Event-driven orchestration platform with YAML-declarative workflows, 600+ plugins, and multi-language support. 26.5K stars, 433 contributors. Structural tailwind from Airflow 2 EOL in 2026 — primary open-source migration target for data engineering teams.
Where it wins
26,569 GitHub stars, 433 contributors, 262 commits/30d — very active
Airflow 2 EOL in 2026 creates structural migration demand
YAML-declarative workflows with Git version control
600+ plugins, multi-language support (Python, Java, Node, Shell, Go)
v1.3.3 released 2026-03-17 — enterprise-grade stability
Where to be skeptical
Java-based — heavier than TypeScript alternatives (n8n, Windmill)
No MCP bridge found — limits Claude Code integration
Less AI-native than n8n (no dedicated AI nodes or LangChain integration)
HN traction is moderate (142pt peak, 3 posts above threshold)
Editorial verdict
Best pick for data engineering and DevOps teams migrating from Airflow, or anyone who wants declarative (YAML) workflow definitions with Git version control. If your automation is data pipelines or scheduled ETL, Kestra beats n8n. No MCP bridge limits relevance for Claude Code users.
Source
Videos
Reviews, tutorials, and comparisons from the community.
What is Workflow Orchestration?
What is Kestra?
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Public evidence
Airflow 2 reaches end-of-life in 2026; Airflow 3.0 has breaking changes. Kestra is well-positioned as the primary open-source migration target for data engineering teams.
Solid initial HN reception with sustained moderate interest (3 posts above threshold). Developer community recognizes the YAML-first approach as differentiated.
YAML-based approach differentiated from n8n's visual canvas and Windmill's code-first model. 600+ plugins cover most data engineering needs.
Raw GitHub source
Constrained peek so you can sanity-check the source material without leaving the site.
Kestra is an open-source, event-driven orchestration platform for data, AI, and infrastructure workflows. It unifies scheduled and event-driven automation behind a declarative, language-agnostic interface. By bringing Infrastructure as Code best practices to your data, process, and microservice pipelines, you can build reliable workflows directly from the UI in just a few lines of YAML, let the built-in AI Copilot write them for you, or generate them straight from your coding agent (Claude Code, Cursor, Windsurf, and others) with Agent Skills.
Key Features:
trigger definition.disabled to true.🧑💻 The YAML definition gets automatically adjusted any time you make changes to a workflow from the UI or via an API call. Therefore, the orchestration logic is always managed declaratively in code, even if you modify your workflows in other ways (UI, No-Code editor, AI Copilot, CI/CD, Terraform, API calls).
Kestra 2.0 is a major release. The highlights below are the short version. See What's New in Kestra 2.0 for the full list, and the 2.0 migration guide for breaking changes and upgrade steps.
AI
Workflow components
ForEach and ForEachItem are replaced by a single Loop task.conditions are now written as when, alongside new date helpers such as isWeekend(), isPublicHoliday(), isDayWeekInMonth(), and isLastWorkingDay(). See date functions.subflow() function returns a subflow's outputs inline in an expression, with no dedicated task.{label, value} options for SELECT/MULTISELECT, JSON Schema validation, and a FORM type that groups inputs into a multi-step wizard.Developer experience
kestractl covers flows, plugins, and IAM; the VS Code extension can mount a namespace as a live folder.Infrastructure
-no-plugins image suffix is now -slim, and KESTRA_PLUGINS_AUTO_INSTALL_ENABLED=true fetches plugins from Maven Central on demand. The Quick Start command below uses both.PurgeStorage cleans internal storage by last-modified date, including files whose execution records are already gone.Enterprise Edition also gains an action-based RBAC model, Policies, Cases for incident management, Promote for UI-driven cross-instance deployment, Reusable Inputs, Worker Groups 2.0, and an external log data store.
Deploy Kestra on AWS using our CloudFormation template:
Deploy Kestra on Google Cloud Infrastructure Manager using our Terraform module.
Make sure that Docker is running. Then, start Kestra in a single command:
docker run --pull=always --rm -it -p 8080:8080 --user=root \
--name kestra \
-v kestra_data:/app/storage \
-v kestra_db:/app/data \
-v /var/run/docker.sock:/var/run/docker.sock \
-v /tmp:/tmp \
-e KESTRA_PLUGINS_AUTO_INSTALL_ENABLED=true \
kestra/kestra:latest-slim server local
If you're on Windows and use PowerShell:
docker run --pull=always --rm -it -p 8080:8080 --user=root `