{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"},{"label":"Other/Nonlisted Topic","url":"https://skillfed.io/packages/category/other-nonlisted-topic"}],"enrichment":{"capability":"FireWorks stores, executes, and manages calculation workflows, providing a system to define, track, and run computational tasks at scale.","skillfed_tags":["workflow-orchestration","scientific-computing","task-scheduling"],"use_cases":["Define and execute multi-step materials science simulations where each step depends on previous results.","Manage large batches of independent computational tasks and monitor their completion status.","Build a web dashboard to visualize and manage ongoing workflows across a research team.","Schedule and retry failed calculations automatically within a defined workflow.","Store and retrieve workflow definitions and execution history for reproducibility and auditing."],"what_it_does":"FireWorks is a workflow management system designed for high-throughput computational applications, particularly in materials science and scientific research. It provides a framework to define, store, execute, and monitor calculation workflows, allowing users to chain together computational tasks and manage their execution across distributed resources. The package includes a web interface (built on Flask and gunicorn), a command-line interface, and a Python API for programmatic workflow control.\n\nThe system relies on MongoDB for persistent storage of workflow state and task metadata, and uses standard Python tooling (Jinja2 for templating, ruamel.yaml for configuration, tqdm for progress tracking) to handle workflow definition and execution. It is intended for users who need to orchestrate complex, multi-step computational pipelines and track their progress over time.","worth_installing":"Yes, if you need to orchestrate and track multi-step computational workflows at scale. FireWorks is production-stable, actively maintained, has no known vulnerabilities, and installs cleanly. It is well-suited for scientific and high-throughput computing environments. Install with caution if you lack MongoDB infrastructure or are unfamiliar with workflow orchestration concepts."},"id":"fireworks","links":{"html":"https://skillfed.io/packages/fireworks","md":"https://skillfed.io/packages/fireworks.md","pypi":"https://pypi.org/project/fireworks/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":null,"license_treatment":"permissive","name":"FireWorks","python_support":"supports_current","summary":"FireWorks workflow software"},"popularity":{"monthly_downloads":86375,"position":13866,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1.4"}