Workflow Orchestration Platform

An open source platform created by the community to programmatically author, schedule, and monitor workflows as directed acyclic graphs, currently in a major new version with AI agent support and attended asset operations. Developed by Apache Software Foundation, the platform follows four core principles: scalability through a modular architecture using message queues to orchestrate arbitrary numbers of workers, dynamism through Python-defined pipelines that enable programmatic generation, extensibility through custom operators and execution libraries, and elegance through lean explicit pipelines with Jinja templating built into the core. Workflows are defined as pure Python code, eliminating command-line or XML configuration in favor of full programming language capabilities including conditionals, loops, error handling, and dynamic task creation based on runtime data. The rich web interface provides DAG visualization with tree and graph views, task instance details, variable and connection management, Gantt charts for execution timing analysis, and code review for audit trails. Task execution supports retries with exponential backoff, timeout handling, task-level SLA monitoring, and XCom for inter-task data passing. The provider system includes over 900 operators covering integrations with AWS, GCP, Azure, Snowflake, Databricks, Spark, Kubernetes, databases, messaging systems, and SaaS platforms. The scheduler continuously monitors pipelines, manages dependencies, and triggers tasks when upstream conditions are met, with support for catchup backfilling for historical data processing. Airflow 3 introduces a redesigned architecture with separate control and execution planes, dataset-aware scheduling for data-driven triggers, and native support for orchestrating machine learning pipelines and AI agent workflows. The active community hosts an annual Airflow Summit conference and maintains comprehensive documentation.