Agent Quickstart

WeightsLab ships with a natural-language agent that can sort/tag/discard data, answer questions about your model, freeze or reset layers, generate experiment reports, and much more, all backed by a local OpenCode server. This page is the fastest path from “just installed WeightsLab” to “asking the agent questions about a live run.”

Warning

Unstable, in active development

The agent is experimental: behaviour and answer quality vary with the model provider you connect. Check what it did before relying on it, especially for anything that changes data or the model, everything it can do is also reachable by hand. See Experiment Agent Assistant for the full reference.

What you need

  • WeightsLab installed (pip install weightslab). That’s the only install step: WeightsLab provisions the OpenCode binary itself, on first use, into a per-user cache, no Node.js and no manual ``npm``/``opencode`` install required.

  • One set of credentials for a model provider: an OpenRouter API key, an Anthropic key, or a local Ollama install. Pick whichever you already have.

Step 1, initialize the agent once

The agent’s provider and credentials live entirely inside OpenCode, never in WeightsLab itself. The one-liner below provisions the OpenCode binary (if it isn’t already) and then signs you in, do this once per machine:

weightslab agent init

Follow the prompts to sign in to OpenRouter, Anthropic, or point it at a local Ollama endpoint. Equivalent alternatives:

  • opencode auth login, if you prefer to drive OpenCode directly (WeightsLab installs the binary either way).

  • The login modal on the Weights Studio landing page, no terminal required.

  • weightslab agent init --provision-only, headless/CI: just install the binary, skip the interactive sign-in.

Note

You can skip this step and start straight away, if no credential is found, WeightsLab logs an info line (“OpenCode is installed, but the agent is not initialized yet, run weightslab agent init”) and keeps running. The assistant is optional; nothing else is blocked.

Step 2, start an experiment

Use a bundled example so there is something live to talk to:

weightslab start example --cls

Then, in another terminal, start Weights Studio:

weightslab start

Open the printed URL. WeightsLab starts (or reuses) a local opencode serve process for you the first time the agent is used, nothing to run by hand.

Step 3, initialize the agent

Two equivalent ways to connect, pick whichever surface you’re already in:

Surface

How to init

Weights Studio (UI)

Type /init into the agent chat bar, then pick a model from the list. The placeholder text switches to a ready-to-use example query once connected.

CLI (weightslab cli)

Run agent init [--model openrouter/anthropic/claude-opus-4.6], or just agent status first to check what’s already configured.

From here on, both surfaces talk to the same OpenCode server and share the same model choice.

Step 4, ask it something

Plain English, no special syntax:

Tag train samples with loss > 1.5 as hard_examples
Which layers are currently frozen?
Generate an experiment report on train_loss and val_loss

Tip

Before an experiment is even running, the Weights Studio landing page has its own agent chat integrated that needs no backend at all, ask it to scaffold a training script or wire wl.serve() into an existing one. See Weights Studio UI.

Where to go next