FireWorks
FireWorks workflow software
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- MongoDB is typically needed as the backend data store for production use, though the package itself does not enforce this at install time.
- Installation is straightforward with low friction—a pure Python wheel and 12 runtime dependencies that are all standard, well-maintained packages.
License · maintenance · safety
modified BSD (permissive) — FireWorks uses a modified BSD license, which is permissive and allows commercial use, modification, and distribution with minimal restrictions—suitable for most use cases.
last release 2026-08-11 (3 days) · last repo commit 2026-08-11 · 424 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 86,375 downloads/mo, #13,866 on PyPI
Alternatives
Verify before relying
pip install fireworks
from fireworks import Firework, Workflow
from fireworks.core.fworker import FWorker
# Define a simple workflow
fw = Firework()
wf = Workflow([fw])
# Launch and manage via FireWorks API or command-line tools- Whether MongoDB setup is required for basic usage or only for production deployments.
- Specific performance characteristics or scalability limits for typical workflow sizes.
- Whether the Flask/gunicorn stack is used for all deployments or optional for certain workflows.
What it is and 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
fireworks on PyPI
Before you install
Installation is straightforward with low friction—a pure Python wheel and 12 runtime dependencies that are all standard, well-maintained packages. The project is actively maintained with a recent release and ongoing commits.
Requires Python 3.10 or later. MongoDB is typically needed as the backend data store for production use, though the package itself does not enforce this at install time.
License in practice
FireWorks uses a modified BSD license, which is permissive and allows commercial use, modification, and distribution with minimal restrictions—suitable for most use cases.
Quickstart
pip install fireworks
from fireworks import Firework, Workflow
from fireworks.core.fworker import FWorker
# Define a simple workflow
fw = Firework()
wf = Workflow([fw])
# Launch and manage via FireWorks API or command-line toolsVerify before relying
- Whether MongoDB setup is required for basic usage or only for production deployments.
- Specific performance characteristics or scalability limits for typical workflow sizes.
- Whether the Flask/gunicorn stack is used for all deployments or optional for certain workflows.
Package facts
| License | modified BSD permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 12 packagesruamel.yamlpymongoJinja2montypython-dateutiltabulateflaskflask-paginategunicorntqdmimportlib-metadatatyping-extensions |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 86,375 / month, #13,866 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsOperating System :: OS IndependentProgramming Language :: PythonTopic :: Other/Nonlisted TopicTopic :: Scientific/Engineering |
Evidence: fireworks-2.1.4-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “workflow management system”
- FireWorksFireWorks stores, executes, and manages calculation workflows,…
- snakemakeSnakemake is a workflow management system that lets you define…
- weaselWeasel is a workflow orchestration system for managing end-to-end…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also jobflow · langchain-fireworks · fireworks-ai · durabletask.azuremanaged · prefect-docker · prefect-redis · durabletask · atomate2 · custodian · celery-redbeat