Code - Docs

Overview

Use the Code node to process workflow data directly with custom JavaScript or Python code. Choose it when standard nodes are not enough, for example for calculations, formatting, validation, file processing, or custom business logic.

When to Use Code Node

Code nodes are perfect for:

Data transformations and formatting
Mathematical calculations
Custom business logic
JSON parsing and manipulation
Data validation and cleaning
Date/time operations
File processing with JavaScript and Python

What code nodes are not ideal for:

AI analysis (use Agent node)
External API calls (use HTTP Request node)
Simple conditions (use Condition node)
Long-running or interactive analysis (use Data Analysis)

Configuration

Language: Choose whether to write your code in JavaScript or Python. Code Editor: Write your transformation logic in the selected language in the code editor that opens when you select the Code node. Access Previous Nodes: Outputs from previous nodes are available as variables in the Code node. The available variable names are shown at the top of the code editor. To use the Code node output later, see Accessing Code Output.

Examples

Calculate Statistics

// Access data from previous nodes
const scores = agent.output.structured.scores || [];

// Calculate statistics
const average = scores.reduce((a, b) => a + b, 0) / scores.length;
const max = Math.max(...scores);
const min = Math.min(...scores);

// Return result
return {
  average_score: average.toFixed(2),
  highest_score: max,
  lowest_score: min,
  grade: average >= 90 ? "A" : average >= 80 ? "B" : "C"
};

# Access data from previous nodes
agent_output = agent.get("output", {})
scores = agent_output.get("structured", {}).get("scores", [])

if not scores:
    return {
        "average_score": 0,
        "highest_score": None,
        "lowest_score": None,
        "grade": "N/A"
    }

# Calculate statistics
average = sum(scores) / len(scores)
print(f"Processed {len(scores)} scores")

# Return result
return {
    "average_score": round(average, 2),
    "highest_score": max(scores),
    "lowest_score": min(scores),
    "grade": "A" if average >= 90 else "B" if average >= 80 else "C"
}

Validate and Clean Data

// Access form data
const email = trigger.output.email || "";
const amount = trigger.output.amount || 0;

// Validate
if (!email.includes("@")) {
  throw new Error("Invalid email format");
}

if (amount <= 0) {
  throw new Error("Amount must be greater than zero");
}

// Clean and return
return {
  email: email.trim().toLowerCase(),
  amount: parseFloat(amount.toFixed(2)),
  validated: true
};

# Access form data
trigger_output = trigger.get("output", {})
email = trigger_output.get("email", "")
amount = trigger_output.get("amount", 0)

# Validate
if "@" not in email:
    raise ValueError("Invalid email format")

if amount <= 0:
    raise ValueError("Amount must be greater than zero")

# Clean and return
return {
    "email": email.strip().lower(),
    "amount": round(float(amount), 2),
    "validated": True
}

Transform and Filter Arrays

// Access data from previous node
const customers = trigger.output.customers || [];

// Filter active customers
const activeCustomers = customers.filter(c => c.status === "active");

// Transform data
const processed = activeCustomers.map(customer => ({
  id: customer.id,
  name: `${customer.firstName} ${customer.lastName}`.trim(),
  email: customer.email.toLowerCase(),
  tier: customer.totalSpent > 1000 ? "premium" : "standard"
}));

return {
  customers: processed,
  total: processed.length,
  premiumCount: processed.filter(c => c.tier === "premium").length
};

# Access data from previous node
customers = trigger.get("output", {}).get("customers", [])

# Filter active customers
active_customers = [
    customer for customer in customers
    if customer.get("status") == "active"
]

# Transform data
processed = [
    {
        "id": customer.get("id"),
        "name": f"{customer.get('firstName', '')} {customer.get('lastName', '')}".strip(),
        "email": customer.get("email", "").lower(),
        "tier": "premium" if customer.get("totalSpent", 0) > 1000 else "standard"
    }
    for customer in active_customers
]

return {
    "customers": processed,
    "total": len(processed),
    "premiumCount": len([c for c in processed if c["tier"] == "premium"])
}

Date Operations

// Access event data
const events = trigger.output.events || [];
const now = new Date();

const processedEvents = events.map(event => {
  const eventDate = new Date(event.date);
  const daysUntil = Math.ceil((eventDate - now) / (1000 * 60 * 60 * 24));

  return {
    title: event.title,
    date: eventDate.toISOString(),
    formatted: eventDate.toLocaleDateString("en-US", {
      weekday: "long",
      year: "numeric",
      month: "long",
      day: "numeric"
    }),
    daysUntil: daysUntil,
    isUpcoming: daysUntil >= 0,
    isThisWeek: daysUntil >= 0 && daysUntil <= 7
  };
});

return {
  events: processedEvents,
  upcomingCount: processedEvents.filter(e => e.isUpcoming).length,
  thisWeekCount: processedEvents.filter(e => e.isThisWeek).length
};

from datetime import datetime, timezone

# Access event data
events = trigger.get("output", {}).get("events", [])
now = datetime.now(timezone.utc)

processed_events = []
for event in events:
    event_date = datetime.fromisoformat(event["date"].replace("Z", "+00:00"))
    days_until = (event_date.date() - now.date()).days

    processed_events.append({
        "title": event.get("title"),
        "date": event_date.isoformat(),
        "formatted": event_date.strftime("%A, %B %d, %Y"),
        "daysUntil": days_until,
        "isUpcoming": days_until >= 0,
        "isThisWeek": 0 <= days_until <= 7
    })

return {
    "events": processed_events,
    "upcomingCount": len([e for e in processed_events if e["isUpcoming"]]),
    "thisWeekCount": len([e for e in processed_events if e["isThisWeek"]])
}

JSON Processing

// Nested JSON from API response
const apiResponse = http_request.output || {};

// Extract and flatten nested data
const users = apiResponse.data?.users || [];

const flattened = users.map(user => ({
  id: user.id,
  name: `${user.first_name || ""} ${user.last_name || ""}`.trim(),
  email: user.contact?.email || "",
  city: user.address?.city || "Unknown",
  isActive: user.status === "active"
}));

return {
  users: flattened,
  total: flattened.length,
  activeCount: flattened.filter(u => u.isActive).length
};

# Nested JSON from API response
api_response = http_request.get("output", {})

# Extract and flatten nested data
users = api_response.get("data", {}).get("users", [])

flattened = [
    {
        "id": user.get("id"),
        "name": f"{user.get('first_name', '')} {user.get('last_name', '')}".strip(),
        "email": user.get("contact", {}).get("email", ""),
        "city": user.get("address", {}).get("city", "Unknown"),
        "isActive": user.get("status") == "active"
    }
    for user in users
]

return {
    "users": flattened,
    "total": len(flattened),
    "activeCount": len([user for user in flattened if user["isActive"]])
}

Aggregate and Summarize

// Sales data from previous node
const sales = http_request.output.sales || [];

// Group by category
const byCategory = {};
sales.forEach(sale => {
  const cat = sale.category || "Other";
  if (!byCategory[cat]) {
    byCategory[cat] = { total: 0, count: 0, items: [] };
  }
  byCategory[cat].total += sale.amount || 0;
  byCategory[cat].count += 1;
  byCategory[cat].items.push(sale);
});

// Calculate summary
const summary = Object.entries(byCategory).map(([category, data]) => ({
  category,
  totalRevenue: data.total.toFixed(2),
  orderCount: data.count,
  averageOrder: (data.total / data.count).toFixed(2)
}));

// Sort by revenue
summary.sort((a, b) => parseFloat(b.totalRevenue) - parseFloat(a.totalRevenue));

return {
  summary: summary,
  topCategory: summary[0]?.category || "None",
  grandTotal: sales.reduce((sum, s) => sum + (s.amount || 0), 0).toFixed(2)
};

# Sales data from previous node
sales = http_request.get("output", {}).get("sales", [])

# Group by category
by_category = {}
for sale in sales:
    category = sale.get("category") or "Other"
    if category not in by_category:
        by_category[category] = {"total": 0, "count": 0, "items": []}

    by_category[category]["total"] += sale.get("amount", 0)
    by_category[category]["count"] += 1
    by_category[category]["items"].append(sale)

# Calculate summary
summary = [
    {
        "category": category,
        "totalRevenue": f"{data['total']:.2f}",
        "orderCount": data["count"],
        "averageOrder": f"{data['total'] / data['count']:.2f}"
    }
    for category, data in by_category.items()
]

# Sort by revenue
summary.sort(key=lambda row: float(row["totalRevenue"]), reverse=True)

return {
    "summary": summary,
    "topCategory": summary[0]["category"] if summary else "None",
    "grandTotal": f"{sum(sale.get('amount', 0) for sale in sales):.2f}"
}

Create a File with JavaScript

JavaScript can produce workflow files by returning a files array. Each entry must include fileName, mimeType, and either text (for text/CSV/JSON content) or base64 (for binary content). The runtime uploads the files and attaches them to the node output under _files.

JavaScript

const customers = trigger.output.customers || [];
const activeCustomers = customers.filter((c) => c.status === "active");

const csv = [
  ["id", "email"],
  ...activeCustomers.map((c) => [c.id, (c.email || "").toLowerCase()]),
]
  .map((row) => row.map((cell) => `"${String(cell).replaceAll('"', '""')}"`).join(","))
  .join("\n");

return {
  active_count: activeCustomers.length,
  files: [
    {
      fileName: "active_customers.csv",
      mimeType: "text/csv",
      text: csv,
    },
  ],
};

Read Files from Previous Nodes

Files passed from previous nodes, such as form uploads or action outputs, are available in the Python working directory. Each file object carries a path you can pass directly to open() and the original filename under _metadata.name.

Python

# File upload fields contain a list of file objects
files = trigger.get("output", {}).get("resume", [])

processed = []
for file in files:
    with open(file["path"], "rb") as f:
        content = f.read()

    processed.append({
        "name": file["_metadata"]["name"],
        "size_bytes": len(content)
    })

return {"files_processed": processed}

Create a File with Python

Files created by Python in the working directory are attached to the node output under _files.

Python

import csv

customers = trigger.get("output", {}).get("customers", [])
active_customers = [c for c in customers if c.get("status") == "active"]

with open("active_customers.csv", "w", newline="") as file:
    writer = csv.DictWriter(file, fieldnames=["id", "email"])
    writer.writeheader()

    for customer in active_customers:
        writer.writerow({
            "id": customer.get("id"),
            "email": customer.get("email", "").lower()
        })

print(f"Created CSV with {len(active_customers)} customers")

return {
    "active_count": len(active_customers),
    "file_name": "active_customers.csv"
}

Accessing Code Output

Use the Code node name to access returned values from JavaScript or Python in subsequent nodes:

{{code_node_name.output.customer}}
{{code_node_name.output.total}}
{{code_node_name.output.formatted_date}}
{{code_node_name.output.processed_items[0].name}}

Files returned by JavaScript or created by Python in the working directory are available under _files:

{{code_node_name.output._files[0]._metadata.name}}

Language Capabilities

Code node capabilities depend on the selected language.

JavaScript

JavaScript runs in a secure sandbox environment with built-in utility functions:

ld.request(): Make HTTP requests
ld.log(): Output debugging information
Data conversions: CSV, Parquet, Arrow format conversions
File creation: Return a files array to expose generated files under _files
Standard JavaScript: JSON, Date, Math, Array, Object methods

Complete Utilities Reference

View all available sandbox utilities including data conversions, SQL validation, cryptography, and more.

Python

Python runs in a sandboxed environment without internet access.

Use top-level return to set the node output
Use print() to write logs
Use preinstalled data and document libraries such as pandas, numpy, openpyxl, and pypdf
Access previous node outputs as variables when their slugs are valid Python identifiers
Read files from previous nodes by passing file["path"] to open()
Save files in the working directory to expose them under _files
Run without internet access

The JavaScript ld.* utilities are not available in Python.

Best Practices

Return Structured Objects

Return data as objects for easy access in later nodes. This makes it simple to reference specific values in subsequent nodes using dot notation.

Handle Missing Data

Use ||, optional chaining (?.), or Python .get() to provide default values and prevent errors when data is undefined or null.

Use Error Handling

Wrap risky operations in try/catch for JavaScript or try/except for Python. This helps prevent workflow failures and provides meaningful error messages.

Keep It Simple

Complex logic might be better in an Agent node. Use code nodes for straightforward transformations and calculations, not for tasks requiring intelligence or context understanding.

Keep Inputs Small

Code nodes fail if the combined output of previous nodes exceeds 5 MB. Reduce upstream output size, process fewer loop items, or disable loop output collection before the code node.

Add Comments

Document what your code does for future reference. Clear comments help you and your team understand the logic when revisiting the workflow later.

Limits

Each Code node has a 5 MiB limit on the combined input it receives from previous nodes. Before your code runs, Langdock measures the size of all upstream outputs that the node can access. If they exceed 5 MiB, the node fails with an error and your code is never executed. Common causes:

A Loop node that collects outputs from many iterations into a single array.
A previous node that returns a very large API response, file content, or dataset.
Deeply nested objects that cannot be safely measured.

If you hit this limit, do one of the following:

Reduce the size of the upstream node output by filtering, summarizing, or selecting only the fields you need.
Process fewer items per iteration in any preceding Loop node.
Disable output collection on the preceding Loop node when you do not need to read its aggregated results.

Next Steps

Agent

Use AI for intelligent processing

HTTP Request

Fetch external data

Data Analysis

Analyze data with an Agent

​Overview

​When to Use Code Node

​Configuration

​Examples

​Calculate Statistics

​Validate and Clean Data

​Transform and Filter Arrays

​Date Operations

​JSON Processing

​Aggregate and Summarize

​Create a File with JavaScript

​Read Files from Previous Nodes

​Create a File with Python

​Accessing Code Output

​Language Capabilities

​JavaScript

Complete Utilities Reference

​Python

​Best Practices

​Limits

​Next Steps

Agent

HTTP Request

Data Analysis

Overview

When to Use Code Node

Configuration

Examples

Calculate Statistics

Validate and Clean Data

Transform and Filter Arrays

Date Operations

JSON Processing

Aggregate and Summarize

Create a File with JavaScript

Read Files from Previous Nodes

Create a File with Python

Accessing Code Output

Language Capabilities

JavaScript

Python

Best Practices

Limits

Next Steps