Workflow Automation

6 open source tools compared. Sorted by stars — scroll down for our analysis.

Tool	Stars	Velocity	Language	License	Score
n8n Fair-code workflow automation with native AI capabilities	181.0k	—	TypeScript	—	72
Airflow Platform to author, schedule, and monitor workflows	44.8k	—	Python	Apache License 2.0	79
Prefect Workflow orchestration for resilient data pipelines	22.0k	+73/wk	Python	Apache License 2.0	79
Temporal Durable execution platform for workflow orchestration	19.1k	+166/wk	Go	MIT License	79
Windmill Developer platform to turn scripts into workflows and UIs	16.1k	+68/wk	HTML	—	67
Dagster Orchestration platform for data assets	15.2k	+34/wk	Python	Apache License 2.0	79

Our Analysis

n8n181.0k★

n8n is what Zapier should have been if Zapier trusted its users. A self-hostable workflow automation platform with 180k+ stars that lets you build complex automations visually — and actually own them. If you're an indie hacker duct-taping APIs together, n8n replaces your cron jobs, your webhook handlers, and half your backend. Zapier charges per task and locks you into their cloud. Make (formerly Integromat) is cheaper but still SaaS-dependent. n8n charges per execution, not per step, and you can self-host the community edition for free. The visual editor handles branching, loops, and error handling that would take pages of code. Use this if you're building automations that touch multiple APIs and you want version control over your workflows. Skip it if you need a no-code team to maintain it — n8n rewards technical users. The catch: the "fair-code" license isn't OSI-approved open source. You can self-host, but commercial redistribution has restrictions. And the community edition lacks SSO — that's paywalled.

Airflow44.8k★

Airflow is the battle-tested workflow orchestrator that runs most production data pipelines on the planet. DAGs defined in Python, massive operator library, and Airflow 3.0 (April 2025) added DAG versioning, multi-language support, and event-driven scheduling. If your data team has more than five pipelines, they probably run on Airflow. Dagster treats data assets as first-class citizens — better for ML pipelines and teams that want lineage and observability built in. Prefect runs Python as-is with decorators, no DAG restructuring needed — faster to get started. Both are more modern, but neither has Airflow's ecosystem depth. Use Airflow if you need a proven orchestrator with the broadest community support and your workflows are primarily schedule-based ETL/ELT. The catch: Airflow is heavyweight — the scheduler, webserver, and workers need real infrastructure. Writing DAGs requires restructuring your code into operators and XCom patterns. The learning curve is steep for simple workflows. And Airflow's "everything is a DAG" model is awkward for event-driven or asset-centric workflows where Dagster and Prefect shine.

Prefect22.0k★

Prefect makes Python workflows resilient with two decorators: @flow and @task. No DAG files, no operators, no boilerplate — write normal Python functions and Prefect handles retries, scheduling, logging, and observability. It's what Airflow would be if built today. Dynamic task creation at runtime is the killer feature over Airflow's static DAG parsing. Your ML pipeline can branch based on data, not just config. The hybrid execution model (local code, cloud orchestration) keeps your data on your infrastructure. Compared to Airflow (static DAGs, more ecosystem), Prefect is simpler. Compared to Dagster (asset-centric), Prefect is more flexible. Compared to Temporal (durable execution), Prefect is Python-only but easier. Use this when you need resilient Python workflows without the Airflow learning curve — data pipelines, ML training, ETL. Skip this if you need multi-language support or your workflows are simple enough for cron. The catch: Prefect Cloud is the path of least resistance, but it's a paid service. Self-hosting Prefect Server is possible but less documented. And the v1 to v2 migration broke a lot of workflows — check which version tutorials target. Apache 2.0 license.

Temporal19.1k★

Temporal makes your code crash-proof. Write a workflow function, and Temporal guarantees it completes — even if servers crash, networks fail, or deployments happen mid-execution. The full running state is durable and fault-tolerant by default. Just raised $300M at a $5B valuation because AI agents need exactly this. OpenAI, ADP, and Block run Temporal in production for agent orchestration. The shift from "reliable ETL" to "durable AI agents" is driving 2026 adoption. Compared to Prefect (Python-native, lighter), Temporal is more language-agnostic and handles longer-running workflows. Compared to Windmill (developer-friendly UI), Temporal is more powerful but harder to learn. Use this when you're building workflows that absolutely cannot fail — payment processing, multi-step AI agents, long-running data pipelines. Skip this for simple cron jobs or scripts that can safely retry from scratch. The catch: the learning curve is steep. Temporal's execution model (deterministic replay) requires thinking differently about side effects. Self-hosting is complex — most teams end up on Temporal Cloud, which isn't free.

Windmill16.1k★

Windmill turns scripts into workflows and UIs in minutes. Write a Python function, TypeScript handler, or SQL query — Windmill auto-generates a UI, handles scheduling, webhooks, and error handling. It's the open-source lovechild of Retool (internal tools) and Temporal (workflow orchestration), and it benchmarks 13x faster than Airflow. The breadth of triggers is impressive: schedules, webhooks, HTTP routes, Kafka, WebSockets, email. Supports Python, TypeScript, Go, Bash, SQL, Rust, PowerShell. The web IDE and low-code builders lower the bar for non-engineers. Compared to Retool (commercial, UI-first), Windmill is code-first and free. Compared to Temporal (more powerful workflows), Windmill is more accessible. Compared to n8n (visual automation), Windmill is more developer-oriented. Use this when you need internal tools and workflow automation without building everything from scratch. Skip this if you only need workflow orchestration — Temporal or Prefect are more focused. The catch: the "Other" license needs checking — Windmill uses AGPL-3.0 with an enterprise tier. Being a platform means lock-in if you adopt it deeply. And trying to be both Retool and Temporal means it's not the best at either.

Dagster15.2k★

Dagster treats data as first-class citizens. Instead of defining tasks that run in order (Airflow-style), you define data assets and their dependencies — Dagster figures out what to run and when. This asset-centric model is a genuinely better mental model for data engineering. The asset graph, built-in data lineage, and integrated observability make debugging data pipelines actually manageable. Dagster Cloud offers serverless execution. Compared to Airflow (task-centric, battle-tested, more complex), Dagster is more opinionated and modern. Compared to Prefect (Python-first, event-driven), Dagster's asset model is more structured. Compared to dbt (SQL transforms only), Dagster orchestrates everything. Use this when you're building data pipelines and want clear lineage from source to dashboard. Skip this if you're doing simple ETL that Airflow handles fine — switching has real migration cost. The catch: the asset-centric model has a learning curve if you're coming from task-based orchestrators. And Dagster's opinionated approach means less flexibility — if your workflow doesn't fit the asset model, you'll fight the framework. Apache 2.0 license.