head to head · open source
airflow vs docetl
airflow has 47,350 GitHub stars, 18,299 forks, 1,831 open issues and last shipped yesterday. docetl has 4,189 stars, 460 forks, 45 open issues and last shipped 4 days ago. airflow leads on adoption by 1,030% (47,350 vs 4,189 stars). airflow is written in Python under Apache-2.0; docetl is written in Python under MIT. airflow has attracted 39% as many forks as stars, docetl 11%. airflow was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 3 topic tags (data-pipelines, elt, etl), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| airflow | docetl | |
|---|---|---|
| GitHub stars | ★ 47K | ★ 4.2K |
| License | Apache-2.0 | MIT |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-10-30 |
| Forks | ⑂ 18K | ⑂ 460 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick airflow if
- You weight community size — 47K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick docetl if
- You want the docetl feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and docetl fits your workflow better
About airflow
Apache Airflow Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on Dags a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when…
read the full airflow overview →
About docetl
DocETL: Declarative & Agentic Map Reduce What is DocETL · Install · Python API · YAML · DocWrangler UI · Docs What is DocETL DocETL helps you process large collections of data (structured and unstructured) with LLMs. You write each operation in natural language, e.g., "pull out every complaint in this ticket," and DocETL provides the operators you need (map, reduce, filter, and more) and orchestrates them, parallelizing work across your data, optimizes your pipeline automatically, swapping models, rewriting prompts, decomposing operations, and replacing subtasks with code wherever possible, to raise accuracy and…
read the full docetl overview →
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Frequently asked questions
Is airflow or docetl more popular?
airflow has 47,350 GitHub stars and docetl has 4,189. airflow has the larger community by that measure.
Are airflow and docetl free?
Both are open source. airflow is licensed under Apache-2.0 and docetl under MIT. Neither carries a licence fee.
What is the difference between airflow and docetl?
airflow is written in Python and docetl in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, airflow or docetl?
Choose airflow if you want the larger community (47,350 stars) or its Apache-2.0 licence terms. Choose docetl if its feature set, stack or MIT licence fits better. Both are self-hostable.