Platform Tour

Documentation, Code, Data Sources, Memories & Skills, Secrets, and Harumi AI Chat, section by section.

Documentation

The Documentation tab holds markdown documents for the project — design notes, business rules, or anything you want alongside the code. Documents are plain .md files committed to the same repository as your code.

Click New doc in the sidebar, give it a name (.md is appended if you leave it off), and start writing. Each document has three view modes:

Read-only rendered markdown, including fenced code blocks and math.

Saving a document commits it to the repository, the same as editing a file in the Code tab.

Code

The Code tab is a browser view of the project's git repository: a file tree on the left, and a viewer/editor on the right.

Clone URL — copy it from the top of the Code tab to git clone the project locally, or use the harumi CLI to bind a local directory to the project.
Download .zip — grab a snapshot of the current branch without cloning.
Branches — the New version button creates a new branch to work on changes without touching main. A run isn't locked to main, though: the toolbar's Run options dialog and harumi run --branch can execute any branch directly, and a scheduled run can target one too. Merging a branch back into main (via git) is only needed to make it the default a plain Run click uses.
Editing a file — select a file, switch to Edit, make your change, and click Commit changes. Edits go straight to the branch you're viewing — there's no separate draft/publish state.

Adding data files

There's no upload button in the Code tab. To add a CSV, Parquet, or other file that should be tracked in git (or to work in your own editor), git push it — either by cloning the repo directly, or with harumi run from the CLI, which pushes your local working tree (including new files) to a scratch branch automatically. To add a data file without committing it to the repository, use the Data Sources tab's Files section instead.

Data Sources

Reached from the project sidebar's Inputs button, this tab has three parts: connecting external databases, a read-only SQL editor, and non-git file uploads.

Data Sources connects a project to an external database so your code can query it directly, without embedding credentials in your repository. Supported types: PostgreSQL, MySQL, SQL Server, and Oracle.

To add one, click Connect new data source and fill in the connection details (host, port, database, username, password). If the database isn't reachable directly, enable Connect through a proxy to route the connection through Harumi's VPN proxy, providing the proxy host/port and mTLS certificates. Click Test connection before saving — Harumi verifies connectivity before persisting the data source.

Once connected, query it two ways:

From your code — the data source is available to your run via its name.
From the SQL Editor in the Data Sources tab — a read-only editor (SELECT/WITH only) for quick exploration.

Files

Files uploaded here aren't tracked in Code (git) — they're stored separately, per project, and appear at inputs/ inside every run's working directory, so main.py can read one with a plain relative path like inputs/model.pkl. Upload by clicking Upload file or by dragging a file onto the section; each file can be downloaded or deleted individually.

A project can hold at most 500 files or 500 MB total. A run refuses to start once uploaded files are over that cap — the UI shows how much of it a project has used, so you can remove files before it becomes a problem.

Memories & Skills

The Memories & Skills shortcut in the project sidebar opens a tab with two kinds of context Harumi AI Chat can draw on for the project — visible and editable by every project member.

Memories

A memory is a short fact or preference the assistant should keep in mind for this project (for example, "Always report revenue in USD"). Type one into the box and click Add.

Each memory has a Retrieval mode:

On demand — only surfaced when the assistant searches memories.
Always — injected into every chat message for this project.

Any memory can be edited or removed from its row afterward.

Skills

A skill is a reusable instruction the assistant loads on demand, stored as a skills/<name>/SKILL.md file in the project's repository. This tab only lists them — click one to open its SKILL.md in the Code tab, where it's actually edited.

New skill creates a folder and starter SKILL.md in the repository under a name you choose, then opens it in the Code tab for editing.

Secrets

Settings → Secrets stores environment variables for your project's code — API keys, credentials, or any other config value you don't want committed to the repository in plain text. Values are encrypted at rest and are never shown again after saving; you can only replace or delete them, not view them.

Click Add secret, provide a name and value, and it becomes available as an environment variable the next time your project runs.

Harumi AI Chat

Chat opens Harumi AI Chat, available as a drawer from any tab or as its own full page. It has two modes:

Agent — full agentic mode. Routes optimization requests to a dedicated solver that builds a model and runs code, and can also answer general questions.
Ask — conversational mode. Searches your project's files and can run code to answer questions, but does not route to the optimization solver.

In either mode, the chat can read and write files in your project's repository and execute code, shown inline as step cards (code execution output, file reads/writes) as it works.

Dashboard

Dashboard is the default tab and shows:

Latest run — widgets declared in dashboard.toml, rendered from the most recent run's output/output.json.
Recent runs — a list of past runs; click one to open its output.
View latest output — a live terminal showing stdout/stderr while a run is in progress, and the full log once it finishes.
Execution history — every run for the project, with status and timestamps.
Schedule — configure a cron schedule to run the project automatically, including which branch it runs.

Beyond [[widgets]], dashboard.toml can declare reusable datasets, define new KPIs as SQL, and add clock-driven playback — see Dashboard datasets, metrics, and the clock.

Before your first run

A project's Dashboard is empty until it has a successful run, and shows one of a few states depending on what's happened so far:

No runs yet — nothing has run on this project. Click Run to see something here.
Run in progress — the dashboard appears once this run finishes.
This run didn't finish successfully — fix the issue and run again; View output opens the raw log from the failed run.
This run has no dashboard output — the run succeeded, but main.py never wrote output/output.json, so there's nothing for dashboard.toml's widgets to bind to.

If a project isn't ready to run

A warning banner above the dashboard means the project is missing something runs and schedules need — most commonly on a project that predates its git repository being provisioned. It lists exactly what's blocking, for example:

Git repository — this project has no repository yet, so there's no code to run. A Set up repository button fixes this in one click.
harumi.toml — add one with a [run] command, from the Code tab or the CLI.
Valid harumi.toml — the file exists but couldn't be read.
Kernel — the kernel named in harumi.toml doesn't exist.

The banner disappears once every check passes.

Run options dialog

Clicking the caret next to Run opens the Run options dialog for a one-off override of a single run, without touching harumi.toml:

Environment — pick a different compute kernel for this run only.
Branch — run a branch other than the project's default, picked from the repo's actual branches.
Command — override the command harumi.toml's [run] section would otherwise run.

Leaving a field untouched uses the project's default for it. These map directly to the CLI's harumi run --kernel, --branch, and --command flags (see CLI commands) — anything you can set here, you can also set from a terminal or a CI job.

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