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VS Code with a LoT notebook open showing markdown and LoT code cells, Coreflux Explorer and project tree on the left, Data Viewer below, and MQTT connection in the status bar

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The Notebook Workflow

LoT Notebooks combine Documentation in Markdown and LoT code in a single file, so you can both run your code and keep your documentation together, using the notebook as living documentation for your project.
Picture a smart kitchen: Markdown is the recipe on the page, each LoT code cell is how you run each step of the recipe, and the Data Viewer shows you the results of each step.

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Install and connect

1

Install the extension

Animated walkthrough: VS Code Extensions view, search for coreflux, install Coreflux LoT, Coreflux icon appears in the Activity Bar
  1. Open VS Code.
  2. Open the Extensions view in the sidebar (or use the shortcut Ctrl+Shift+X on Windows/Linux, Cmd+Shift+X on macOS).
  3. Search for LoT Notebooks by Coreflux and install it.
2

Connect to the broker

VS Code: connect to MQTT broker via LoT extension; status bar shows MQTT connected
  1. Click the MQTT connection status in the bottom-left of VS Code, or click Connect to Broker in the Data Viewer (Coreflux sidebar).
  2. Enter your broker URL, username, and password.
Use a full URL such as mqtt://localhost:1883 or mqtts://your-host:8883 for TLS.
SettingExample (local defaults)
URLmqtt://localhost:1883
Usernameroot
Passwordcoreflux (change immediately in production)
When you are connected, the status bar shows a green MQTT: Connected. If any issues occur, the status bar will become red.
The extension securely stores your credentials. You only need to configure them once per workspace.

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Create a new project

Animated walkthrough: creating a new .lotnb file from the Coreflux PROJECT section in VS Code
  1. In the Activity Bar, open the Coreflux view. Under PROJECT, use New File to create a notebook and save it in your workspace (use New Folder first if you want a dedicated project folder).
  2. In the notebook, add Markdown and LoT cells from the toolbar as needed.

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Add LoT code to a LoT Notebook

Animated walkthrough: insert a code cell in a LoT Notebook and add LoT definition text in VS Code
  1. Open your LoT Notebook in VS Code.
  2. From the notebook toolbar, add a code cell.
  3. Type or paste your LoT code (DEFINE ACTION, DEFINE MODEL, and so on). For a first example, use the code below.
DEFINE ACTION Heartbeat
ON EVERY 10 SECONDS DO
    PUBLISH TOPIC "system/heartbeat" WITH TIMESTAMP "UTC"

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What type of cells you can add to LoT Notebooks

LoT Notebooks support three types of cells, each with different behavior:

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Markdown Cells - Documentation

LoT Notebook markdown cell showing a heading and body text with the cell toolbar for formatting and actions
Markdown cells are for documentation only—they don’t execute. Use them to:
  • Explain what your code does and why
  • Document configuration requirements
  • Add diagrams, tables, and formatted text
  • Create section headers and navigation

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LoT Code Cells - Runnable Logic

LoT Notebook LoT cell with TemperatureSimulator action, run button, and status bar showing Upload to broker and LOT cell type
LoT cells contain executable LoT (Language of Things) code. When you run a LoT cell:
  1. The extension parses your code
  2. Sends it to the connected broker via MQTT
  3. The broker compiles and registers the definition
  4. You receive confirmation or error feedback
For what LoT is, how it fits the broker, and more examples, see Introduction to LoT.

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Python Cells - Advanced Logic

LoT Notebook Python cell with Script Name header, function definitions, run button, and Python label in the status bar
Python cells contain scripts that run inside the broker when called from LoT Actions. The broker’s Python runtime executes your functions. 
# Script Name: DataProcessor
def calculate_average(values):
    """Calculate average from a list of numbers"""
    if not values:
        return 0
    return sum(values) / len(values)

def format_alert(sensor_id, value, threshold):
    """Generate formatted alert message"""
    return f"ALERT: Sensor {sensor_id} exceeded {threshold} with value {value}"
Python scripts require a specific format with # Script Name: YourScriptName as the first line. Without this header, the script won’t be recognized.

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Running LoT

Animated walkthrough: Run on a LoT notebook cell to deploy the definition to the connected Coreflux broker
When you click the Run button on a cell, the Extension builds your code, checks for errors and deploys to the broker. If successful, a simple Green response will appear. If any error is found, the code is not deployed and a error message will appear with the error details.

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What else you can do with the LoT extension

Animated walkthrough of the LoT Data Viewer in VS Code: subscribing to topics and viewing live MQTT traffic
Use the Coreflux view (Activity Bar) for broker connection, entities, secrets, license, clients, and live MQTT—without a separate client for most tasks.
CapabilityWhy it helps
See what is running in the BrokerSee what Actions, Models, Routes, and Rules the broker actually has after a deploy.
Remove LoT codeOn Coreflux Explorer, remove one or more LoT entities.
Manage secrets and env varsConfigure the secrets within the broker, to manage secure credentials
See license usageSpot license limits when deploy fails—before you debug LoT as a syntax issue.
See connected clientsSee who is connected; useful when a device or app should be online but is not.
MQTT Data ViewerLive topics and payloads in the Data Viewer.
Execution TraceFor a LoT cell, see it’s execution history and which topics and variables that run touched.
For more on the Data Viewer, replay, formatters, and trace, see How to Debug in LoT Notebooks.

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Develop LoT Notebooks with AI

Animated walkthrough: VS Code with a LoT notebook open and GitHub Copilot chat focused on a notebook cell
Alongside the extension, you can develop with AI using Copilot, just like any programming language. For better results, you can add the Coreflux MCP server to the agent, so it can read official docs, and add AGENTS.md file for LoT coding practices, so it works like a LoT expert. To for more details on how to use AI with LoT, check this page: Developing with LoT Using AI.

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Version control with Git

VS Code Source Control showing pending changes across .lotnb notebooks, docker-compose.yml, markdown, and editor config files in the same repo
Use Git on your workspace folder like any project: commit .lotnb files aswell as any other files, like documentation, Docker files and supporting files.

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Best Practices

Use markdown cells liberally to explain your code. Future you (and teammates) will appreciate the context.
Keep each Action, Model, or Route in its own cell. This makes debugging easier and allows selective execution.
Run cells one at a time when developing. This helps isolate issues and understand system behavior.
Name your Actions, Models, and Routes clearly. They appear in the broker’s entity lists and should be self-explanatory.

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Troubleshooting

  • Check that you’re connected to the broker (look for connection status in status bar)
  • Verify your credentials are correct
  • Ensure the broker is running and accessible
If running a cell shows an error and the broker rejects the definition because you have reached a license limit (for example on actions, routes, or another licensed resource), deployment stops and the message under the cell should describe the limit. Open the Coreflux view in the Activity Bar and check the Coreflux Explorer section for your license status and usage so you can see what is capped and plan next steps (reduce definitions or adjust your license).
  • Confirm the first line is # Script Name: YourScriptName
  • Check for syntax errors in the Python code
  • Ensure function names match what you’re calling from LoT
  • Check the output for error messages
  • Verify no syntax errors in your LoT code
  • Use the Coreflux Entities panel to refresh the list

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Next Steps

Debug in VS Code

Use cell output, Coreflux Entities, and live MQTT values to fix issues.

Explore Tutorials

Work through hands-on notebooks covering Actions, Models, Routes, and Python.