Hey everyone 
Over the past few weeks, I’ve been working on creating three fairly advanced Bubble plugins, and I thought it made sense to showcase them all in a single post, even though each one has its own dedicated thread.
I’ve been involved in software and web development for most of my life really, and certainly within the Bubble space, I’ve built apps, helped other developers, created custom plugins, and of course continue to support my own plugins.
Rather than building yet another set of standard plugins, I wanted to create a set of tools that can extend what Bubble can do through its native features.
The Bubble marketplace has grown massively over the years compared to what I remember it being like in the early days. There are so many plugins available that finding the right one can be a pain. It’s like navigating a minefield and you never really know which ones are good, bad or secure.
Because of that, each of these plugins was designed around a very specific use case, with the goal of taking existing Bubble functionality and kind of turning it into something a bit more AI-native.
These plugins also sit a little deeper than your typical “drag and drop and you’re done” type of plugin. They’ve been designed to work more closely with how Bubble apps are structured, which means there is a small amount of setup involved at the start. That’s simply down to how they function and the level of capability they unlock, rather than any limitation.
Once configured, they become powerful building blocks for AI-driven functionality inside your app.
If you’re building AI-powered apps in Bubble, these are arguably three of the most useful systems you can have inside your application, especially if you’re going all-in on AI functionality rather than simply attaching a few API calls onto the side of an existing product.
So… what exactly are these plugins?
Each plugin focuses on a core area of AI functionality inside Bubble.
Bubble RAG AI
This turns your Bubble database into a vector storage system, allowing you to build AI-powered question and answer features directly on top of your existing data.
In most cases, doing this properly in Bubble usually means relying on external vector databases like Pinecone or similar services, which adds extra cost and complexity. This plugin removes that requirement entirely by keeping everything inside your own Bubble database.
Features:
- Upload and query plain text, PDFs, or images (images are processed using OpenAI’s vision model so they become searchable)
- Streaming and non-streaming response modes
- Source attribution returned with every answer
- Multi-turn conversations with automatic query rewriting for better context handling
- Per-user data isolation via namespace support
- Filtering by source type during retrieval
- Fully configurable retrieval settings (chunk size, overlap, top K, similarity threshold, temperature, system prompt, embedding model)
- Token usage tracking on every request
- Debug mode with detailed logs on both client and server
- 3 client-side actions and 3 server-side actions for flexible workflow design
Bubble Data AI
This turns your database records into live AI context, letting you run analysis, Q&A, and content generation directly on top of your existing Bubble data without restructuring anything.
Normally, this kind of setup requires a lot of custom logic around data formatting, retrieval, and consistency before AI can reliably work with it. This plugin handles that layer for you so you can focus on using the output rather than building the pipeline.
Features:
- Seven built-in analysis types:
- Dataset insights (trends, metrics, recommendations)
- Record classifier (tags, categories, confidence scoring)
- Priority ranker (importance scoring and ordering)
- Anomaly detection (outliers and unusual records)
- Data validation (missing fields, inconsistencies, fixes)
- Sentiment analysis (emotion, intensity, sentiment per record)
- Theme extraction (recurring topics with examples)
- Ask questions in plain English with structured answers and record references
- Generate content per record, optionally writing results back into your database
- Works with OpenAI or Gemini using your own API key
- Multi-type dataset support in a single run
- Relationship traversal up to 3 levels deep
- Date filtering and language flexibility
- Smart caching with staleness detection
- Real-time token usage tracking
- Debug mode for troubleshooting
Bubble Agentics AI
This turns your backend workflows into tool calls (also know as function calls), allowing an AI agent to take actions inside your Bubble app rather than just generate text.
Building this kind of agentic behaviour from scratch usually means implementing your own tool-calling loop, managing retries, handling failures, and wiring everything together manually. This plugin abstracts all of that so you can focus on defining what your app can do, not how the agent executes it.
Features:
- Multi-provider support (OpenAI, Anthropic, Gemini)
- Automatic tool discovery from Backend Workflows using naming conventions
- Full tool-calling loop (decide → call tool → read result → respond or continue)
- Streaming responses for OpenAI, non-streaming for other providers
- Extended reasoning support where available
- Safety controls for tool loop limits and retries
- Two execution modes (user-level or admin-level authentication)
- Server-side execution support (workflows, scheduled runs, webhooks)
- Conversation management (stop, clear, load history)
- Fully structured chat output (no JSON parsing required)
- Token usage tracking per turn and per session
- Configurable system prompt, max turns, retry logic, and tool selection
- Debug mode with full logs
So that’s it.
The goal here is not just to showcase these plugins, but also to push Bubble a bit further in terms of what’s possible with AI inside the platform through use of plugins. Hopefully they’re useful if you’re building anything in that space, or at least give you some ideas on how you might approach it differently.
Regards,
Paul