Kestra
Workflow automationSchedule and run workflows for data, systems and AI from one place.
Kestra gives a team one place to schedule, run and watch automated work, from data pipelines to infrastructure jobs and AI tasks. Each workflow is a short YAML file, so it can be reviewed and versioned like code, and it can also be built in a visual editor. Because flows react to events as well as schedules, work can start as soon as a file lands or a message arrives.
- Builds the same flow in a YAML editor, a No-Code editor or the AI Copilot.
- Starts flows on a schedule or from events such as new files or queue messages.
- Keeps flows in Git, with draft revisions that do not disturb running triggers.
What people use it for
Run a nightly data pipeline
Create a flow in a namespace with tasks that query a database, run a Python script and load the result into cloud storage. Add a Schedule trigger so it runs each night, and set retries and timeouts on tasks that call outside systems. Use backfills to rerun the flow for past dates when data is missing.
Start a process when a file or message arrives
Add a trigger that watches a source such as cloud storage or a message queue like Kafka, Amazon SQS or Google Pub/Sub. Each new file or message starts an execution of the flow. Subflows and conditional branching route the work, and notification plugins can tell Slack, email or PagerDuty when a run fails.
Add AI steps to a business process
Use the AI Agents and RAG task types to call models from OpenAI, Anthropic, Google Gemini, Amazon Bedrock or Ollama inside a flow, with token usage reported per execution. The built-in MCP server exposes your flows as tools that other AI agents can call. In the open-source edition, the AI Copilot that drafts flows for you works with Google Gemini.