Execution

Visual Workflow AI's execution engine brings your workflows to life by processing blocks in the correct order, managing data flow, and handling errors gracefully.

Documentation Overview

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

Execution Triggers

Workflows can be executed through multiple channels:

• Manual: Test and debug directly in the editor
• Deploy as API: Create an HTTP endpoint secured with API keys
• Deploy as Chat: Create a conversational interface on a custom subdomain
• Webhooks: Respond to external events from third-party services
• Scheduled: Run on a recurring schedule using cron expressions

Deploy as API

When you deploy a workflow as an API, Visual Workflow AI:

• Creates a unique HTTP endpoint: https://visualworkflow.app/api/workflows/{workflowId}/execute
• Generates an API key for authentication
• Accepts POST requests with JSON payloads
• Returns workflow execution results as JSON

Example API call:

curl -X POST https://visualworkflow.app/api/workflows/your-workflow-id/execute \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"input": "your data here"}'

Deploy as Chat

Chat deployment creates a conversational interface for your workflow:

• Hosted on a custom subdomain: https://your-name.visualworkflow.app
• Optional authentication (public, password, or email-based)
• Customizable UI with your branding
• Streaming responses for real-time interaction
• Perfect for AI assistants, support bots, or interactive tools

Each deployment method passes data to your workflow's starter block, beginning the execution flow.

Best 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 and Cost Calculation to monitor and optimize your executions.