brew-ai Overview
brew-ai is a conversational assistant docked inside the model builder. The legacy application had no AI assistant at all — brew-ai is new capability layered directly onto the canvas, so you can ask questions about the workflow you’re building, run functionals, schedule tasks, and get code help without leaving Build.
The Chat Panel
Section titled “The Chat Panel”The chat panel docks alongside the canvas and streams responses token-by-token over SSE as the assistant replies, rather than waiting for a full response before showing anything. While the assistant is working, a “thinking chain” shows the server-side tool steps it’s running — reading the model, inspecting a functional, sampling output rows, and so on — each with a live elapsed-time counter while it’s in progress and a done/error state once it settles. Assistant replies render as GFM markdown, so tables, code blocks, and formatted lists all display properly. You write your side of the conversation in a rich-text composer (built on TipTap) that supports @-mentioning model inputs, outputs, and components directly in your message.
Availability
Section titled “Availability”The chat panel is feature-flagged at the system level, and individual capabilities can be marked experimental — visible only to users who’ve opted into experimental features. If you don’t see the assistant in Build, or a capability described here isn’t showing up, check with your administrator about your deployment’s configuration. Responses are always generated by the real AI backend in normal use; a mock response mode exists only for offline frontend development and isn’t something you’ll encounter in a deployed environment.
Commands & Mentions
Section titled “Commands & Mentions”Beyond free-form chat, brew-ai supports slash commands like /explain, /schedule, /find, and /execute-functional, plus @-mentions that reference model inputs, outputs, and components — hovering a mention highlights the matching component on the canvas. See Commands & Mentions for detail.
Running Functionals in Chat
Section titled “Running Functionals in Chat”Ask brew-ai to run a functional and it walks you through a picker, an editable run card for scalar inputs and CSV uploads, and an async run — then narrates the results back to you once they’re in. See Running Functionals in Chat for detail.
Scheduling Tasks via Chat
Section titled “Scheduling Tasks via Chat”Ask brew-ai to schedule a workflow and it proposes an editable task card — name, cron schedule with live validation, and email-on-error — before creating the task. See Scheduling Tasks via Chat for detail.
Code Copilot
Section titled “Code Copilot”For Python, SQL, R, and JS code functionals, brew-ai can read your live code, LSP diagnostics, and typed inputs/outputs together, then propose edits as an inline diff you can apply or reject. See Code Copilot for detail.