Code - VectorShift

The Code node lets you run custom Python or JavaScript code directly inside your pipeline. Define your own inputs and outputs, write transformation logic, call libraries, and return structured results — all without leaving VectorShift. Use it to apply custom business logic to financial data, transform API responses into the exact format you need, or give an agent the ability to compute values on the fly during a conversation.

Core Functionality

Executes custom Python or JavaScript code within a pipeline
Supports user-defined inputs and outputs with configurable names and types
Includes a built-in code editor with boilerplate templates for each language
Available input/output types include Text, File, Image, and more
Works in both Workflows as a canvas node and Agents as the Code tool

Tool Inputs

Input Name — A user-defined input parameter. You can add multiple inputs by clicking + Add. Each input has a configurable name (e.g., input_1) and a type selected from a dropdown (e.g., Text, File, Image).
Code * — The Python or JavaScript code to execute. Required. The code editor is pre-populated with boilerplate that shows how to access inputs and set the output variable.
Language — Toggle between Python and JavaScript. The code editor template updates to match the selected language.

* Required field

Tool Outputs

Output Name — A user-defined output parameter. You can add multiple outputs by clicking + Add. Each output has a configurable name (e.g., output_1) and a type selected from a dropdown (e.g., Text, File, Image). The code must assign a value to each defined output variable.

Agents
Workflows

Overview

In agents, the Code tool lets the agent execute custom Python or JavaScript code during a conversation. You define the inputs, outputs, and code logic — the agent then invokes this tool when appropriate, passing in values from conversation context. Use it when you need the agent to perform calculations, data transformations, or any custom logic that goes beyond what built-in tools offer.

Use Cases

Calculate compound interest or amortization schedules from user-provided loan parameters
Parse and transform raw JSON API responses into human-readable financial summaries
Run custom validation logic on portfolio allocation percentages before proceeding
Convert between currencies or units using custom formulas with live rates
Generate formatted reports by processing structured data from upstream tools
Compute weighted averages or custom scoring across multiple data points

How It Works

Step 1: Add the Code Tool

From the agent editor, click + Add Tool. Select the Logic category and click + Add next to Code.

Agent tool panel showing the Logic category with Code Interpreter tool highlighted

Step 2: Open the Tool Configuration

Click Edit on the Code tool in the tool list to open its configuration panel.

Code Interpreter tool configuration panel showing Tool Description, Code, Input Files, and Knowledge Base fields

Step 3: Select the Language

Choose between Python and JavaScript using the language toggle at the top of the configuration panel. The code editor template updates automatically.

Step 4: Define Inputs

Add input parameters that your code will receive. For each input:

Set the Input Name (e.g., input_1, principal, rate).
Select the Input Type from the dropdown (e.g., Text).
Click + Add Input to add additional inputs.

Each input field can be filled automatically by the agent based on conversation context, or locked to a fixed value. Click the toggle icon next to a field to switch between these modes.

Step 5: Write Your Code

Write your logic in the code editor. The default template shows how to access inputs as variables and how to assign values to the output variable:Python:

# Access your inputs as variables (e.g., input_1)
# Assign your result to the output variable
output = input_1

JavaScript:

// Access your inputs as variables (e.g., input_1)
// Assign your result to the output variable
output = input_1;

Step 6: Define Outputs

Add output parameters that your code will return. For each output:

Set the Output Name (e.g., output_1, result).
Select the Output Type from the dropdown (e.g., Text).
Click + Add Output to add additional outputs.

Your code must assign a value to each output variable you define.

Step 7: Write a Tool Description

The Tool Description tells the agent when and how to use this tool. Customize it with specifics — for example: “Use this tool to calculate compound interest. The user will provide the principal, rate, and number of periods.”

Step 8: Set Auto Run Behavior

Click the Auto Run dropdown to control execution:

Auto Run — The agent runs the code automatically without asking for confirmation.
Require User Approval — The agent pauses and asks the user before running the code.
Let Agent Decide — The agent uses its judgment to decide whether to confirm first.

Code Interpreter tool requiring user approval, showing code, Knowledge Base, and Input Files fields in the approval dialog

Step 9: Test the Tool

In the agent chat panel, ask the agent to perform a calculation or transformation. The agent invokes the Code tool and returns the result. A Code Interpreter panel appears in the sidebar showing the executed code and its output.

Settings

Setting	Type	Default	Description
`Language`	Toggle	Python	Python or JavaScript.
`Input Name`	Text	input_1	Name of each input parameter. Add more with + Add Input.
`Input Type`	Dropdown	Text	Type of each input (Text, File, Image, etc.).
`Code`	Code editor	Boilerplate template	The code to execute. Required.
`Output Name`	Text	output_1	Name of each output parameter. Add more with + Add Output.
`Output Type`	Dropdown	Text	Type of each output (Text, File, Image, etc.).

Best Practices

Name inputs descriptively. Use meaningful names like principal, rate, periods instead of input_1, input_2 — this helps the agent understand what values to pass.
Write a detailed tool description. Explain what the code does, what inputs it expects, and what it returns. This guides the agent to invoke it correctly.
Require user approval for irreversible operations. If the code modifies data or calls external services, set Auto Run to Require User Approval so users can review before execution.
Keep code focused. Write code that does one thing well. For complex multi-step logic, use multiple Code tools chained together.
Handle edge cases in the code. Add input validation and error handling within your code to avoid silent failures.

Snowflake Analytics Agent

Queries and interprets Snowflake data warehouse data to deliver actionable business insights.

Earnings Call Insight and Sentiment Analyzer

Analyzes earnings call transcripts for sentiment, key themes, and forward-looking signals.

FX Arbitrage Research Agent

Identifies and analyzes foreign exchange arbitrage opportunities across markets and instruments.

Capital Account Statements Agent

Processes and validates capital account statements for accuracy and investor reporting.

Common Issues

For help with common configuration issues, see the Common Issues page.

Overview

In workflows, the Code Execution node lets you write and run custom Python or JavaScript code on the canvas. Define typed inputs connected from upstream nodes, write your transformation logic, and output structured results to downstream nodes. Use it for any custom processing step — data transformations, calculations, format conversions, or business logic that isn’t covered by built-in nodes.

Use Cases

Transform raw API response JSON into a structured format for downstream processing
Calculate financial metrics (IRR, NPV, CAGR) from extracted spreadsheet values
Apply custom data validation rules before writing results to a database or file
Parse and reformat dates, currencies, or account numbers across different standards
Merge and deduplicate data from multiple upstream sources using custom logic

How It Works

Step 1: Add the Code Execution Node

In the workflow canvas, click the Logic tab in the node palette and click Code. Drag it onto the canvas.

Workflow node palette showing the Logic tab with Code option

Step 2: Select the Language

Use the language toggle at the top of the node to switch between Python and JavaScript. The code editor template updates to match.

Step 3: Define Inputs

Each input has a name and a type:

Name — Type a name in the text field (default: input_1). This becomes the variable name in your code.
Type — Select from the dropdown: Text, File, Image, and more.
Click + Add to add more inputs. Click the red − button to remove an input.

Connect each input’s left-side handle to the output of an upstream node.

Step 4: Write Your Code

The code editor is pre-populated with boilerplate showing how to access inputs and set the output:

# Access your inputs as variables (e.g., input_1)
# Use the 'output' variable to set the result
output = input_1

Replace the boilerplate with your custom logic. Your code can access all defined inputs as variables by name.

Step 5: Define Outputs

Each output has a name and a type:

Name — Type a name in the text field (default: output_1). Your code must assign a value to this variable.
Type — Select from the dropdown to match the data your code returns.
Click + Add to add more outputs. Click the red − button to remove one.

Connect each output’s right-side handle to downstream nodes.

Code node connected to input and output nodes in a workflow

Step 6: Test the Workflow

Click Run to execute the workflow. The Code Execution node runs your code with the upstream input values and passes the output to downstream nodes.

Settings

Setting	Type	Default	Description
`Language`	Toggle	Python	Python or JavaScript.
`Input Name`	Text	input_1	Name of each input parameter. Add more with + Add.
`Input Type`	Dropdown	Text	Type of each input (Text, File, Image, etc.).
`Code`	Code editor	Boilerplate template	The Python or JavaScript code to execute. Required.
`Output Name`	Text	output_1	Name of each output parameter. Add more with + Add.
`Output Type`	Dropdown	Text	Type of each output (Text, File, Image, etc.).

Best Practices

Match input/output types to connected nodes. Ensure the types you select for inputs and outputs match what upstream nodes produce and downstream nodes expect. A type mismatch causes runtime errors.
Use descriptive variable names. Rename input_1 to something meaningful like raw_json or account_balance. This makes the code more readable and the node connections easier to understand on the canvas.
Test with small inputs first. Before connecting a large data pipeline, test your code with a simple hardcoded input to verify the logic works correctly.
Return the correct output variable. Your code must assign a value to the exact variable name defined in the output field. If your output is named result, your code must set result = ....
Keep external dependencies minimal. Stick to standard library functions when possible. If your code requires external packages, verify they are available in the VectorShift execution environment.

Snowflake Analytics Agent

Queries and interprets Snowflake data warehouse data to deliver actionable business insights.

Earnings Call Insight and Sentiment Analyzer

Analyzes earnings call transcripts for sentiment, key themes, and forward-looking signals.

FX Arbitrage Research Agent

Identifies and analyzes foreign exchange arbitrage opportunities across markets and instruments.

Capital Account Statements Agent

Processes and validates capital account statements for accuracy and investor reporting.

Common Issues

For help with common configuration issues, see the Common Issues page.

Get Started

Platform

Nodes

Account

​Core Functionality

​Tool Inputs

​Tool Outputs

​Overview

​Use Cases

​How It Works

​Step 1: Add the Code Tool

​Step 2: Open the Tool Configuration

​Step 3: Select the Language

​Step 4: Define Inputs

​Step 5: Write Your Code

​Step 6: Define Outputs

​Step 7: Write a Tool Description

​Step 8: Set Auto Run Behavior

​Step 9: Test the Tool

​Settings

​Best Practices

​Related Templates

Snowflake Analytics Agent

Earnings Call Insight and Sentiment Analyzer

FX Arbitrage Research Agent

Capital Account Statements Agent

​Common Issues

​Overview

​Use Cases

​How It Works

​Step 1: Add the Code Execution Node

​Step 2: Select the Language

​Step 3: Define Inputs

​Step 4: Write Your Code

​Step 5: Define Outputs

​Step 6: Test the Workflow

​Settings

​Best Practices

​Related Templates

Snowflake Analytics Agent

Earnings Call Insight and Sentiment Analyzer

FX Arbitrage Research Agent

Capital Account Statements Agent

​Common Issues

Core Functionality

Tool Inputs

Tool Outputs

Overview

Use Cases

How It Works

Step 1: Add the Code Tool

Step 2: Open the Tool Configuration

Step 3: Select the Language

Step 4: Define Inputs

Step 5: Write Your Code

Step 6: Define Outputs

Step 7: Write a Tool Description

Step 8: Set Auto Run Behavior

Step 9: Test the Tool

Settings

Best Practices

Related Templates

Common Issues

Overview

Use Cases

How It Works

Step 1: Add the Code Execution Node

Step 2: Select the Language

Step 3: Define Inputs

Step 4: Write Your Code

Step 5: Define Outputs

Step 6: Test the Workflow

Settings

Best Practices

Related Templates

Common Issues