Custom Workflow Framework

Build proprietary data extraction and automation workflows tailored to your business. Flexible, extensible actor framework with built-in error handling, monitoring, and scaling capabilities.

Generic actors can't do everything. Sometimes you need custom logic specific to your business—unique parsing rules, proprietary data sources, custom validation, or specific output formats that third-party tools don't support. Custom Workflow Framework provides a flexible, production-ready base for building actors that solve your organization's specific data challenges. Whether you're building internal data pipelines, custom API integrations, or specialized business logic, this framework handles the infrastructure so you can focus on the logic that matters.

What Does Custom Workflow Framework Do?

This is an extensible framework for building production-grade custom data extraction and automation workflows. Rather than being locked into predefined functionality that doesn't match your business needs, you have complete control over input processing, data extraction logic, validation rules, and output formatting. The framework handles scaling, error recovery, logging, and monitoring—you focus on writing the logic that matters to your business.

Key Capabilities:

• Custom input processing and validation
• Flexible data extraction logic (HTTP requests, API calls, parsing)
• Custom output formatting and validation
• Error handling and retry logic
• Logging and monitoring hooks
• Authentication and credential management
• Rate limiting and request throttling
• Result caching and deduplication

Key Features (8 Features)

1. Extensible Architecture - Full control over extraction logic and processing rules
2. Custom Validators - Define validation rules specific to your data and business logic
3. Error Recovery - Built-in retry logic, dead-letter handling, and error recovery
4. Performance Optimization - Caching, request batching, and parallel processing
5. Credential Management - Secure handling of API keys and credentials
6. Result Transformation - Custom output formatting and data transformation
7. Monitoring Hooks - Instrumentation points for logging and monitoring
8. Local Testing - Run and test locally before deploying to Apify

How to Use (Step by Step)

Step 1: Define Your Requirements

Specify what your actor needs to do:

• Input data structure and validation rules
• Data sources to connect to
• Processing logic and business rules
• Output format and structure
• Error handling and retry strategies

Step 2: Implement Your Logic

Write custom code for your specific needs:

• Implement input validation
• Define data extraction/processing logic
• Set up error handling
• Configure logging and monitoring
• Write tests for your logic

Step 3: Configure and Deploy

Set up actor configuration:

• Define input schema
• Set resource limits (memory, timeout)
• Configure environment variables
• Set up monitoring and alerts
• Deploy to Apify platform

Step 4: Monitor and Optimize

Track performance and iterate:

• Monitor execution logs
• Track error rates and performance
• Optimize slow operations
• Iterate on logic based on results

Input Parameters (Brief Table)

Parameter	Type	Required	Description
action	string	Yes	Action to perform
input	object	Yes	Action-specific input data
validate	boolean	No	Validate input before processing (default: true)
retryOnError	boolean	No	Retry on transient errors (default: true)
timeout	number	No	Timeout in seconds

Output Data (Brief Table)

Field	Type	Description
success	boolean	Whether operation completed successfully
data	object	Extracted or processed data
error	string	Error message if failed
metadata	object	Operation metadata (duration, retry count, etc.)

Pricing & Performance

Pricing depends entirely on your implementation:

• Resource usage (memory, CPU, network)
• Execution duration
• Data transfer volume
• API quota consumption

Performance depends on your logic implementation and external data sources.

FAQ (3 Questions)

Q: Can I use this as a template for my own actor? A: Yes—fork this repository and customize it for your specific needs. The framework is designed to be extended and includes all necessary scaffolding for production deployment on Apify.

Q: How do I test my implementation locally? A: Use the Apify SDK locally with apify run to test your complete actor, or import and test specific functions directly in Node.js/TypeScript. The framework includes mock utilities for simulating Apify's runtime environment.

Q: What if my actor has dependencies on third-party libraries? A: Add them to package.json and they'll be installed automatically when the actor is built on Apify. The framework supports any npm package. For large dependency trees, consider using yarn for better monorepo support.

Development Best Practices

• Use TypeScript for type safety
• Write tests for your validation and processing logic
• Implement comprehensive error handling
• Log important operations for debugging
• Use environment variables for secrets
• Implement rate limiting to respect API quotas

Integration Capabilities

This framework supports all standard Apify integrations based on your implementation:

• Webhooks: Send results to external systems or trigger downstream workflows
• Email Notifications: Automated reports and alerts based on output data
• Slack Integration: Real-time notifications about actor execution and results
• API Endpoints: Expose your actor's logic via REST API
• Database Integration: Connect to SQL/NoSQL databases for data persistence
• Message Queues: Integrate with RabbitMQ, Kafka, or SQS for event-driven workflows

Works Great With

• Internal Data Pipelines - ETL workflows specific to your business
• Custom API Integrations - Connect to proprietary or niche systems
• Business Logic Automation - Implement complex parsing and validation rules
• Legacy System Integration - Bridge data between old and new systems
• Proprietary Data Sources - Extract from systems without public APIs
• Multi-Step Workflows - Chain custom logic across multiple processing stages

Performance & Scaling

Pricing and performance depend entirely on your implementation, but the framework supports:

• Horizontal Scaling: Built-in support for parallel processing across multiple tasks
• Resource Management: Configurable memory, CPU, and timeout limits
• Data Handling: Efficient streaming for large datasets to minimize memory usage
• Rate Limiting: Built-in utilities for respecting API rate limits and quotas
• Retry Logic: Automatic exponential backoff for transient failures

Best Practices

• Use TypeScript for type safety and better IDE support
• Implement comprehensive error handling and logging
• Write tests for validation and processing logic
• Use environment variables for configuration (never hardcode secrets)
• Implement rate limiting to respect API quotas
• Monitor execution logs and performance metrics
• Document your custom logic for team maintainability

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