Ambient "operator recommender" — predictive next-operator suggestions on the canvas

[gupta-sahil01]

The idea in one line

As a user builds a workflow, show 1–3 faded "ghost" operators on the output port of the operator they just added — ranked suggestions for what likely comes next, one click to insert.

How it differs from the existing chatbot agent

The current chatbot is on-demand and conversational: the user describes a goal, the agent generates a workflow. That's powerful, but it assumes the user already knows what to ask for. This feature is ambient and predictive: suggestions surface automatically as part of normal canvas building, with no prompt. It's aimed at the moment where a user has some data on the canvas but doesn't know which operator unlocks the next step. Think Copilot ghost-text, but for the dataflow graph rather than a chat box. The two are complementary — I'd want to reuse the chatbot's LLM plumbing rather than build a parallel stack.

Questions for the maintainers

Existing work: Is anyone already working on operator suggestions / autocomplete on the canvas? I'd rather extend than collide.
Mergeability: If this turns out well, would the team be open to a PR? What would make it acceptable (config flags, opt-in, evaluation, etc.)?

Quick visuals:

The left panel is Texera as it exists today. The user either drags operators by hand from a side panel, or types a description into the chatbot, which constructs a workflow and offers to apply it. Both modes assume the user already knows what to ask for.

The right panel is my idea. The user has only built two operators (CSV Reader → Filter). The pulsing teal port on Filter's output is where your feature lives — three faded "ghost" operators appear automatically, ranked by how well they fit the data. Hovering shows the rationale. Clicking a ghost solidifies it into a real operator on the canvas. No chat needed.