* docs: add global ai mode plan
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: add global ai draft mode
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* fix: scope global ai mode to scripts and flows
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* refactor: simplify global ai workspace item shape
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* refactor: split global ai write tool into per-type tools
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: add global ai schedule and trigger workspace item tools
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: add dev-only /global_drafts route to inspect ai draft store
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: add edit_script and patch_flow_json global ai tools
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: add deploy_workspace_item global ai tool with confirmation
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: emit open-resource action card after deploy_workspace_item
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: add delete_workspace_item global ai tool with confirmation
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* chore(system_prompts): emit RESOURCES_BASE and resource/variable zod schemas
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: add global ai resource and variable workspace item tools
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* fix: search_resource_types uses listResourceType to avoid embedding feature dep
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Revert "fix: search_resource_types uses listResourceType to avoid embedding feature dep"
This reverts commit 6d1d19514a.
* feat: emit open-resource action card for variable and resource deploys
* feat: add global ai raw app workspace item tools
* feat: split raw-app prompt into chat-only authoring and cli prefix
* feat: add init_app global ai tool to scaffold raw apps from templates
* fix: pass write_flow value as JSON string for gemini compat
* refactor: hoist countExactMatches and applyExactReplace to chat/shared
* refactor: extract editableFlowJson module shared with global mode
* fix(global): preserve flow schema and groups across draft and deploy
* feat: extract inline scripts from flow reads and patches in global mode
* refactor: add findAndReplace helper for match-validated text patches
* refactor: extract getInlineRunnableContent helper for app file tools
* refactor: extract assertNotGeneratedAppFile guard for /wmill.d.ts
* feat: gate global ai mode behind localStorage flag for dev rollout
* chore: bump svelte to ^5.55.5 in raw app template (sync with main)
* fix: isolate global ai draft rollout
* fix: preserve global ai deploy metadata
* fix: harden global ai draft tools
* chore: remove global ai plan doc
* fix: align raw app prompt guidance
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
5.2 KiB
Windmill Raw Apps
Raw apps let you build custom frontends with React, Svelte, or Vue that connect to Windmill backend runnables and datatables.
App shape
A raw app has three logical parts:
- Frontend — bundled with esbuild from
index.tsxas the entrypoint. Files include the entrypoint, components (App.tsx), styles, etc. - Backend runnables — server-side scripts the frontend calls, each addressed by a unique key.
- Data — optional whitelisted datatables (managed PostgreSQL) that the backend runnables can query. The frontend never queries the database directly; backend runnables are the only bridge.
Frontend
Entrypoint
index.tsx is the bundling entrypoint. It typically renders a top-level App component. The bundler is esbuild.
Generated bindings (wmill.d.ts / wmill.ts)
The frontend imports a generated module that mirrors the backend runnables. Never write to it directly — it gets regenerated whenever backend runnables change. Modifying it by hand will be overwritten.
Calling backend runnables
Import the generated bindings and call the runnable like a function:
import { backend } from './wmill';
// Call a backend runnable
const user = await backend.get_user({ user_id: '123' });
The frontend cannot reach datatables, workspace items, or external services on its own — it goes through backend.<key>(args) for everything server-side.
Backend runnables
Each runnable has a unique key (used to call it from the frontend) and one of four types:
| Type | What it is |
|---|---|
inline |
Custom code stored on the app itself. Most common for app-specific logic. |
script |
Reference to an existing workspace script by path. |
flow |
Reference to an existing workspace flow by path. |
hubscript |
Reference to a hub script by path. |
Inline runnables
Inline runnables carry their own source code. For file-based raw apps, the runnable language is determined by the backend file extension. The script must expose a main function as its entrypoint.
TypeScript example (backend/get_user.ts):
import * as wmill from 'windmill-client';
export async function main(user_id: string) {
const sql = wmill.datatable();
const user = await sql`SELECT * FROM users WHERE id = ${user_id}`.fetchOne();
return user;
}
Python example (backend/get_user.py):
import wmill
def main(user_id: str):
db = wmill.datatable()
user = db.query('SELECT * FROM users WHERE id = $1', user_id).fetch_one()
return user
Path runnables (script / flow / hubscript)
When type is script, flow, or hubscript, the runnable just stores a path to an existing workspace or hub item — no inline code. The referenced item's input/output schema becomes the runnable's surface.
Static inputs
staticInputs is an optional Record<string, any> for arguments not overridable from the frontend. Useful with path runnables to pre-fill some args while leaving the rest to the frontend caller.
Data Tables
Data tables are PostgreSQL databases managed by Windmill. Backend runnables query them via the wmill client; the frontend never queries them directly.
Critical rules
- Whitelisted tables only: a runnable can only query tables listed in the app's
data.tablesconfig. Tables not in this list are not accessible. - Add tables before using: queries against unlisted tables fail at runtime. When you introduce a new table, register it in
data.tablesfirst. - Use the configured datatable/schema: the app's
dataconfig sets the default datatable and schema; reference them consistently across runnables.
Querying in TypeScript (Bun/Deno)
import * as wmill from 'windmill-client';
export async function main(user_id: string) {
const sql = wmill.datatable(); // Or: wmill.datatable('other_datatable')
// Parameterized queries (safe from SQL injection)
const user = await sql`SELECT * FROM users WHERE id = ${user_id}`.fetchOne();
const users = await sql`SELECT * FROM users WHERE active = ${true}`.fetch();
// Insert/Update
await sql`INSERT INTO users (name, email) VALUES (${name}, ${email})`;
await sql`UPDATE users SET name = ${newName} WHERE id = ${user_id}`;
return user;
}
Querying in Python
import wmill
def main(user_id: str):
db = wmill.datatable() # Or: wmill.datatable('other_datatable')
# Use $1, $2, etc. for parameters
user = db.query('SELECT * FROM users WHERE id = $1', user_id).fetch_one()
users = db.query('SELECT * FROM users WHERE active = $1', True).fetch()
# Insert/Update
db.query('INSERT INTO users (name, email) VALUES ($1, $2)', name, email)
db.query('UPDATE users SET name = $1 WHERE id = $2', new_name, user_id)
return user
Best Practices
- Check existing tables before creating new ones — reuse beats schema growth.
- Use parameterized queries — never concatenate user input into SQL.
- Keep runnables focused — one function per runnable; small surface area.
- Use descriptive keys —
get_user, nota. - Always whitelist tables — adding a runnable that queries a new table requires the table to be in
data.tablesfirst.