# n8n vs Airflow: a DAG orchestrator, a canvas, and the open-source system for open-ended work

> When the steps cannot be listed in advance, Kortix is the open-source option: an agent plans its own steps on its own machine and lands a change request you review. Airflow orchestrates Python DAGs on a schedule, and n8n draws fixed flows; both are the right shape only when the job repeats.

URL: https://n8n-alternative.com/n8n-vs-airflow

_Compare_

## Kortix vs Airflow at a glance

Every Airflow cell states only what Airflow documents on its own site; every Kortix cell quotes the Kortix docs.

| | Kortix (recommended) | Airflow |
| --- | --- | --- |
| Open source | Yes. Read it, fork it, audit it. | Yes. Its own docs call Airflow an open-source platform. |
| What it is | Open-source AI Operating System; an agent finishes the work. | A platform for orchestrating batch workflows. |
| Unit of work | A session: an agent plans and runs the tools. | A DAG: tasks and the dependencies between them. |
| How you build it | Markdown agents and `kortix.yaml`, all in git. | Workflows as Python code, with Jinja templates. |
| Who it is for | Teams whose work changes each time it runs. | Developers who would rather code than click. |
| Where it runs | Kortix Cloud, your VPC, or on-prem. Self-host is free. | From a single process to a distributed system. |
| Best-fit job | Open-ended work with steps nobody can pre-draw. | Batch jobs with a clear start and end, on a schedule. |
| How work lands | A change request a person reviews and merges. | DAG runs and task status in a web UI. |

Airflow facts read from Airflow's own documentation, checked October 2026. Kortix facts from the Kortix docs.

_The unit of work_

## A session that plans its own steps

The change request is where open-ended work lands.

`terminal`

```sh
$ kortix sessions new --prompt "Reproduce issue #142, fix it, and open a change request"
$ kortix cr ls        # review what an agent proposes
$ kortix chat         # talk to a session's agent from your terminal
```

Source: README.md in https://github.com/kortix-ai/suna

From the Kortix repository: start a session on a job, then review the change request it opens.

## What Airflow is built to run

Airflow's own documentation calls it a platform for orchestrating batch workflows, and describes it as an open-source platform for developing, scheduling and monitoring workflows. You define the work in Python. A DAG holds tasks, the dependencies between them and a schedule; the framework parses the script, schedules the tasks, and runs them in the order you set. Operators connect a run to Google Cloud, AWS, Azure and other services.

The same docs are direct about who it fits. Workflows with a clear start and end that run on a schedule are a good fit for Airflow DAGs, and the framework is built for people who would rather code than click. People who would rather click than code will find it a poor match. It runs anywhere from a single process on a laptop to a distributed system, and a web UI shows DAG status, task logs and run history, so you can backfill old runs or rerun only the tasks that failed. Airflow's own site announces stateful tasks and multi-language support for its current release (checked October 2026).

## When a DAG orchestrator is the right tool

A DAG is the right shape when the steps are known before the run and the same path should repeat:

- A nightly load that lands warehouse tables in order.
- A chain of dbt or Spark jobs with dependencies between them.
- An ETL job that moves records between two systems on a schedule.
- A model-training pipeline that reruns when its inputs change.
- A scheduled report that pulls from several sources and writes one file.

These jobs need ordering, retries, backfills and a record of what ran, which is what Airflow's scheduling and execution semantics exist for. Expressing the pipeline in Python keeps it in version control, testable, and reviewable as code.

## When the job is open-ended

The mismatch appears when you cannot write the task list before the run. Research a market and write a one-page brief; read a bug report, reproduce the bug and open a fix; reconcile a month of records where each row needs a judgment. A DAG is a graph the parser builds before the run executes, so the route is fixed before any data is seen. Work like this needs a machine to run tools on, and a plan the agent writes as it goes. That is a different layer from scheduling.

## What Kortix runs instead

Kortix is the open-source AI Operating System: your agents, their skills, your company memory and every connector live in one git repo you own ([Read the docs](https://kortix.com/docs)). Each session runs one agent on its own cloud computer, one isolated sandbox per session, on its own branch. The agent plans, calls connectors as tools, and when the work is done opens a change request; session work reaches `main` through a change request a person reviews as a diff. Any model provider with your own keys. Connectors reach 3,000+ apps in a click, plus MCP, OpenAPI, Postman, GraphQL and raw HTTP, and connector credentials are brokered server-side and never enter the machine. When a job should run on a schedule, a cron trigger or a signed webhook starts a session with nobody present.

## The two run side by side

A team rarely swaps one for the other. Keep Airflow on the pipelines whose dependencies and schedule are fixed, and give Kortix the work whose route is decided while it runs. The same split applies to the canvas tools: the [n8n vs Kortix comparison](/n8n-vs-kortix) sets a visual automation tool against Kortix, [is n8n obsolete](/is-n8n-obsolete) covers when a fixed flow still fits, and [the n8n alternatives guide](/best-n8n-alternatives) sorts the wider field by the work each tool assumes.

## Where to start

Take the one job your current setup handles worst, the one a person still does by hand because the steps change each time, and run it in Kortix. `kortix sessions new` starts the first session, and the agent opens a change request when the work is done.

_FAQ_

## n8n vs Airflow questions

The short answers a team asks before it commits.

### Is Kortix an alternative to Airflow?

Only for the work Airflow is not built for. Airflow orchestrates batch workflows whose steps and schedule are fixed. Kortix runs open-ended work an agent plans as it goes and lands as a change request.

### Can Kortix run scheduled jobs the way Airflow does?

Yes. A cron trigger or a signed webhook starts a session with nobody present. The unit differs: Airflow runs a fixed DAG, while Kortix runs an agent that plans its own steps.

### Do Airflow and Kortix overlap?

At the edges. Airflow keeps the pipelines whose dependencies are known in advance, and Kortix takes the research, reports and fixes whose route is not. Many teams run both.

### Is Kortix open source?

Yes. Read it, fork it, audit it. Self-host is free.

### How does Kortix reach the tools a pipeline uses?

Connectors reach 3,000+ apps in a click, plus MCP, OpenAPI, Postman, GraphQL and raw HTTP. Connector credentials are brokered server-side and never enter the machine.

## Hand Kortix the work a DAG cannot draw

One open-ended job, one session, and a change request you review.

[Try Kortix](https://kortix.com) · [See the n8n alternatives](https://n8n-alternative.com/best-n8n-alternatives)

Open source · Any model, your keys · Self-host, VPC, or on-prem