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.
This framework integrates Google Apps Script web apps into a standard website
Integrating a Google Apps Script web app into a public website offers powerful possibilities, but developers can often hit a wall of limitations. Serving from a custom domain, managing multiple Google account logins, preventing security vulnerabilities like clickjacking, and providing a smooth user experience are significant hurdles.
A new open-source framework from developer Zig Mandel provides a comprehensive solution, offering a flexible framework for embedding Apps Script web apps securely and professionally. At its core, the framework uses a parent website to host the Apps Script in an iframe, using a secure postMessage protocol for communication between the parent website and your web app. A key innovation is its ability to dynamically and securely load different script deployments using a cryptographic signature (org/sig) system, preventing unauthorised use while enabling advanced features like centralised logging and analytics integration.
What This Framework Solves
This solution is more than just an iframe wrapper; it’s a complete integration kit designed to enhance your Apps Script web app to a professional standard. It addresses several key pain points:
Custom Domain Serving: Serve your app from your own domain, not the standard script.google.com URL.
Secure Embedding: A communication protocol and domain validation prevent your app from being embedded on unauthorized sites.
Multi-Account & Workspace Compatibility: The framework is built to handle complexities that arise when users are logged into multiple Google accounts or are using a Google Workspace account.
Seamless User Experience: The architecture eliminates the jarring “flash” of a new page loading within the iframe, creating a smooth transition for single-page application (MPA) style web apps.
Analytics & Logging Integration: Send events from your Apps Script directly to the parent website’s Google Tag Manager instance and forward client-side logs to a centralized location like Google Cloud Logging.
Summary
In short, this framework hopefully unlocks a better way to implement public-facing Google Apps Script for web applications. For developers it provides the tools to overcome common embedding hurdles, moving beyond simple scripts to create secure, professional, and seamlessly integrated user experiences. To explore the code and get started on your own integration, check out the repository on GitHub.
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!
Exploring Agent2Agent (A2A) protocol implementation in Google Apps Script seamlessly allows AI agents to access Google Workspace data and functions. This could enable complex workflows and automation, overcoming platform silos for integrated AI applications.
The buzz around AI and collaborative agent systems is growing, and with it, the need for standardized communication. The recently introduced Agent2Agent (A2A) protocol aims to enable exactly that – allowing different AI agents, regardless of their underlying framework, to discover each other’s capabilities and work together. But how can the Google Workspace developer community tap into this exciting new frontier?
Enter prolific Apps Script contributor Kanshi Tanaike, who has once again provided a valuable resource for the community. Kanshi has published a detailed guide and a Google Apps Script library demonstrating how to build an Agent2Agent (A2A) server using Google Apps Script.
Why is this significant?
Kanshi’s work explores the feasibility of implementing a core A2A server component directly within the Google Workspace ecosystem. As Kanshi notes, “Such an implementation could seamlessly allow AI agents to securely access and utilize data and functionalities across Google services like Docs, Sheets, and Gmail via a standardized protocol.”. This opens up possibilities for:
Integrating AI agents with your Google Workspace data: Imagine AI agents that can directly interact with your Sheets, Docs, Drive files, or Gmail.
Building sophisticated AI-powered workflows: Automate complex tasks that span across different services, orchestrated by communicating AI agents.
Kanshi’s guide walks through the setup and implementing functions that allow AI agents to interact with Google Workspace services like Drive or external APIs via UrlFetchApp. This work significantly lowers the barrier to entry for Google Workspace developers looking to experiment with and implement A2A-compatible agents and as such I highly encourage you to check out the linked source post for the complete guide, code, and library details.
In this guide, I’ll show you how to build a data-driven organizational chart based on Google Sheets data, using Apps Script and the OrgChart JavaScript library.
Recently, I used Gemini Deep Reacher to “find the most popular or useful Google Apps Script tutorials?”. There was no mention of AppsScriptPulse (rude, but if you are curious, here is the report :), but it did introduce me to the work of Joseph Petty on the GreenFlux Blog.
One tutorial, in particular, stood out for its practical application for many organisations: “Building A Data-Driven Organisational Chart In Apps Script”.
Creating diagrams, particularly organisational charts, often requires laborious design work and manual revisions. Petty’s tutorial directly addresses this common frustration by demonstrating how Google Sheets, Apps Script, and the OrgChart JavaScript library can produce dynamic, data-driven organisational charts that automatically stay current.
The Core Idea: From Spreadsheet Data to Visual Chart
The beauty of this approach lies in its simplicity and efficiency. Most organisations already maintain employee data, including supervisor information, in spreadsheets or databases. Petty’s method cleverly taps into this existing data source.
The tutorial guides you through several key steps:
Foundation Setup: Deploy a basic Apps Script web app, creating an index.html for the frontend and Code.gs for server-side logic (including a doGet() function).
OrgChart Library Integration: Introduce the open-source OrgChart JavaScript library, which uses a nested JSON structure to define the visual hierarchy.
Data Transformation: Write an Apps Script function (getEmployeeData) to pull data from Google Sheets (employee IDs, names, titles, supervisor IDs) and convert it into the required nested JSON format for the chart.
Dynamic Connection: Pass the generated JSON data from Code.gs to the OrgChart library in index.html using google.script.run.withSuccessHandler() to render the chart.
The Result? An Always Up-to-Date Org Chart
By following these steps, you can create an organisational chart that’s directly linked to your Google Sheet. When an employee changes roles, or a new team member joins, simply update the spreadsheet, and your web app will reflect these changes automatically. No more manual graphic updates!
For the complete code and detailed setup instructions, please follow the link to the original tutorial on GreenFlux Blog.
[Update 2025-05-16: A great tip from Pablo Felip Monferrer: “might be worth mentioning that the good old Sheets has a native organizational chart that can be easily published, too (hint: employee names in a third column can be displayed as tooltips)”.
Amit is a Google Workspace developer, the founder of Digital Inspiration, and a Google Developer Expert. He has published multiple Google Workspace Add-ons to the Workspace Marketplace and they have a combined 46 million+ installs.
Looking to create successful Google Workspace Add-ons? Chanel Greco (Google Workspace DevRel) recently interviewed Amit Agarwal (Founder, Digital Inspiration), who shared valuable insights for aspiring developers.
Amit Agarwal highlighted several critical areas for add-on development success:
Solve Real Problems with Great UX: Focus on addressing specific user needs with a clean, intuitive interface. A compelling first-run experience is key.
Build Trust with Documentation: Provide clear, comprehensive documentation and tutorials. A detailed privacy policy is crucial for user trust and smoother Google reviews. Prompt communication with Google during review is also important.
Smart Business & Future Focus: A freemium model can drive adoption. Keep an eye on future trends like AI integration to enhance your add-on.
Engage the Community: Participate in the Google Developer Community and programs like Google Developer Experts to learn and grow.
To find out more about these areas and more, check out the full interview.
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.
This is a sample Google Apps Script designed to replace all instances of specific text within a Google Slides presentation, while simultaneously applying a desired text style. The built-in Presentation.replaceAllText() method within the Google Slides service is limited; it efficiently replaces text strings but lacks the functionality to modify text formatting during the replacement process. This limitation poses a challenge when aiming for styled text replacements. This report presents a detailed script solution that overcomes this constraint.
Have you ever needed to replace text across your Google Slides presentation but also wanted to apply specific formatting to the new text at the same time? The standard replaceAllText() method in Apps Script is handy for bulk text replacement, but it falls short when you need to control the styling – like font, size, or colour – during the replacement process.
Community contributor Kanshi Tanaike has developed a clever solution to overcome this limitation. Tanaike has shared a Google Apps Script function that not only finds and replaces text throughout all elements in your slides (including shapes, tables, and grouped objects) but also applies your desired text styles simultaneously.
The script works by iterating through the elements on each slide. When it finds the text you want to replace, it uses TextRange methods to perform the replacement and apply the specified formatting attributes, such as font family, size, colour, bold, italics, underline, and more.
This approach provides significantly more control than the built-in method, allowing you to ensure that automatically replaced text matches the exact styling you need for visually consistent and polished presentations. Tanaike’s post includes the full script, configuration details for specifying the text and styles, and sample slides showing the results.
I was thrilled to be invited by Chanel Greco (Google Workspace Developer Advocate) to join Vinay Vyas (Software Engineer, Google) and Steve Bazyl (Developer Program Engineer, Google) for a Developer Spotlight session recorded at Google Cloud Next ‘25.
We explored the exciting evolution of Google Workspace development, focusing on the tools and technologies shaping the future of automation and AI integration.
What We Covered:
The Rise of Flows for Workspace Automation: We kicked things off discussing the potential of Flows, a powerful new tool Google had just announced which is designed to simplify task automation in Google Workspace. We explored how developers can tap into its potential by building custom actions, including integrating with Vertex AI [01:17].
Extending Capabilities with Apps Script: Your existing Apps Script skills remain crucial! We discussed how they empower developers to extend Flows capabilities as the product evolves [02:08], offering significant opportunities within this user-centric automation tool [01:52].
Workspace as an Integrated Platform: Learn about the ongoing efforts to enhance connectivity with third-party services [02:35] and how add-ons (including Chat apps deployed as add-ons [03:32]) are key to keeping users productive within the Workspace context [02:52].
The Gemini Effect on Development: Hear our experiences with Gemini 2.5 and its significant impact on code development, particularly for Apps Script [05:29]. We discuss its impressive ability to generate substantial, high-quality code for complex tasks [06:35].
AI Agents & The Future: We explored the concept of AI Agents powered by Gemini within Google Apps Script for automating tasks using natural language and discussed the exciting prospects for deeper Gemini integration directly within Apps Script [14:41]. I checky asked Steve if there was going to be a Gemini Advanced Service for Apps Script (you’ll have to watch the video to find out the answer :).
Why Watch?
If you are interested in understanding the future direction of Google Workspace development, this discussion hopefully provides some practical perspectives and explores exciting possibilities.
