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

  • Experiment Agent Assistant — the full command list, safeguards, configuration (OpenRouter/Ollama), and the /loop background-job surface.

  • Weights Studio UI — the docked agent bar and Agent Window inside the studio UI.

  • Experiment Reports — generating reports from the agent, the CLI, or Python directly.