CLI) to interact with scenario workflows. This interface allows users to run predefined demos, parse scenario prompts, and manage simulation artifacts stored under a .forecast directory. The CLI commands such as scenario-lab demo-run --root .forecast and scenario-lab scenario --root .forecast "<prompt>" suggest a workflow centered on reproducibility and traceability.
The repository structure, implied by the installation path packages/core/.venv, indicates a monorepo or multi-package design, which is common in Python projects aiming for modularity and separation of concerns.
Command-line-driven architecture with reproducible output
What distinguishes scenario-lab is its focus on a CLI-first user experience, combined with a workflow that generates simulation artifacts in a consistent directory structure (.forecast/runs/demo-run). This approach suits use cases where scenarios need to be repeatedly run, stored, and analyzed.
The use of a virtual environment scoped to the core package improves dependency isolation, avoiding conflicts with system Python or other projects. This is especially relevant given the explicit requirement for Python 3.12, which may cause friction in environments that have older Python versions installed.
The commands themselves are intuitive, providing both a demo run and a scenario parsing mode. Notably, the scenario parsing command does not run the full simulation immediately but returns the next workflow turn, which suggests a design optimized for interactive or stepwise scenario exploration.
While the repo enforces modern Python requirements and a disciplined environment setup, it may pose an entry barrier for users not already familiar with Python virtual environments or CLI tooling. Additionally, the dependency on Python 3.12 restricts compatibility with older Python setups, which might be a tradeoff for leveraging newer language features.
Quick start
The repository includes a clear Quickstart section with exact commands to clone, set up a Python 3.12 virtual environment, install development dependencies, and run a demo:
git clone git@github.com:YSLAB-ai/scenario-lab.git
cd scenario-lab
PYTHON=/path/to/python3.12
"$PYTHON" -m venv packages/core/.venv
source packages/core/.venv/bin/activate
pip install -e 'packages/core[dev]'
scenario-lab demo-run --root .forecast
After the demo run completes, you should find artifacts under .forecast/runs/demo-run.
For a more interactive scenario prompt parsing mode, the repo provides:
scenario-lab scenario --root .forecast "/scenario how would a U.S.-Iran conflict at the Strait of Hormuz develop over the next 30 days"
This command parses the scenario prompt and returns the next step in the workflow instead of running the full simulation, which is useful for incremental scenario development or testing.
verdict
scenario-lab is a niche but well-structured Python CLI tool for scenario simulation workflows. It is best suited for practitioners who need reproducible scenario runs and are comfortable working with Python 3.12 and virtual environments.
The modular package design and CLI-driven interface make it a good candidate for integration or extension in forecasting or strategic simulation projects. However, the Python 3.12 requirement and somewhat involved environment setup may limit adoption among less Python-savvy users.
Overall, scenario-lab delivers a focused approach to scenario management with clear workflows and artifact handling, but users should be aware of the setup tradeoffs and the learning curve associated with the environment and CLI tooling.
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→ GitHub Repo: YSLAB-ai/scenario-lab ⭐ 299 · Python