Why use a dedicated app when you can simply ask Gemini to write and run the Python code for you? A look at the power of Google Apps Script and GenAI
For many Google Workspace developers, handling complex file formats or performing advanced data analysis has traditionally meant navigating the limitations of Apps Script’s built-in services. We have previously featured solutions for these challenges on Pulse, such as merging PDFs and converting pages to images or using the PDFApp library to “cook” documents. While effective, these methods often rely on loading external JavaScript libraries like pdf-lib, which can be complex to manage and subject to the script’s memory and execution limits.
While users of Gemini for Google Workspace may already be familiar with its ability to summarise documents or analyse data in the side panel, those features are actually powered by the same “Code Execution” technology under the hood. The real opportunity for developers lies in using this same engine within Apps Script to build custom, programmatic workflows that go far beyond standard chat interactions.
A recent project by Stéphane Giron highlights this path. By leveraging the Code Execution capability of the Gemini API, it is possible to offload intricate document and data manipulation to a secure Python sandbox, returning the results directly to Google Drive.
Moving beyond static logic
The traditional approach to automation involves writing specific code for every anticipated action. The shift here is that Gemini Code Execution does not rely on a pre-defined set of functions. Instead, when provided with a file and a natural language instruction, the model generates the necessary Python logic on the fly. Because the execution environment includes robust libraries for binary file handling and data analysis, the model can perform varied tasks without the developer needing to hardcode each individual routine. Notably, the model can learn iteratively which means if the generated code fails, it can refine and retry the script up to five times until it reaches a successful output.
While basic data analysis is now a standard part of Gemini for Workspace, having direct access to the library list in the Gemini sandbox opens up additional specialised, developer-focused avenues:
Dynamic Document Generation: Using python-docx and python-pptx, you can programmatically generate high-fidelity Office documents or presentations based on data from Google Workspace, bridging the gap between ecosystems without manual copy-pasting. [Here is a container bound script based on Stéphane code for Google Docs that generates a summary PowerPoint file]
Programmatic Image Inspection: Using Gemini 3 Flash, you can build tools that inspect images at a granular level. For example, a script could process a batch of site inspection photos, using Python code to “zoom and inspect” specific equipment labels or gauges, and then log those values directly into a database.
The mechanics and constraints
The bridge between Google Drive and this dynamic execution environment follows a straightforward pattern:
File Preparation: The script retrieves the target file from Drive and converts the blob into a format compatible with the Gemini API.
Instruction & Execution: Along with the file, a prompt is sent describing the desired outcome.
The Sandbox: Gemini writes and runs the Python code required to fulfil the request.
Completion: Apps Script receives the modified file or data and saves it back to the user’s Drive.
However, there are important boundaries to consider. The execution environment is strictly Python-based and has a maximum runtime of 30 seconds. Furthermore, developers should be mindful of the billing model: you are billed for “intermediate tokens,” which include the generated code and the results of the execution, before the final summary is produced.
Get started
For those interested in the implementation details, Stéphane has shared a repository containing the Apps Script logic and the specific configuration needed to enable the code_execution tool.
With the announcement of Google Workspace Flows at Google Cloud Next 2025, Workspace developers and power users have been eager to see how this new platform integrates with the ecosystem we know and love. Currently available in the Gemini for Workspace Alpha programme, Workspace Flows promises to bridge the gap between simple tasks and complex, AI-driven processes.
In a fantastic new three-part series, Google Workspace expert Stéphane Giron takes us on a complete tour of the platform. From the initial interface to complex logic handling, this series is the perfect primer for anyone looking to automate their organisation’s daily grind.
Here is a breakdown of the series:
Part 1: A First Look The introductory article sets the stage, explaining how to enable Workspace Flows within the Admin console and providing a tour of the dashboard. Stéphane highlights how Flows is built with Gemini at its foundation, allowing users to describe workflows in natural language to generate automations instantly.
Part 2: The Starters (Triggers) In the second instalment, the focus shifts to the engine of every Agent: the Starter. Stéphane provides a deep dive into Application-based starters (reacting to events in Gmail, Drive, Chat, Sheets, Calendar, and Forms) versus Schedule-based starters. He details how granular these triggers can be—such as filtering emails by specific criteria before a flow even begins.
Part 3: Steps and Logic The final piece explores the “Steps”—the actions that happen after the trigger. The article covers:
Core App Steps: Standard actions like sending emails or creating Calendar events.
AI Integration: Using ‘Ask Gemini’ to reason, summarise, or transform content.
Logic: Implementing ‘Check if’ and ‘Decide’ steps to create dynamic paths.
Apps Script Integration
Of particular interest to the AppsScriptPulse community is the mention in Part 3 regarding Custom Steps. Stéphane notes that for ultimate power users, Workspace Flows will offer the ability to create custom steps using Google Apps Script. This feature will unlock the full potential of the ecosystem, allowing developers to build highly specific integrations that aren’t available out of the box.
Automate research with this script. It turns a Google Sheet into an AI that plans, researches, and writes detailed reports on any topic.
The Gemini App (gemini.google.com) has a sophisticated Deep Research agent whose capabilities are incredibly powerful, but they are designed to tackle one complex topic at a time. The manual process of running each query and collating the results can however be very slow.
What if you could deconstruct the capabilities of Deep Research and build a similar agent which is instead orchestrated from a Google Sheet? Thankfully, Google Workspace Developer Expert, Stéphane Giron, has designed and shared an elegant solution that transforms Google Sheets and Apps Script into a powerful, automated, bulk research assistant.
Stéphane’s approach is built on a “Divide and Conquer” strategy, which he outlines in his detailed article on the project.
Instead of just asking a single AI to answer a complex question, this script acts as a manager for a specialised AI team… The process is broken down into three distinct phases:
Phase 1: The Strategist (Plan): A powerful AI model analyses your main query and breaks it down into a logical plan of smaller, essential sub-questions.
Phase 2: The Researcher (Execute): A fast, efficient AI model takes each sub-question and uses targeted Google searches to find factual, concise answers.
Phase 3: The Editor (Synthesise): The strategist AI returns to act as an editor, weaving all the collected research and data into a single, cohesive, and well-written final report.
How the AI Research Team Works
The solution, which is available in full on GitHub, uses a clever combination of different Gemini models and tools, all orchestrated by Google Apps Script:
Input: The user simply lists all their research topics in a Google Sheet (e.g., in column A).
Phase 1 (Plan): The script loops through each topic. For each one, it calls the Gemini 2.5 Pro model, treating it as the “Strategist.” It uses Gemini’s function-calling capability to force the model to output a structured plan of sub-questions needed to answer the main query.
Phase 2 (Execute): The script then takes this list of sub-questions and calls the Gemini 2.5 Flash model for each one, treating it as the fast “Researcher.” This call uses the built-in Google Search tool (grounding) to find up-to-date, factual answers for each specific sub-question.
Phase 3 (Synthesise): Finally, with all the collected research, the script calls Gemini 2.5 Pro one last time. In this “Editor” role, the model receives the original query and all the question/answer pairs, synthesising them into a single, comprehensive report.
Output: The script creates a new Google Doc for this final report and places a link to it directly in the Google Sheet, next to the original topic.
This “AI team” approach is a fascinating pattern we’re beginning to see emerge in the community. It strongly echoes the “AI Scrum Master” concept shared by Jasper Duizendstra at the recent Google Workspace Developer Summit in Paris. Both solutions smartly move away basic prompting and instead orchestrate a team of specialised AIs, leading to a far more robust and scalable workflow.
Stéphane’s script is highly customisable, allowing you to set the number of sub-questions to generate, define the output language, and pass the current date to the models to ensure the research is timely.
This is a fantastic example of how to build sophisticated, autonomous AI agents inside Google Workspace. A big thank you to Stéphane Giron for sharing this project with the community.
Get Started
You can find the complete code, setup instructions, and a deeper dive into the architecture at the links below:
Use Gemini and Imagen 3 on Vertex AI to create the images you envision. Generate tailored images from reference sources with Apps Script.
Have you ever generated an image using Gemini in Google Workspace and wished you could easily tweak or iterate on it? While Gemini for Workspace is great for initial creation, iterating on those images directly isn’t currently straightforward. A recent post by Stéphane Giron highlights a programmatic approach using Google Apps Script, Vertex AI (with Imagen 3 and Gemini models) to achieve the goal of generating image variations based on a source image.
Stéphane’s method uses a source image (which could be one previously generated or found elsewhere) which is provided to the Vertex AI API for Gemini (e.g., gemini-2.0-pro) along with text instructions for the required changes. The Gemini model analyzes the image and the request to generate a new prompt. This new prompt is then used with the Imagen 3 model (e.g., imagen-3.0-generate-001) via Vertex AI to generate the final image variation.
It’s interesting to contrast this with another solution we’ve previously featured from Kanshi Tanaike on ‘Iterative Image Generation with the Gemini API and Google Apps Script‘. While Tanaike’s method uses the Gemini API’s chat history to iteratively build an image from sequential text prompts, Stéphane’s focuses on reinterpreting a source image with specific modifications via a newly generated prompt.
You can check out Stéphane Giron’s source post for the complete code and setup instructions.
Discover how to combine Imagen 3 and Google Sheets for rapid image creation. Automate your workflow and generate visuals in bulk.
Have you ever wished you could create a bunch of images without the hassle of typing in each prompt one by one? Well, Stéphane Giron has shared a clever solution using Google’s Imagen 3, Google Sheets, and Apps Script to streamline this process. While Gemini for Workspace is rolling out to more users and includes Imagen 3 for image creation in apps like Slides and the Gemini side panel, it doesn’t offer an easy way to create images in bulk. That’s where this method comes in. Instead of making images one at a time, this approach allows you to generate multiple images at once, saving you time and effort.
To get started with this method, you’ll need a Google Cloud Project and creating a service account (see the Build an AI slides reviewer with Gemini tutorial for instructions on setting this up). Once that’s set up, you create a Google Sheet with a column for your text prompts, and another for the links to the generated images. The images are automatically saved into a specific folder in your Google Drive.
The real magic happens with Apps Script. It uses Vertex AI to connect with the Imagen 3 model. The script reads each prompt from your sheet, sends it to Imagen 3 to create an image, and then stores the image in Google Drive. It also helpfully adds the image’s link back into the Google Sheet.
Check out the source post for the required code and instructions.
Bluesky is gaining traction this end of 2024, and if you’re on the platform, you need to know your numbers. Want to track your Bluesky stats without the hassle? Google Apps Script and Google Sheets are here to rescue you with a simple, yet powerful solution.
In this blog post, Stéphane Giron provides a guide to tracking Bluesky social media statistics using Google Apps Script and Google Sheets. With the growing popularity of Bluesky it can be useful and interesting to understand the social media dynamic of the platform.
The script he offers tracks metrics such as the number of posts, followers, likes, reposts, and more. Stéphane also details how to install and set up the script, including copying and pasting the provided code, running initialization functions, and scheduling automatic data collection. The result is raw data that users can then use to create graphs and visualize their Bluesky activity over time.
Automate publication of your script from development to production easier.
This post by Stéphane Giron provides a CI/CD solution for Google Apps Script, which makes it easy for you to automate the deployment of Apps Script code from a development version to production. The solution uses the Apps Script API to manage versions and streamline the code copying process.
Stéphane has previously created a backup solution for Google Apps Script code, but it lacked version control and multi-file support. This new CI/CD solution addresses these limitations by using the Apps Script API to handle version management and the publication process.
The post includes the necessary code and configuration for implementing this solution. To use this solution you will need to enable the Apps Script API and as well as setting up your project with a standard Google Cloud Platform (GCP) Project.
As a bonus the solution also allows you the option of restoring previous versions of your deployed script, providing a rollback mechanism in case of errors or issues. This solution is lighter weight than other CI/CD setups featured in Pulse, but is easier to set up, providing a practical approach to implementing CI/CD for Google Apps Script projects and enhancing code management and deployment processes.
If your automations are not being triggered by Google Form submissions, Pablo Felip’s tip is it could be because your Google Workspace Admin has turned on AppSheet Core Security.
Use Google Apps Script to automate actions on Bluesky Social using their API.
I’ve largely given up on X (formerly Twitter) and there are now numerous alternatives including Bluesky Social. If you would like to automate posting and getting messages from Bluesky Stéphane Giron provides a guide on how to communicate with Bluesky Social API using Google Apps Script. It includes detailed steps on how to obtain an app password, authenticate with Bluesky, retrieve messages for a user, and post a new message. The post also includes code snippets for each step, making it easy if you want to modify this integration to your own needs.
Create a Chatbot answering user questions based on your documents. RAG implementation with multiturn using Vertex AI Search and Apps Script
More GenAI, this time from Stéphane Giron looking at how Apps Script can be used to provide the glue for a Google Chat app powered by Vertex AI Search. In this example you can see how unstructured data like PDF documents can easily be uploaded to a Cloud Storage folder, which then become the knowledge base for the Chat app. The post includes a Google Apps Script snippet for sending messages to the Vertex AI Search API as well as instructions on how to create the chatbot, including how to import data into Vertex AI Search and how to integrate the chatbot into Google Chat.
The post is a great summary of what is possible when combining Google Chat and Vertex AI Search. If you are interested in finding out more about what is possible using Vertex AI Search with follow-ups Google provide a comprehensive guide.
