How to Build an AI Chatbot in Bubble.io Without Code
AI + Bubble.io How to Build an AI Chatbot in Bubble.io Without Code A complete guide to building a fully functional AI chatbot inside Bubble.io — covering conversation history, streaming responses, system prompts, and deployment — without writing a single line of backend code. No CodeRequired Multi-turnConversation support 1 DayTo build a working MVP Architecture First How a Bubble.io Chatbot Actually Works Before building, understand the data structure that makes multi-turn conversation possible. An AI chatbot is not a single API call — it is a conversation thread where every message (user and AI) is stored and sent back to the model on each new message. This is what creates the feeling of memory and context. In Bubble.io, you need three data types: 💼 Conversation One record per chat thread. Fields: User (User), Created Date, Title (text), System Prompt (text). 💬 Message One record per message. Fields: Conversation (Conversation), Role (option set: user/assistant), Content (text), Created Date. ⚙️ Option Set: Role Create an Option Set with two options: user and assistant. This maps directly to the OpenAI message format. API Setup Configure the API Connector for Chat The key difference for a chatbot is that you pass the full conversation history — not just the latest message. In the API Connector, set your chat completions call with this body structure. The <messages_array> parameter will be replaced dynamically with your conversation history: { “model”: “gpt-4o-mini”, “messages”: , “max_tokens”: 800 } 📌 You will construct the messages_array dynamically in your workflow using a backend workflow that formats all messages in the conversation as a JSON array before calling the API. Building the UI The Chatbot Interface in Bubble A clean chat UI requires three main elements. 1 Message list (Repeating Group) Create a Repeating Group with data source: Search for Messages with Conversation = Current conversation, sorted by Created Date ascending. For each row, show the message content with left/right alignment based on Role = ‘user’. 2 Input area Add a Multi-line Input at the bottom of the page. Add a Send button that triggers the main chat workflow. Add a keyboard shortcut: when Enter is pressed (and Shift is not held), trigger the same workflow. 3 Typing indicator Add a small animated element (three bouncing dots) that is only visible when a custom state ‘is_loading’ is true. Set this state to true when the API call starts and false when it completes. The Core Workflow Sending a Message and Getting a Reply This is the main workflow that runs every time a user sends a message. 1 Save the user message Create a new Message: Conversation = current_conversation, Role = user, Content = Input’s value. Clear the input element immediately so the user can see the message sent. 2 Set loading state Set custom state is_loading = true. This shows the typing indicator while waiting for the AI response. 3 Build conversation history Trigger a backend workflow that loops through all Messages in the current Conversation (oldest first) and formats them as the OpenAI messages array. Pass this array to the API call. 4 Call the OpenAI API Trigger the API Connector call with the formatted messages array. The model receives full conversation context and generates a contextually appropriate reply. 5 Save the assistant reply Create a new Message: Conversation = current_conversation, Role = assistant, Content = API result’s choices[0].message.content. 6 Clear loading state Set custom state is_loading = false. The repeating group auto-refreshes and displays the new AI message. System Prompts Giving Your Chatbot a Persona and Purpose The system prompt is the most powerful tool for controlling chatbot behaviour. A system prompt is a message with role system that always appears first in the messages array. It defines who the chatbot is, what it knows, how it speaks, and what it will and will not do. What to Include The chatbot’s name and role (“You are Alex, a support agent for Acme SaaS”) Tone and style (“Respond in a friendly, concise tone”) Knowledge scope (“Only answer questions about our product features”) Escalation instructions (“If asked about billing, direct the user to support@acme.com”) Bubble Implementation Store the system prompt in the Conversation data type — one per chatbot instance Prepend it to the messages array before every API call Make it editable in an admin panel so non-technical staff can update it Version it so you can roll back if a change causes unexpected behaviour Want a Custom AI Chatbot Built in Bubble.io? SA Solutions has built production AI chatbots for customer support, onboarding, and in-app assistance. We handle conversation architecture, system prompt engineering, and UI polish. Start the ConversationOur Bubble.io Services
Integrating ChatGPT API into Bubble.io: Step-by-Step
AI + Bubble.io Integrating ChatGPT API into Bubble.io: Step-by-Step A precise, developer-tested walkthrough for connecting OpenAI’s ChatGPT API to your Bubble.io application — covering authentication, request structure, response handling, error management, and cost control. 5 StepsTo a working integration 3 ModelsCovered with tradeoffs 1 HourEstimated setup time Before You Start Prerequisites You need three things before opening your Bubble editor. 🔑 OpenAI API Key Create an account at platform.openai.com, navigate to API Keys, and generate a new secret key. Store it securely — you cannot view it again after creation. 🔌 API Connector Plugin In your Bubble app, go to Plugins and install the ‘API Connector’ by Bubble. It is free and maintained officially. 🗄️ A Data Type for Responses Create a data type (e.g., ‘AI Response’) with at least two fields: prompt (text) and result (text). This lets you store and display AI outputs. Step 1 Configure the OpenAI API in Bubble Navigate to Plugins → API Connector → Add another API. API Name: OpenAI Authentication: Private key in header Header name: Authorization Header value: Bearer YOUR_API_KEY Set Shared headers for all calls to include Content-Type: application/json 📌 Never expose your API key in client-side calls. In Bubble, mark the Authorization header as ‘Private’ to keep it server-side only. Step 2 Define the API Call Click ‘Add another call’ inside the OpenAI API you just created. Call name: Chat CompletionMethod: POSTURL: https://api.openai.com/v1/chat/completions Set Body type to JSON and paste this request body: { “model”: “gpt-4o-mini”, “messages”: [ { “role”: “system”, “content”: “You are a helpful assistant for .” }, { “role”: “user”, “content”: “” } ], “max_tokens”: , “temperature”: 0.7 } 📌 , , and become dynamic Bubble parameters. Set a sensible default like 500 for max_tokens when initialising. Step 3 Initialise the Call and Map the Response Click ‘Initialize call’. Bubble sends a test request and maps every field in the JSON response. After initialisation, Bubble maps fields including: Key Response Fields choices[0].message.content — The AI reply text. This is what you display to users. usage.total_tokens — Total tokens consumed. Use this for cost tracking. id — Unique ID for this completion. Useful for logging. model — Confirms which model version processed the request. Step 4 Wire the Call to a Workflow Add a workflow to a button or event, then call the API and handle the response. 1 Add the API action In your workflow, add action: Plugins → OpenAI – Chat Completion. Fill in app_context with a static description of your app, user_message with the input element’s value, and max_tokens with a number (e.g., 800). 2 Save the result Add action: ‘Create a new AI Response’. Set prompt = Input Element’s value. Set result = Result of step 1’s choices[0].message.content. 3 Display it Bind a multi-line text element to ‘Current Page’s AI Response’s result’ — or use a custom state if you want to show results without database persistence. 4 Handle errors Add a ‘Trigger a custom event’ action after the API call that checks if the result is empty. Show an alert or retry message if the AI response did not return cleanly. Models Choosing the Right GPT Model The model you choose in the request body has a significant impact on cost, speed, and quality. Model Speed Quality Cost Best For gpt-4o-mini Fast Good Low High-frequency tasks: classification, short generation, chat gpt-4o Medium Excellent Medium Complex reasoning, long documents, nuanced writing gpt-4-turbo Medium Excellent Higher Tasks needing very long context windows (128k tokens) Cost Control Managing Token Costs in Bubble API costs accumulate quickly in production. Apply these practices from day one. 💰 Set max_tokens limits Never let users trigger unlimited token requests. Set a max_tokens cap appropriate to the feature — 300 for short summaries, 1000 for longer content — and make it a configurable app setting. 🔁 Cache common responses If multiple users ask the same question, store the AI response in your database and return the cached version instead of calling the API again. A simple exact-match lookup on the prompt field eliminates duplicate spend. 🚦 Rate limit per user Add a constraint in your workflow: check how many AI requests the current user has made today before triggering the API call. Enforce a daily limit to prevent abuse. Want a Production-Ready ChatGPT Integration? SA Solutions builds Bubble.io apps with robust, cost-controlled AI integrations — not just proof-of-concept demos. We handle authentication, error handling, response management, and UI together. Talk to Our TeamSee Our Bubble.io Services
How to Add AI to Your Bubble.io App: A Complete Guide
AI + Bubble.io How to Add AI to Your Bubble.io App: A Complete Guide Your Bubble.io app is live. Now users want it smarter. This complete guide walks through every AI integration option — from text generation to chatbots to predictive features — and exactly how to wire them up. 3 APIsCovered in depth 0 CodeRequired to follow this guide 1 GuideEverything you need The Basics What ‘Adding AI’ Actually Means When founders say they want AI in their app, they usually mean one of three things — and each has a different implementation path. ✍️ Generative AI The app writes, summarises, or creates content — text, images, or structured data — based on user input or stored records. 💬 Conversational AI Users ask questions and receive intelligent answers. Think support chatbots, onboarding assistants, or in-app knowledge bases. 📊 Predictive AI The app scores, classifies, recommends, or forecasts — surfacing insights from the data your users have already generated. Core Setup The API Connector: Your Bridge to Any AI All AI integration in Bubble.io flows through the API Connector plugin. Here is the exact setup for OpenAI. Navigate to Plugins → API Connector in your Bubble editor. Create a new API named OpenAI, set authentication to Private key in header, and add the Authorization header: Authorization: Bearer YOUR_OPENAI_API_KEY Create a new call with method POST and endpoint: https://api.openai.com/v1/chat/completions Set the request body — wrapping user input in angle brackets makes it a Bubble dynamic parameter: { “model”: “gpt-4o-mini”, “messages”: [ { “role”: “system”, “content”: “You are a helpful assistant.” }, { “role”: “user”, “content”: “” } ], “max_tokens”: 500 } 📌 In Bubble workflows, pass the value of any input element or database field into . The API Connector handles the substitution automatically. AI Services Which AI Service Should You Use? 🟢 OpenAI GPT-4o / GPT-4o mini Best for text generation, summarisation, Q&A, classification. GPT-4o mini is fast and affordable for high-frequency tasks. GPT-4o handles complex reasoning. Most Bubble.io developers start here. 🔵 Anthropic Claude Best for longer document analysis, nuanced writing, and instruction-following with fewer errors. Claude’s large context window suits apps that process user-submitted documents or long chat histories. 🟡 Google Gemini Best for multimodal tasks — text plus image in the same request. If your app processes uploaded images alongside text, Gemini is the natural fit. Implementation Step-by-Step: Connecting the API Call to a Workflow Once the API Connector is configured, wiring it to your app takes four steps. 1 Initialise the call Click ‘Initialize call’ in the API Connector. Bubble makes a test request and maps the response fields — including choices[0].message.content, which is the AI’s reply. These mapped fields become available anywhere in your Bubble editor. 2 Create a workflow trigger Add a workflow to any button, input change, or page event. Choose ‘Plugins → YourAPIName – YourCallName’ as the action. Pass the dynamic value into the parameter you defined. 3 Store the response Use a ‘Make changes to a Thing’ or ‘Create a new Thing’ action to save the AI response to your database. This lets you display it, reference it later, or use it in other workflows. 4 Display the result Bind a Text element’s content to the relevant database field or use the workflow result directly in a custom state if you do not need persistence. What to Build AI Features You Can Add Right Now These are the most common AI capabilities founders add to Bubble.io apps — each achievable with the API Connector and a well-designed prompt. Content & Writing Auto-generate product descriptions from structured fields Summarise long user-submitted text into key points Draft email responses based on support tickets Generate personalised onboarding copy for each user Classification & Extraction Classify incoming support tickets by category and urgency Extract structured data from unstructured form responses Tag or label user-generated content automatically Sentiment analysis on customer feedback or reviews Conversational In-app chatbot with knowledge of your product FAQ assistant that answers from your documentation Onboarding guide that responds to user questions AI search that understands natural language queries Recommendation & Personalisation Recommend next steps based on user behaviour Personalise dashboard content per user profile Suggest relevant items from your database Adaptive coaching or learning paths Ready to Add AI to Your Bubble.io App? SA Solutions builds custom Bubble.io applications with AI integration built in from day one. Whether you need a simple text generation feature or a fully conversational AI layer, we handle the architecture. Schedule a Free CallView Our Bubble.io Work