A Google Apps Script wrapper for the Google AppSheet API – bienlim/AppSheetApp
Recently I had a nice ping into my LinkedIn feed from App Developer Bien Lim. Bien has enhanced my original AppSheetApp library for Apps Script with a powerful new feature: fetchAll(). This addition lets you run multiple AppSheet API calls at the same time, significantly boosting the performance of your Apps Script integrations.
Building on the library’s existing methods like Add, Delete, Edit, and Find, this new function allows developers to execute these calls in parallel. This can drastically reduce execution time, which is a big improvement for applications that rely on multiple API operations.
You can find the updated library on the code repository to start using this new feature today.
Example Code
Here is a snippet showing how you can use the new fetchAll() method:
/**
* Executes multiple AppSheet API requests in parallel.
* This example shows how to perform Add, Delete, Edit, and Find operations simultaneously.
*/
function parallelRequest(){
// Replace with your actual App ID and Access Key
const AppSheet = new AppSheetApp('YOUR_APP_ID', 'YOUR_ACCESS_KEY');
// Placeholder data for demonstration
const properties = { "Locale": "en-US" };
// Sample data for adding a new record. The key field is usually omitted if it's auto-generated.
const dataToAdd = [{"Name": "John Doe", "Age": 30}];
// Sample data for editing an existing record. The key field is required to identify the row.
const dataToEdit = [{"ID": "unique-id-123", "Age": 31}];
// Sample data for deleting an existing record. The key field is required.
const dataToDelete = [{"ID": "unique-id-456"}];
// The FetchAll method takes multiple API calls as arguments.
// The 'true' argument tells each method to return a parameter object instead of
// making an immediate API call. These parameter objects are then passed to fetchAll().
const responses = AppSheet.fetchAll(
AppSheet.Add('People', dataToAdd, properties, true),
AppSheet.Delete('People', dataToDelete, properties, true),
AppSheet.Edit('People', dataToEdit, properties, true),
AppSheet.Find('People', [], properties, true)
);
// The responses are returned in an array, in the same order as the requests.
const [ respFromAdd, respFromDelete, respFromEdit, respFromFind ] = responses;
// You can now handle each response individually
console.log('Add Response:', respFromAdd);
console.log('Delete Response:', respFromDelete);
console.log('Edit Response:', respFromEdit);
console.log('Find Response:', respFromFind);
}
As developers, we are constantly looking for ways to improve our workflow and the quality of our code. The recent rise of powerful AI assistants, like Google’s Gemini, has opened up new frontiers for productivity, particularly in the realm of code reviews. These tools can offer instant feedback, suggest optimisations, and spot potential bugs we might have missed.
However, there’s often a practical hurdle: providing the AI with the complete context of our project. For Google Apps Script developers, this can mean the tedious process of copying and pasting code from multiple .gs and .html files, not to mention the crucial appsscript.json manifest.
To solve this, I’ve developed a simple tool built, fittingly, with Apps Script itself. It’s a Google Sheet that lets you export an entire Apps Script project into a single, neatly formatted JSON file, ready to be added directly to the Gemini App or a Gemini Gem.
Why This Approach?
Holistic Reviews: By packaging every file—including the manifest with its scopes and dependencies—you give the AI the bigger picture leading to more accurate analysis.
Boosted Efficiency: Forget manual copy-pasting. A couple of clicks are all it takes to get a complete project export, saving you time and preventing errors.
How It Works & How to Prompt Gemini
To get the most out of your exported project file, it helps to understand its structure and how to tell your AI assistant what it’s looking at.
The JSON Structure
The tool uses the official Google Apps Script API to fetch your project’s content. The resulting .json file contains a list (an array) of File objects. Based on the official documentation, each object in the list represents one file from your project and looks something like this:
name: The name of the file (e.g., “Code”, “Index”, “appsscript”).
type: The kind of file it is. This will be SERVER_JS for .gs files, HTML for .html files, and JSON for the appsscript.json manifest.
source: The actual code or content of the file as a string.
Prompting Gemini for the Best Results
When you upload this file, you can give Gemini a simple instruction to ensure it understands the context. Here is a sample prompt you can adapt:
“Please review the attached Google Apps Script project. The JSON file contains an array of file objects, where each object has a ‘name’, ‘type’, and ‘source’. Please analyse all the files together as a single project to provide a comprehensive review.”
This prompt tells Gemini exactly how to interpret the file, ensuring it sees the appsscript.json manifest for its scopes, the server-side logic in the .gs files, and any client-side .html files as a complete, interconnected project.
Tip: For scripts bound to a Google Sheets, Docs, or Slides, to get the most accurate review, add these documents to your Gemini conversation alongside the exported JSON file to give Gemini the complete context.
Take It to the Next Level: Create a Custom Gem
To save even more time, you can embed these instructions into a custom Gem in the Gemini App. This creates a reusable ‘persona’ for your code reviews. When creating your Gem, you can provide it with more detailed instructions to focus its analysis.
Here is a more advanced set of instructions you could use for your “Apps Script Code Reviewer” Gem:
You are an expert Google Apps Script developer who specialises in writing clean, efficient, and secure code.
When I provide a JSON file, it will represent a complete Apps Script project. The file contains an array of file objects, each with a 'name', 'type', and 'source'.
Your task is to perform a thorough code review of the entire project. Please analyse all files together.
Specifically, focus on:
1. **Best Practices:** Check if the code follows the official Google Apps Script style guide and modern JavaScript (ES6+) conventions.
2. **Performance:** Identify any potential bottlenecks, inefficient loops, or excessive API calls.
3. **Security:** Look for any potential security vulnerabilities, especially concerning data handling and web app permissions.
4. **Clarity and Readability:** Assess the code for clarity, and check for adequate comments and meaningful variable names.
Please provide your feedback in a structured format, starting with a high-level summary and then a detailed list of suggestions, grouped by file.
If you are suggesting code revisions, provide the fully revised file.
Getting Started
Here’s how to set up your own Apps Script project exporter.
Step 1: Prerequisites – Enabling the Apps Script API
The tool relies on the Apps Script API to fetch project files. To use it, you first need to link your script project to a Google Cloud Platform (GCP) project where the API is enabled.
Create a GCP Project: If you don’t already have one, create a standard GCP project by visiting the Google Cloud Console.
Enable the Apps Script API: Within your GCP project, navigate to the “APIs & Services” dashboard, click “+ ENABLE APIS AND SERVICES”, search for “Google Apps Script API”, and enable it.
Configure the OAuth Consent Screen: Before the script can be authorised, you must configure the consent screen.
In the GCP Console, navigate to “APIs & Services” > “OAuth consent screen”.
Choose Internal for the User Type and click Create.
Fill in the required fields (App name, User support email, and Developer contact information) and click Save and Continue. No other configuration is needed. As this is for internal use, you do not need to submit the app for verification.
Link to Your Apps Script Project:
Open your Apps Script project (the one containing the exporter code).
Go to “Project Settings” (the cog icon ⚙️).
Under “Google Cloud Platform (GCP) Project”, click “Change project” and enter your GCP project number.
Step 2: Setting Up the Exporter Sheet
Make a Copy: Click this link to make your own copy of the pre-configured Google Sheet Template.
Configure the Destination Folder:
In your new sheet, go to Extensions > Apps Script.
In the Code.gs file, find the line const DEST_FOLDER_ID = '...';
Replace the placeholder ID with the ID of the Google Drive folder where you want your JSON exports to be saved. You can get this from the folder’s URL (it’s the string of characters after folders/). Tip: Your copy of this template can be shared with your colleagues. Also share the folder with them so that the script can write the export file.
Save and Authorise: Save the script project. Return to your sheet and reload the page. A new “Script Exporter” menu will appear. The first time you run it, you will be prompted to authorise the script’s required permissions.
Step 3: Exporting Your First Project
Using the tool is the easiest part!
Find the Script ID: First, you need the ID of the project you wish to review. The easiest way to find this is by copying it from the script editor’s URL.
For standalone scripts: The URL will look like https://script.google.com/d/THIS_IS_THE_ID/edit. The ID is the long string of characters between /d/ and /edit.
For container-bound scripts (in a Sheet, Doc, etc.): From your Google Sheet or Doc, open the script editor via Extensions > Apps Script. The URL in this new browser tab will have the same format. Copy the ID from there.
Run the Exporter: In your exporter sheet, click Script Exporter > Export Script Project.
Paste the ID: When the dialog box appears, paste in the Script ID you just copied and click OK.
Check the Output: The script will fetch the project files, create a JSON file in your designated Drive folder, and confirm with a success message. A log of the export, including a link to the file, will be automatically added to the “Execution Log” sheet.
You can now head over to the Gemini App (gemini.google.com), add the JSON file from Google Drive, and start your comprehensive code review!
I hope you find this tool as useful, it’s a small optimisation, but one that hopefully makes integrating Gemini for Workspace into our daily Apps Script development practices that much smoother. I’d love to hear if you use this or similar approaches and any other tips you have for Gemini assisted Google Workspace development. Happy scripting!
🤖 AI-powered Gmail assistant that automatically analyzes emails and drafts responses using Google’s Gemini AI
Google is rapidly integrating Gemini across the Workspace platform, bringing powerful AI features directly into the tools we use every day. While these native integrations offer incredible value, developer-led solutions provide a tantalising glimpse into the future of hyper-personalised automation, especially as we look toward the potential of the new Google Workspace Flows. This is where the real magic begins, allowing for custom agents that can handle complex, multi-step tasks.
Google Developer Advocate Zack Akil has created one such example solution that turns your Gmail inbox into a smart, automated assistant. His open-source project, the “AI-got-this-gmail-delegator,” is a Google Apps Script that uses the Gemini API to analyse incoming emails, categorise them, and even draft replies based on your own knowledge and writing style.
This project is a great example of how to combine the power of Google Workspace automation with the intelligence of modern AI. The script works by:
Analysing Incoming Emails: When a new email arrives, the script sends the content to the Gemini API for analysis.
Providing Context: The AI’s analysis is guided by a “knowledge base” that you provide in a Google Doc. This could include your role, common responses, or specific project details.
Intelligent Triage: Based on the email’s content and your knowledge base, the script decides whether the email needs a reply, requires manual intervention, or can be archived.
Drafting Replies: For emails that need a response, the script uses Gemini to generate a draft reply in your own writing style, ready for you to review and send.
The entire process is logged in a Google Sheet, giving you a complete overview of your AI assistant’s activity.
What makes this project so compelling is its practicality and customisability. By modifying the Google Doc knowledge base and the writing style prompts, you can create a personalised email assistant that understands the nuances of your work.
To explore the code and set up your own AI email delegator, check out the project on GitHub.
In this video we build a Chat app as a Google Workspace add-on with Apps Script and extend it to other Workspace applications (Calendar, Gmail, Drive, Docs, Sheets, and Slides).
To see this new framework in action, Pierrick Voulet from the Google Developer Relations team has released a fantastic video tutorial, ‘Build a Chat app as a Google Workspace add-on with Apps Script’.
The ‘OOO Assistant’: A Practical Demonstration
The video centres on building an ‘Out-of-Office Assistant’ that can block out the user’s Calendar for the day and set a Gmail out-of-office auto-reply. The real magic is how this Chat-based logic extends into a full Workspace Add-on, available in the sidebars of Gmail, Calendar, Drive, Docs, Sheets and Slides. This is achieved with minimal code changes, primarily by configuring the appsscript.json manifest to include:
A homepageTrigger to display a UI card in the add-on sidebar.
universalActions that reuse the exact same functions (blockDayOut(), setAutoReply()) originally built for the Chat quick commands.
The video provides a complete overview of the process, and you can get hands-on with the shared code yourself. For more technical details, check out the official Extend Google Chat documentation.
Learn how to add, update and search or query for custom Google Drive properties in file metadata in with Google Apps Script
Managing and searching for project files in Google Drive can be a challenge, especially when naming conventions are not followed. While Google’s Drive Labels API offers a solution, its use is often restricted to administrators, leaving many developers looking for a more accessible alternative.
Over on his blog, Yagisanatode, developer Scott Donald provides an in-depth tutorial on using custom file properties with Apps Script. This technique allows you to programme-matically attach your own searchable metadata—like a project ID, version number, or status—to any file in Drive.
The tutorial walks you through the entire process, including:
Enabling the Drive API v3 advanced service.
Using Drive.Files.update() to add properties to a file.
Crafting queries with Drive.Files.list() to find files based on your custom properties.
For developers working at scale, the tutorial also covers how to avoid slow, iterative updates. Scott introduces a powerful batch update method that uses UrlFetchApp to efficiently modify thousands of files in a single request.
This is an excellent, practical guide for any intermediate developer looking to build more organised and robust file management systems in Google Drive.
Nine years ago, I wrote an article describing how to scrape data from the internet in about five minutes. It featured a Google Apps Script library that allowed you to specify what to download from a webpage by identifying the text surrounding your target information. This became one of my most-read articles, and the library itself saved me a significant amount of time.
With the advent of large language models like Gemini, this entire paradigm is shifting. Just as “vibe coding” describes a more intuitive approach to programming, I’d say we’re now entering an era of “vibe-scraping.”
Using data from the web has long been a staple. But what if you could just describe the information you want and have an AI fetch it for you?
Google Developer Expert, Ivan Kutil, introduces a modern approach he calls “vibe scraping.” In his latest post, he demonstrates a powerful method that combines Google Apps Script with the Gemini API to pull structured data from any webpage directly into a Google Sheet.
A Practical Example: From Movie Links to a Full Database
To understand the power of this technique, Ivan set out to solve an old problem. He had a Google Sheet with a list of movies, each with a URL pointing to its page on ČSFD (a popular Czech-Slovak film database). His goal was to automatically populate columns with details like genre and rating by extracting the data directly from those linked pages.
At the heart of his solution is a new, experimental Gemini API feature called the URL Context tool. As the documentation notes, this tool is surprisingly flexible: it “can then retrieve content from the URLs and use that content to inform and shape its response.”
The tool can be used in two main ways:
URL Context “By Itself”: In this mode, the tool directly fetches and processes the content from the URL you provide. This is a straightforward way to analyze the live content of a single page.
URL Context with Google Search Grounding: In this mode, the tool can read content from a URL and then use Google Search for a more in-depth understanding.
What makes Ivan’s script so elegant is how it combines this tool with the simplicity of Google Sheets. For each row, the script constructs a prompt for the Gemini API, asking it to analyse the content of the provided URL and find the specific information defined in the spreadsheet headers.
The result is a system where modifying your data query is as easy as renaming a column. This turns a potentially complex scraping task into a simple spreadsheet edit, allowing anyone to create powerful, custom data importers.
To get the full details and the code to build this yourself, check out Ivan’s original post.
Last month, we introduced the ability to take basic actions to create and modify tables via the Google Sheets API. This week, we’re expanding the API functionality to include the ability to write and read file and people smart chips.
Following last month’s update for tables, it was recently announced that the Google Sheets API now allows developers interact with the file and people smart chips. This update provides developers with direct access to manage these types of smart chips, which were previously only available through the user interface. For detailed documentation and code snippets on implementation, refer to the official Google Developers support page.
Here is a quick overview of what you can do:
Reading Smart Chips: You can retrieve data for all smart chips within a cell. The API response will detail the properties for chipped sections of text and will also identify the plain text sections of the cell. As noted in the documentation to get the chip’s display text, you must request the formattedValue.
Adding Smart Chips: You can now insert smart chips for people and files into cells.
People Chips: These can be created to link to an individual using their email address. You can also specify the display format for the person’s name.
Rich Link Chips: These can be used to create a chip that links to a Google Drive file. Note that writing these chips requires your application to have at least one Drive OAuth scope.
In the feature image above you can hopefully see how a recruitment tracker could be created and read. In the example people chips are used for the ‘Applicant’ and ‘Interviewer’ columns, providing immediate access to contact information. A file chip is used in the ‘Resume’ column to link directly to the candidate’s document in Google Drive. A developer could automate this entire process. For instance, a script could be triggered when a new application is submitted, automatically adding a new row to the tracker complete with the relevant person and file chips, streamlining the entire recruitment workflow.
For those using Google Apps Script, these new functionalities are accessible via the Google Sheets Advanced Service. This update provides better methods for managing smart chips in Google Sheets.
As powerful as Google’s new AI tools are, those of us who live in Google Workspace have likely encountered some of their current limitations. One of the most significant for me is in Google Chat. While Gemini can analyse recent conversations, it seems to be limited to roughly the last 20 messages or 7 days of history. Crucially, it also can’t see or process messages sent by apps or webhooks, which are often the lifeblood of a technical team’s space.
This creates a knowledge gap. How can you analyse project history, debug an automated alert, or get a complete overview of a conversation if your tools only see a fraction of the data?
To solve this I’ve created an automated solution using Google Apps Script. This tool can archive an entire Google Chat space history into two powerful, synchronised formats:
A structured JSON file that serves as the definitive, machine-readable “source of truth”.
A clean, human-readable Google Doc, which is automatically generated from the JSON data.
This dual-output approach provides the best of both worlds: a portable data file perfect for backups or analysis, and a browsable document ideal for quick searches or for use as a direct data source in a Gemini Gem or other tools like NotebookLM.
Best of all, once configured, you can set up a time-driven trigger to run the script automatically, ensuring your archives are always kept up-to-date without any manual intervention.
Prerequisites
Before you begin, please ensure you have the following:
A Google Workspace Account: For setup this can only be a Google Workspace account, once it is configured it is possible to test with a personal consumer Gmail account.
Access to Google Cloud Platform (GCP): You will need the ability to create a new GCP project and enable APIs. The guide below will walk you through creating a fresh project, but if you are using a Google Workspace account, you may need to request a project depending on your organisation’s policies.
User-Based Access: This script runs as you, the user who authorises it. Therefore, it will only be able to see and archive Google Chat spaces that you are a member of.
The Setup Guide
Getting started is straightforward. The core logic is pre-packaged in a Google Sheet template. You just need to make a copy and connect it to your own Google Cloud project.
Step 1: Copy the Google Sheet Template
First, make your own personal copy of the Chat Archiver template.
Create a new project. Give it a memorable name (e.g., “My Chat Archiver”).
Enable the Google Chat API:
In your new project, navigate to the “APIs & Services” > “Enabled APIs & services” and enable the Google Chat API and Google Drive API , or Enable the APIs
If you see a message that says Google Auth platform not configured yet, click Get Started
Add an App name and support email
Choose a user type. Internal is best if you’re on a Workspace account. External will work for anyone but require you to add yourself as a test user (you can add up to 100 testers without the need of going through verification).
Click Next.
Under Contact Information, enter an Email address where you can be notified about any changes to your project, then click Next.
Under Finish, review the Google API Services User Data Policy and if you agree, select I agree to the Google API Services: User Data Policy, then click Continue.
In the Apps Script editor, go to Project Settings (the cog icon ⚙️).
Under “Google Cloud Platform (GCP) Project”, paste the Project Number and click Set Project.
Your copy of the sheet is now correctly configured and authorised to make requests to the Chat API on your behalf.
Step 3: Run Your First Archive
Refresh your copied Google Sheet. A new menu named Chat Archiver will appear.
In the Config tab, paste a Google Chat space ID or full room URL and add your email address (you can share your copy of the Google Sheet with other users who will be able to jump to this step).
Click Chat Archiver > Archive All Spaces.
The first time, a dialogue will appear asking for authorisation. Grant the permissions to allow the script to run.
The script will now run, creating both a JSON file and a Google Doc, and will place their links in the corresponding columns in the sheet.
(Optional) Click Chat Archiver > Setup 15-Minute Trigger so setup a trigger to update the archive documents every 15 minutes.
Understanding the Outputs
The script produces two distinct files, each with a specific purpose:
The JSON File: The primary output is a structured JSON file. It takes the chat messages and uses a nested format, which groups replies under their parent message, makes it easier for data analysis with LLMs.
The Google Doc: Alongside the JSON, the script generates a clean Google Doc. This format is perfect as knowledge for a Gem or as a data source in NotebookLM. Crucially, this document is completely regenerated from the JSON data on every run. This means it is a “read-only” artefact; any manual changes will be overwritten.
Important Considerations and Limitations
While this solution is powerful and robust for many use cases, it’s important to understand its design limitations and potential areas for future improvement.
Access is User-Based (By Design) – The script runs with the permissions of the user who authorises it. This means it can only access and archive Google Chat spaces that the user is currently a member of.
Future Improvement: For a true, organisation-wide backup solution, this script could be adapted to use a Service Account. With domain-wide delegation granted by a Workspace administrator, a service account could access any chat space in the domain, regardless of membership. This is a more complex setup but is the standard for enterprise-level automation.
Archives are Text-Only – This solution is focused on archiving the textual content of conversations. It does not save file attachments like images, PDFs, or videos. The JSON data will note that a message contained an attachment, but it won’t contain the file itself.
Data is Append-Only – The script is designed to add new messages to the archive. It does not synchronise edits or deletions. If a message is edited in Google Chat after it has been archived, the JSON file will still contain the original text. If a message is deleted, it will remain in the archive.
Apps Script Execution Limits – Google Apps Script has built-in limits to prevent abuse.
Execution Time: A single script run is limited to 6 minutes for standard Gmail accounts or 30 minutes for Google Workspace accounts. For an exceptionally large space (many hundreds of thousands of messages), the very first archive could potentially time out. Using the “Archive Start Date” field is the best way to manage very large initial backfills.
File Size: There is a 50 MB limit on the size of a data blob that Apps Script can create. As the JSON archive grows, a single file may eventually hit this limit. For extremely large spaces, the script could be modified to create new files on a yearly or quarterly basis.
Not a Compliance Substitute for Google Vault – For organisations that require eDiscovery, legal holds, and retention policies for compliance reasons, Google Vault is the official, authoritative tool. This script should be seen as a powerful data extraction and analysis tool, not a substitute for an official compliance solution.
Ideas for Your Archive
Now that you have your complete chat history in a structured format, what can you do with it? Here are a few ideas to get you started:
Create a Project FAQ with Gemini: Provide the generated Google Doc or JSON file to Gemini and ask it to synthesise a “Frequently Asked Questions” document about a specific project. It can pull out key decisions, find links to important resources, and identify common questions your team has answered.
Generate an Infographic Summary: Use a prompt like, “Based on this JSON data, identify the top 5 most active members, the busiest day of the week, and the main topics discussed. Present this as data for an infographic.” You can use the Gemini App ‘canvas’ to create a webpage or feed the structured output into a design tool.
Onboard New Team Members: Give a new team member the Google Doc archive and ask Gemini to act as a project expert. They can ask questions like, “What was the final decision on the ‘Phoenix’ feature?” or “Summarise the key discussions from last March,” and get instant, context-aware answers.
Perform Topic and Sentiment Analysis: The structured JSON file is perfect for programmatic analysis. You could ask Gemini to write a simple script (or use a Colab notebook) to parse the messages, perform sentiment analysis over time to track team morale, or use natural language processing to identify recurring topics and pain points.
Build a Project Timeline: Ask Gemini to scan the archive for key dates, deadlines, and project milestones mentioned in the chat, then format the results as a timeline or a table that you can use for project retrospectives and reports.
Summary
Ultimately, this tool is about providing an easy way for you to unlock the value hidden in your team’s conversations. By moving beyond the current limitations and including every message, you create a better true source of truth for analysis and reporting. While we hope future updates to Gemini for Workspace will make this kind of deep integration seamless, for now, this solution gives you more control over your data and a process you can experiment with. I hope this guide helps you get started quickly and if you would like to explore enterprise level solutions get in touch.
The landscape of what’s possible within the browser is quietly undergoing a significant shift, and for Google Workspace Add-on developers, this could be a game-changer. Chrome’s AI mission is simple yet powerful: to ‘make Chrome and the web smarter for all developers and all users.’ We’re seeing this vision begin to take shape with the emergence of experimental, built-in AI APIs in Google Chrome, designed to bring powerful capabilities using models like Gemini Nano directly to the user’s device.
There is a growing suite of these on-device APIs. This includes the versatile Prompt API, specialised Writing Assistance APIs (like Summarizer, Writer, and Rewriter), Translation APIs (Language Detector and Translator), and even a newly introduced Proofreader API. For many existing Workspace Add-on developers, some of the more task-specific APIs could offer a relatively straightforward way to integrate AI-powered enhancements.
However, my focus for this exploration, and the core of the accompanying demo Add-on being introduced here, is the Prompt API. What makes this API particularly compelling for me is its direct line to Gemini Nano, a model that runs locally, right within the user’s Chrome browser. This on-device approach means that, unlike solutions requiring calls to external third-party GenAI services, interactions can happen entirely client-side. The Prompt API provides web applications, including Google Workspace Editor Add-ons, with an open-ended way to harness this local AI for text-based generative tasks.
To put the Prompt API’s text processing abilities through its paces in a practical Workspace context, I’ve developed a Google Workspace Add-on focused on text-to-diagram generation. This post delves into this demonstration and discusses what on-device AI, through the versatile Prompt API, could mean for the future of Workspace Add-on development, including its emerging multimodal potential.
Why This Matters: New Horizons for Google Workspace Developers
Using an on-device LLM like Gemini Nano offers several key benefits for Workspace Add-on developers:
Enhanced Data Privacy & Simplified Governance:Sensitive user data doesn’t need to leave the browser, meaning no external API calls are made to third-party servers for the AI processing itself, which is a huge plus for privacy and can simplify data governance including Google Workspace Marketplace verification and Add-on data privacy policies.
Potential for Cost-Free GenAI (with caveats!): Client-side processing can reduce or eliminate server-side AI costs for certain tasks. Remember, “Nano” is smaller than its cloud counterparts, so it’s best for well-scoped features. This smaller size means developers should think carefully about their implementation, particularly around prompt design to achieve the desired accuracy, as the model’s capacity for understanding extremely broad or complex instructions without guidance will differ from larger models.
Improved User Experience & Offline Access:Expect faster interactions due to minimise network latency.
The biggest takeaway here is the opportunity to explore new avenues for GenAI capabilities in your Add-ons, albeit with the understanding that this is experimental territory and on-device models have different characteristics and capacities compared to larger, cloud-based models.
Proof of Concept: AI-Powered Text-to-Diagram Add-on
To showcase the tangible possibilities of on-device text processing, the demonstrator Add-on (available in the Nano-Prompt-AI-Demo GitHub repository) focuses on a text-to-diagram use case:
Users can describe a diagram in natural language (e.g., “flowchart for a login process”).
The Add-on then uses the Gemini Nano API via the Prompt API to convert this text description into MermaidJS diagram code.
It also allows users to directly edit the generated MermaidJS code, see a live preview, and utilise an AI-powered “Fix Diagram” feature if the code has errors.
Finally, the generated diagram can be inserted as a PNG image into their Google Workspace file.
Nano Prompt API Demo
This example illustrates how the Prompt API can be used for practical tasks within a Google Workspace environment.
Under the Bonnet: Utilising the Chrome Gemini Nano Prompt API for Text
The Add-on interacts with Gemini Nano via client-side JavaScript using the LanguageModel object in the Sidebar.html file. I should also highlight that all of the Sidebar.html code was written by the Gemini 2.5 Pro model in gemini.google.com, with my guidance which included providing the appropriate developer documentation and this explainer for the Prompt API.
The Add-on’s core logic for text-to-diagram generation includes:
Session Creation and Prompt Design for Gemini Nano:A LanguageModel session is created using LanguageModel.create().
Generating Diagrams from Text: The user’s natural language description is sent to the AI via session.prompt(textDescription).
AI-Powered Code Fixing: If the generated or manually entered MermaidJS code has errors, the faulty code along with the error message is sent back to the model for attempted correction.
Given that Gemini Nano is, as its name suggests, a smaller LLM, careful prompt design is key to achieving optimal results. In this demonstrator Add-on, for instance, the initialPrompts (system prompt) play a crucial role. It not only instructs the AI to act as a MermaidJS expert and to output onlyraw MermaidJS markdown, but it also includes two explicit examples of MermaidJS code within those instructions.
Providing such “few-shot” examples within the system prompt was found to significantly improve the reliability and accuracy of the generated diagram code from text descriptions. This technique helps guide the smaller model effectively.
Navigating Experimental Waters: Important Considerations (and Reassurances)
It’s important to reiterate that the majority of AI APIs are still experimental. Functionality can change, and specific Chrome versions and flags are often required. I recommend referring to official Chrome AI Documentation and Joining the Early Preview Program for the latest details and updates.
Before you go updating your popular production Google Workspace Add-ons developers should be aware of the current system prerequisites. As of this writing, these include:
Operating System: Windows 10 or 11; macOS 13+ (Ventura and onwards); or Linux.
Storage: At least 22 GB of free space on the volume that contains your Chrome profile is necessary for the model download.
GPU: A dedicated GPU with strictly more than 4 GB of VRAM is often a requirement for performant on-device model execution.
Currently, APIs backed by Gemini Nano do not yet support Chrome for Android, iOS, or ChromeOS. For Workspace Add-on developers, the lack of ChromeOS support is a significant consideration.
However, Google announced at I/O 2025 in the ‘Practical built-in AI with Gemini Nano in Chrome’ session that the text-only Prompt API, powered by Gemini Nano, is generally available for Chrome Extensions starting in Chrome 138. While general web page use of the Prompt API remains experimental this move hopefully signals a clear trajectory from experiment to production-ready capabilities.
Bridging the Gap: The Hybrid SDK
To address device compatibility across the ecosystem, Google has announced a Hybrid SDK. This upcoming extension to the Firebase Web SDK aims to use built-in APIs locally when available and fall back to server-side Gemini otherwise, with a developer preview planned (see https://goo.gle/hybrid-sdk-developer-preview for more information). This initiative should provide a more consistent development experience and wider reach for AI-powered features.
A Glimpse into the Future: Empowering Workspace Innovation
On-device AI opens new opportunities for privacy-centric, responsive, and cost-effective Add-on features. While the demonstrator Add-on focuses on text generation, the Prompt API and the broader suite of on-device AI tools in Chrome offer much more for developers to explore
Focusing on Unique Value for Workspace Add-ons
It’s important for developers to consider how these on-device AI capabilities—be it advanced text processing or new multimodal interactions which support audio and image inputs from Chrome 138 Canary—can be used to extend and enhance user experience in novel ways, rather than replicating core Gemini for Google Workspace features. The power lies in creating unique, value-added functionalities that complement native Workspace features.
Explore, Experiment, and Provide Feedback!
This journey into on-device AI is a collaborative one and Google Workspace developers have an opportunity to help shape on-device AI.
Try It Out: Follow setup instructions to experience on-device AI with the demo, and consider exploring multimodal capabilities for your own projects by referring to the latest Early Preview Program updates.
Provide Feedback: Share your experiences either about the example add-on or through the Early Preview Program.
I hope you have as much fun working with these APIs as I have and look forward to hearing how you get on. Happy Scripting!
Following the improvements made to tables in Google Sheets in March and April, we’re excited to introduce API support for tables. Now, users will be able to take basic actions to create and modify tables via the Sheets API.
Following enhancements to Google Sheets tables last year, Google recently highlighted an update for developers: the Sheets API now supports tables. This is great news for the Google Workspace developer community, as it allows for direct programmatic creation and modification of tables, moving beyond previous reliance on workarounds.
For a while, developers have found clever ways to interact with table-like structures, such as Kanshi Tanaike’s notable 2024 solution using a mix of Apps Script and Sheet formulas. While these methods were very clever, the new, direct API support offers a more robust and straightforward way for interacting with tables in Google Sheets. For Google Apps Script users, for now this will require using the Google Sheets Advanced Service to call these new table methods, as direct integration into the SpreadsheetApp service hasn’t been announced at this time.
Key API Capabilities for Tables:
The Sheets API now lets developers:
Add Tables: Create tables with defined names, ranges, and specific column properties (like ‘PERCENT‘ or ‘DROPDOWN‘ with validation).
Update Tables: Modify table size (add/remove rows/columns), and toggle table footers. The API also provides methods like InsertRangeRequest and DeleteRangeRequest for more granular control.
Append Values: Easily add new rows to the end of a table using AppendCellsRequest, which intelligently handles existing data and footers.
Delete Tables: Remove entire tables and their content (DeleteTableRequest) or just the formatting while retaining data (DeleteBandingRequest).
Utilise Column Types: Work with various column types including numeric, date, dropdown, smart chip, and checkbox.
