Run steps in containers: Each step runs in its own Docker container with specific tools and dependencies.Connect step outputs: Pass data between steps to build complex automation sequences.Handle failures gracefully: Built-in retry logic and error handling keep workflows robust.Execute in parallel: Independent steps run simultaneously for faster completion.Integrate AI agents: Include intelligent decision-making within deterministic workflows.
Reliable: Same inputs produce same outputs every time - no surprises in production.Isolated: Each step runs in its own container, preventing conflicts and dependency issues.Language-agnostic: Use Python, Node.js, Go, or any language/tool you need.AI-enhanced: Include intelligent agents that can make decisions and use tools.Scalable: Steps run in parallel when possible, and the system scales automatically.
Use any Docker image (Python, Node.js, custom tools)
No shared state between steps
Clean environment for each operation
Automatic resource management
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# Different containers for different tasksstep.analyze(image="python:3.11", script="analyze.py")step.build(image="node:18", command="npm run build") step.deploy(image="kubectl:latest", command="kubectl apply -f app.yaml")
# AI agent analyzes logs and decides next actionanalysis = step.inline_agent( message="Analyze these error logs and suggest fixes", tools=[log_parser_tool, fix_generator_tool])# Use AI decision in next stepif analysis.severity == "high": step.emergency_response()else: step.create_ticket()
Define workflow: Write steps and their dependenciesTest locally: Run workflows in development environmentDeploy to production: Execute on your infrastructure with runnersMonitor execution: Track progress and debug issuesNext: Try building your first workflow with our examples or quick start guide.