Pailot's execution engine brings your workflows to life by processing blocks in the correct order, managing data flow, and handling errors gracefully, so you can understand exactly how workflows are executed in Pailot.
Every workflow execution follows a deterministic path based on your block connections and logic, ensuring predictable and reliable results.
Documentation Overview
Execution Basics
Learn about the fundamental execution flow, block types, and how data flows through your workflow
Logging
Monitor workflow executions with comprehensive logging and real-time visibility
Cost Calculation
Understand how workflow execution costs are calculated and optimized
External API
Access execution logs and set up webhooks programmatically via REST API
Key Concepts
Topological Execution
Blocks execute in dependency order, similar to how a spreadsheet recalculates cells. The execution engine automatically determines which blocks can run based on completed dependencies.
Path Tracking
The engine actively tracks execution paths through your workflow. Router and Condition blocks dynamically update these paths, ensuring only relevant blocks execute.
Layer-Based Processing
Instead of executing blocks one-by-one, the engine identifies layers of blocks that can run in parallel, optimizing performance for complex workflows.
Execution Context
Each workflow maintains a rich context during execution containing:
- Block outputs and states
- Active execution paths
- Loop and parallel iteration tracking
- Environment variables
- Routing decisions
Deployment Snapshots
All public entry points—API, Chat, Schedule, Webhook, and Manual runs—execute the workflow’s active deployment snapshot. Publish a new deployment whenever you change the canvas so every trigger uses the updated version.
The Deploy dialog keeps a version history—inspect a snapshot, compare it with the draft, and promote or roll back when you need a prior release.
Programmatic Execution
Pailot does not publish a public npm or PyPI client. Call the External API from your application, or ask the vendor for a supported integration.
curl -H "x-api-key: YOUR_API_KEY" \
https://pailot.yourdomain.com/api/v1/logs?workspaceId=YOUR_WORKSPACE_IDBest Practices
Design for Reliability
- Handle errors gracefully with appropriate fallback paths
- Use environment variables for sensitive data
- Add logging to Function blocks for debugging
Optimize Performance
- Minimize external API calls where possible
- Use parallel execution for independent operations
- Cache results with Memory blocks when appropriate
Monitor Executions
- Review logs regularly to understand performance patterns
- Track costs for AI model usage
- Use workflow snapshots to debug issues
What's Next?
Start with Execution Basics to understand how workflows run, then explore Logging to monitor your executions and Cost Calculation to optimize your spending.