Process Each Item

purpose to process or transform each element in a list, array, or dataset individually this node enables bulk operations like cleaning, combining, calculating, or filtering fields for every item in a collection problem statement when workflows receive large datasets (e g , api responses, form submissions), it’s often necessary to apply similar transformations or calculations to each record doing this manually or in separate steps increases complexity and time the process each item node automates this by iterating through all items and applying defined operations in a loop requirements must accept an array or list as input should allow selection of the source variable containing the array must support defining field level transformations (combine, clean, calculate, etc ) should allow previewing and testing results before deployment must output a new variable containing all processed items use cases cleaning and formatting incoming api data combining multiple fields (e g , first name + last name) applying calculations on numeric fields (e g , price × quantity) filtering or excluding unwanted fields adding conditional logic to modify items dynamically generating indexed or counted records outcome creates a structured, processed dataset by iterating through each item, performing defined transformations, and outputting a consistent list or array ready for the next node in your workflow how to use 1\ drag & drop add the process each item node from the node panel into your workflow canvas 2\ configure data source select or enter the variable that contains your array data 3\ add sample json (optional) to detect and reference fields easily, you can provide a sample json structure this allows you to preview and select available fields visually for transformations 4\ define operations set up transformations to apply to each item available operations combine fields → join multiple fields (e g , firstname + lastname) calculate → perform arithmetic (e g , price × quantity) clean data → format text (lowercase, trim, etc ) add condition → apply logic (e g , if age > 18) remove fields → exclude unnecessary fields count & index → add row numbers or counters 5\ preview results check how your operations will transform the data before finalizing output variable name assign a name (e g , processeditems) quick presets processeditems, results, transformeddata, outputdata