Desktop quickstart

The installable Desktop app is the shortest path to creating, observing, and managing research runs. Its distribution contains Runtime, the Python scientific environment, the control surface, and bundled plugins. You do not need a repository checkout or a manually started local server.

1. Install and launch

Obtain the package for your platform from the project's release channel: a Windows .msi or setup program, a macOS .dmg, or the native Linux package supplied for the release. Launch Scientific Agent after installation.

The first-run assistant covers:

  1. Welcome — enter setup.
  2. Storage — confirm the Scientific Data Location. It defaults to ScientificAgent under your Documents directory and holds durable runs, models, reports, and artifacts.
  3. Provider — configure and test a model service, or select Continue Offline. Offline mode is sufficient for this deterministic example.
  4. Capabilities — inspect discovered plugins, the local executor, and Codex CLI status.
  5. Ready — enter the new-run page.

If data from an older version is detected, a migration page appears first. You can copy, verify, and move it to the recommended location, keep using the old location, or choose another one. Run IDs, events, provenance, and artifact hashes are not rewritten.

2. Complete the first run

On New Research Run, enter:

  • Task title: Toy response quickstart
  • Goal: Reach a measured score of at least 0.90
  • Capabilities: select only toy.evaluate
  • Acceptance criterion: score >= 0.90
  • Budget: Actions 10, Experiments 5, Failures 3
  • Scientific workflow: leave the optional switches off
  • Input artifacts: none

Select Review Research Contract. Confirm that the preview names toy.evaluate, score >= 0.90, and the deterministic local Reasoner, then select Start Research.

The reference run normally makes two probes: the first records score=0.64, and the second records score=0.93. CriteriaEngine then sets the run to SUCCESS. This is a deterministic offline product check, not a claim about real scientific-model performance.

3. Inspect the result

In the Research Control Room:

  • filter the timeline to Evidence and expand both experiments;
  • open State and confirm that Criteria progress is satisfied;
  • open Metrics for the two-point score series;
  • open Budget and confirm two actions and two experiments were used;
  • open Lineage, select observation.json, and inspect its SHA-256, creator, content, and relationships.

At the bottom, ask Why did this run succeed?. Ask returns references to recorded events or metrics but does not modify research state.

4. Configure a real model

Open Settings → Providers:

  • If the official Codex CLI is already authenticated, enable Local Codex login. The app does not copy the Codex credential.
  • Remote services may use OpenAI Responses, OpenAI-compatible Chat Completions, or Anthropic Messages formats.
  • API keys go only to the operating-system credential store. Use Test connection after saving.

Then open Settings → Models:

  • Simple Mode assigns one provider/model to scientific reasoning, code generation, and repair.
  • Turn Simple Mode off to assign each role independently.

Routing changes apply only to new runs. Existing runs retain their creation-time model contract.

Next steps

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