EdgeWeave vs Apache Airflow
Both tools describe work as a graph of steps. The difference is which side you author: in Airflow you write Python and the UI shows the graph it produces; in EdgeWeave you build the graph and the code follows.
| Topic | Apache Airflow | EdgeWeave |
|---|---|---|
| How you author | You write DAG files in Python. The web UI displays and monitors the resulting graph. | You edit the graph on a canvas. The .weave file is the source, and you can drop into code when you want to. |
| What a step shows | Task status, logs and run history. | A live preview of the node’s result: tables, charts, maps or a 3D scene. |
| Where it runs | A scheduler, a metadata database and workers that you deploy and operate. | A desktop app that bundles its own Python. A built-in scheduler covers simple recurring runs. |
| Built for | Production batch pipelines run by a team: retries, backfills, SLAs, many integrations. | Interactive exploration and building: data science, simulation and AI work you iterate on. |
| Iteration speed | Edit code, deploy it, trigger a run. | Change a node, press Run, look at the result on the graph. |
| Code | The code is the product. | Export a graph to a standalone Python script when you want it outside the app. |
Using both. Prototype and check a workflow in EdgeWeave, then export it to Python and run that script as a task in your Airflow pipeline.
Based on each project's public documentation, reviewed October 2026. Tools change quickly; if something here is out of date, let us know and we will fix it.