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Agentic WorkFlow

Edit Fields

What it does: Transforms and cleans up data by renaming fields, converting data types, and applying validation rules.

Perfect for: Data cleaning • API response processing • Form data preparation • Field standardization

What It Does

Section titled “What It Does”
  • Rename fields - Change field names to match your needs
  • Convert data types - Turn strings into numbers, dates, etc.
  • Clean values - Remove extra spaces, fix formatting
  • Add validation - Ensure data meets your requirements
Edit Fields data transformation illustration

How It Works

Section titled “How It Works”
graph LR
    A[Raw Data] --> B[Edit Fields]
    B --> C[Clean Data]

    style A fill:#e1f5fe
    style B fill:#e8f5e8
    style C fill:#f3e5f5

Simple flow: Raw data → Edit Fields node → Clean, formatted data

Common Operations

Section titled “Common Operations”
  • Rename Fields: Change user_name to username.
  • Convert Types: Turn a text “25” into the number 25.
  • Clean Values: Fix capitalization (e.g., change “ALICE” to “Alice”).
  • Add Fields: Create a new field to stamp the current time ({{now}}).

Real Example

Section titled “Real Example”

Before (Messy Data): You have a user record with inconsistent naming and formatting:

  • user_name: “Alice Smith”
  • age: “28” (stored as text)
  • email: “ALICE@COMPANY.COM” (all caps)

The Fix: You ask the node to do three things:

  1. Rename user_name to username.
  2. Convert age from text to a number.
  3. Clean email by making it lowercase.

After (Clean Data):

  • username: “Alice Smith”
  • age: 28 (now a usable number)
  • email: “alice@company.com”

Common Use Cases

Section titled “Common Use Cases”

Clean web scraped data - Fix messy field names and data types from websites Prepare API data - Format data before sending to external services Process form submissions - Clean and validate user input data Standardize data - Make data consistent across different sources

Quick Troubleshooting

Section titled “Quick Troubleshooting”

Field not found errors: Check that field names match exactly (case-sensitive) Type conversion fails: Make sure the data can actually be converted (e.g., “abc” can’t become a number) Operations not working: Verify your JSON syntax is correct

What’s Next?

Section titled “What’s Next?”

Related nodes: Filter • Merge • HTTP Request

Common workflows: Data Processing Patterns • Web Extraction Workflows • API Integration

Learn more: Data Transformation Guide • Multi-Step Workflows