---
id: fireworks
version: "2.1.4"
license: modified BSD
license_treatment: permissive
maintenance: active
---

# FireWorks — FireWorks workflow software

License: permissive · Maintenance: active · Downloads: 86.4K/mo

## 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 above — verify before relying.

FireWorks stores, executes, and manages calculation workflows, providing a system to define, track, and run computational tasks at scale.

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

pip install fireworks
uv add fireworks
poetry add fireworks

## Installing FireWorks

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.

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 tools

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.

Verify 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_current
- Install friction: low
- Maintenance: active
- Downloads: 86.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

workflow management system, task execution framework, distributed computation orchestration, calculation job scheduler, high-throughput workflow engine, computational task runner, workflow state tracking, workflow-orchestration, scientific-computing, task-scheduling

[View on SkillFed](https://skillfed.io/packages/fireworks) · [View on PyPI](https://pypi.org/project/fireworks/)