The AI assistant

SciStudio has an AI assistant built in. There are two ways it shows up: the chat, where you talk to an assistant about your project, and the AI Agent block, where you put an AI step inside a workflow. Most users lean on the chat heavily — it is the fastest way to get things done without memorizing the API.

Before you start: install a provider

SciStudio does not ship its own model. It drives one of two AI providers, and you pick which one to use:

  • Claude Code (Anthropic)
  • Codex (OpenAI)

Both run as a small command-line tool (a "CLI") on your computer that SciStudio talks to. You only need one to get going, but you can install both and choose per chat or per AI Agent block. Install whichever you have an account for:

  1. Open the provider's official CLI install guide to get its install command:
  2. Claude Code — https://code.claude.com/docs/en/quickstart#step-1-install-claude-code
  3. Codex — https://developers.openai.com/codex/quickstart?setup=cli
  4. Copy the install command shown there.
  5. Paste it into a terminal and run it:
  6. macOS / Linux: open Terminal and paste the command.
  7. Windows: open PowerShell and paste the command.

(SciStudio also has an embedded terminal you can use for this.) 4. Sign in the first time, following the tool's own prompts (it opens a login in your browser).

Once a provider's CLI is installed and signed in, SciStudio detects it and the chat and the AI Agent block can use it. If the assistant says no provider is available, it means neither CLI is installed yet — come back here and install one.

The chat assistant

Open the AI chat panel and describe what you want in plain language. The assistant works inside your project — it can see your workflows, your data, your blocks, and your run results — so it acts, not just advises. What it is good for:

  • Answering SciStudio questions. "What block loads a CSV?" "Why did my run fail?" "What types can connect to this port?" It knows the app and your project.
  • Building workflows. Describe a pipeline — "load these files, baseline- correct, find peaks, save a table" — and it assembles and wires the blocks for you, with valid types and parameters.
  • Writing blocks and plots. "Write a block that normalizes each spectrum to its max" or "plot the peak table as a bar chart." It writes the code against the public API and the canonical imports, so you do not have to (see writing-blocks.md, writing-plots.md).
  • Checking your data. Ask it to look at a port, sanity-check a table, find outliers, or confirm two batches line up — it inspects bounded previews without loading everything into memory.
  • Iterating for you. "Try sigma from 1 to 5 and tell me which gives the cleanest baseline." It can run the workflow, read the results, adjust a parameter, and run again — the tuning loop you would otherwise do by hand.

You stay in control: you review what it proposes and run it. Think of it as a collaborator who knows the tool and your project, not an autopilot.

The AI Agent block

The chat helps you build a workflow. The AI Agent block puts AI into the workflow as a step that runs every time the pipeline runs. Use it when part of your processing is a judgment task that is hard to write as fixed code — classifying, summarizing, extracting, or inferring something from messy inputs.

Add the AI Agent block from the palette like any other block. Its parameter panel has:

  • User prompt — the task, in plain language.
  • Provider — which assistant runs it (claude-code or codex).
  • Permission mode — Ask (the agent checks with you before sensitive actions) or Bypass (full access, no prompts).
  • Input / output ports — you declare these in the port editor: name each port and give it a type. Inputs are handed to the agent as files; for each output you say where the agent should write its result and what type it is.

At run time the block spawns the agent in a terminal tab, hands it your inputs, and waits until it has produced the declared output files — which SciStudio then loads back as ordinary typed data for the next block. From the workflow's point of view it is just another block with typed ports.

Example: infer a metadata table from raw data

A common, genuinely useful job: you have a pile of raw data files and you want a tidy metadata table — one row per sample, with columns like sample id, experimental condition, and instrument — inferred from the files themselves. That is awkward to write as fixed code (every dataset is messy in its own way) and a perfect fit for an AI Agent block.

Set it up like this:

  • Input port data_files, type Artifact — wire your raw files in (e.g. from a Load block producing file artifacts).
  • Output port metadata, type DataFrame, with an expected path such as ./metadata.csv.
  • User prompt, something like:

You are given several experimental data files. Inspect each one and build a metadata table with one row per sample and the columns: sample, condition, instrument, source_file. Infer values from the file contents and names; leave a cell blank if you cannot determine it. Write the table to ./metadata.csv.

When the workflow runs, the agent reads each input file, works out the metadata, and writes metadata.csv. SciStudio loads that file back as a DataFrame on the metadata port, ready to drive the rest of the pipeline — join it to your measurements, filter by condition, group by sample. The AI did the messy inference; the workflow stays typed and reproducible around it.

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