windmill-labs CLI Commands
Place scripts in a folder.
After writing, tell the user which command fits what they want to do:
wmill script preview <script_path>— default when iterating on a local script. Runs the local file without deploying.wmill script run <path>— runs the script already deployed in the workspace. Use only when the user explicitly wants to test the deployed version, not local edits.wmill generate-metadata— regenerate the local.script.yaml(input schema) and.lock(resolved dependencies) for scripts you changed, and refresh their content hashes inwmill-lock.yaml. Local files only — not a deploy. See "Keep metadata in sync" below.- Deploy local changes to the workspace — via
git pushorwmill sync pushdepending on how the repo is wired (see the Deploying section inAGENTS.wmill.md). Only suggest/run a deploy when the user explicitly asks to deploy/publish/push — not when they say "run", "try", or "test".
Preview vs run — choose by intent, not habit
If the user says "run the script", "try it", "test it", "does it work" while there are local edits to the script file, use script preview. Do NOT push the script to then script run it — pushing is a deploy, and deploying just to test overwrites the workspace version with untested changes.
Only use script run when:
- The user explicitly says "run the deployed version" / "run what's on the server".
- There is no local script being edited (you're just invoking an existing script).
Only use sync push when:
- The user explicitly asks to deploy, publish, push, or ship.
- The preview has already validated the change and the user wants it in the workspace.
Keep metadata in sync after editing
wmill-lock.yaml tracks a content hash for each item. Editing a script's content — most importantly adding or removing an import or changing main's arguments — invalidates that hash and leaves the .lock, the .script.yaml input schema, and the hash row out of date. Run wmill generate-metadata (scoped to what you touched) after such edits so the resolved lock, the auto-generated args UI (driven by .script.yaml), and wmill-lock.yaml all match the code. Leaving them stale produces spurious diffs in git-sync and CI.
This only writes local files (it is not a deploy), but it re-resolves dependencies, so it can bump unpinned versions (the same as deploying from the UI; expected, not a bug). So by default offer it and run it once the user agrees, rather than running it silently after every edit — unless the project's AGENTS.md opts into running metadata automatically (see the "Keeping metadata in sync" preference there). Either way YOU run the command, not the user. After running it, diff the regenerated .lock / .script.lock files and tell the user which dependency versions changed (e.g. requests 2.31.0 → 2.32.0), so they can catch an unwanted bump before deploying — even under Metadata: auto, since it's information, not a confirmation gate. Pin versions in code to keep them fixed.
With no path argument, generate-metadata regenerates only the items whose content hash drifted — not everything. Imports propagate: editing a script that others import marks every importer stale too, so a one-line change to a shared module can regenerate many locks (by design — their locks must reflect the imported code). If it touches more than you expect, run wmill generate-metadata --dry-run — it lists each stale item with a reason (content changed or depends on <path>) without changing anything — then narrow with a path argument (wmill generate-metadata f/foo) or --strict-folder-boundaries.
If the on-disk .lock and .script.yaml are already correct and only wmill-lock.yaml needs its hashes refreshed (hash drift, or bootstrapping missing entries), use wmill generate-metadata rehash — it re-records hashes from disk with no backend round-trip and no dependency changes.
After writing — offer to test, don't wait passively
If the user hasn't already told you to run/test/preview the script, offer it as a one-sentence next step (e.g. "Want me to run wmill script preview with sample args?"). Do not present a multi-option menu.
If the user already asked to test/run/try the script in their original request, skip the offer and just execute wmill script preview <path> -d '<args>' directly — pick plausible args from the script's declared parameters. The shape varies by language: main(...) for code languages, the SQL dialect's own placeholder syntax ($1 for PostgreSQL, ? for MySQL/Snowflake, @P1 for MSSQL, @name for BigQuery, etc.), positional $1, $2, … for Bash, param(...) for PowerShell.
wmill script preview does not deploy, but it still executes script code and may cause side effects; run it yourself when the user asked to test/preview (or after confirming that execution is intended). wmill generate-metadata does not deploy either — it only writes local files (locks, schemas, hashes) — but offer it before running (or run automatically if the project's AGENTS.md opts in), per "Keep metadata in sync" above. Deploying to the workspace (git push or wmill sync push depending on how the repo is wired — see the Deploying section) is the only step that mutates remote state — do it only when the user explicitly asks to deploy/publish/push.
For a visual open-the-script-in-the-dev-page preview (rather than script preview's run-and-print-result), use the preview skill.
Use wmill resource-type list --schema to discover available resource types.
Python
Structure
The script must contain at least one function called main:
def main(param1: str, param2: int):
# Your code here
return {"result": param1, "count": param2}
Do not call the main function. Libraries are installed automatically.
Resource Types
On Windmill, credentials and configuration are stored in resources and passed as parameters to main.
You need to redefine the type of the resources that are needed before the main function as TypedDict:
from typing import TypedDict
class postgresql(TypedDict):
host: str
port: int
user: str
password: str
dbname: str
def main(db: postgresql):
# db contains the database connection details
pass
Important rules:
- The resource type name must be IN LOWERCASE
- Only include resource types if they are actually needed
- If an import conflicts with a resource type name, rename the imported object, not the type name
- Make sure to import TypedDict from typing if you're using it
Imports
Libraries are installed automatically. Do not show installation instructions.
import requests
import pandas as pd
from datetime import datetime
If an import name conflicts with a resource type:
# Wrong - don't rename the type
import stripe as stripe_lib
class stripe_type(TypedDict): ...
# Correct - rename the import
import stripe as stripe_sdk
class stripe(TypedDict):
api_key: str
Windmill Client
Import the windmill client for platform interactions:
import wmill
See the SDK documentation for available methods.
Preprocessor Scripts
For preprocessor scripts, the function should be named preprocessor and receives an event parameter:
from typing import TypedDict, Literal, Any
class Event(TypedDict):
kind: Literal["webhook", "http", "websocket", "kafka", "email", "nats", "postgres", "sqs", "mqtt", "gcp"]
body: Any
headers: dict[str, str]
query: dict[str, str]
def preprocessor(event: Event):
# Transform the event into flow input parameters
return {
"param1": event["body"]["field1"],
"param2": event["query"]["id"]
}
S3 Object Operations
Windmill provides built-in support for S3-compatible storage operations.
Receiving an S3Object as a script parameter
To accept a file from S3 as input to a script, type the parameter with S3Object (imported from wmill):
import wmill
from wmill import S3Object
def main(file: S3Object):
content = wmill.load_s3_file(file)
# ...
S3 operations
import wmill
# Load file content from S3
content: bytes = wmill.load_s3_file(s3object)
# Load file as stream reader
reader: BufferedReader = wmill.load_s3_file_reader(s3object)
# Write file to S3
result: S3Object = wmill.write_s3_file(
s3object, # Target path (or None to auto-generate)
file_content, # bytes or BufferedReader
s3_resource_path, # Optional: specific S3 resource
content_type, # Optional: MIME type
content_disposition # Optional: Content-Disposition header
)
Python SDK (wmill)
Import: import wmill
The client configures itself from the job's environment — base URL, token and credentials mode are all set before your code runs, so there is nothing to initialize and no reason to read WM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only reintroduces details the client already handles. Call the SDK for anything Windmill, and use raw HTTP for third-party APIs.
The functions below are the surface to prefer. For an endpoint none of them covers, wmill.Windmill().get(endpoint) and .post(endpoint) issue an authenticated request against this instance. What does not exist is a function name you guessed at: if it is not listed below, do not call it.
To know who is running the script, read the contextual variables rather than calling the API:
os.environ.get("WM_END_USER_EMAIL") or os.environ.get("WM_EMAIL"). WM_END_USER_EMAIL is the app
viewer when the run was triggered from an app and empty otherwise (both variables are always
defined), WM_EMAIL is the user the job is permissioned as. WM_USERNAME is the matching username.
def worker_has_internal_server() -> bool
def get_mocked_api() -> Optional[dict]
Get the HTTP client instance.
Returns:
Configured httpx.Client for API requests
def get_client() -> httpx.Client
Make an HTTP GET request to the Windmill API.
Args:
endpoint: API endpoint path
raise_for_status: Whether to raise an exception on HTTP errors
**kwargs: Additional arguments passed to httpx.get
Returns:
HTTP response object
def get(endpoint, raise_for_status = True, **kwargs) -> httpx.Response
Make an HTTP POST request to the Windmill API.
Args:
endpoint: API endpoint path
raise_for_status: Whether to raise an exception on HTTP errors
**kwargs: Additional arguments passed to httpx.post
Returns:
HTTP response object
def post(endpoint, raise_for_status = True, **kwargs) -> httpx.Response
Create a new authentication token.
Args:
duration: Token validity duration (default: 1 day)
Returns:
New authentication token string
def create_token(duration = dt.timedelta(days=1)) -> str
Create a script job by path and return its job id.
def run_script_by_path_async(path: str, args: dict = None, scheduled_in_secs: int = None, tag: str = None) -> str
Create a script job by hash and return its job id.
def run_script_by_hash_async(hash_: str, args: dict = None, scheduled_in_secs: int = None, tag: str = None) -> str
Create a flow job and return its job id.
def run_flow_async(path: str, args: dict = None, scheduled_in_secs: int = None, do_not_track_in_parent: bool = True, tag: str = None) -> str
Run script by path synchronously and return its result.
def run_script_by_path(path: str, args: dict = None, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False, tag: str = None) -> Any
Run script by hash synchronously and return its result.
def run_script_by_hash(hash_: str, args: dict = None, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False, tag: str = None) -> Any
Run a script on the current worker without creating a job.
On agent workers (no internal server), falls back to running a normal
preview job and waiting for the result.
def run_inline_script_preview(content: str, language: str, args: dict = None) -> Any
Wait for a job to complete and return its result.
Args:
job_id: ID of the job to wait for
timeout: Maximum time to wait (seconds or timedelta)
verbose: Enable verbose logging
cleanup: Register cleanup handler to cancel job on exit
assert_result_is_not_none: Raise exception if result is None
Returns:
Job result when completed
Raises:
TimeoutError: If timeout is reached
Exception: If job fails
def wait_job(job_id, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False)
Cancel a specific job by ID.
Args:
job_id: UUID of the job to cancel
reason: Optional reason for cancellation
Returns:
Response message from the cancel endpoint
def cancel_job(job_id: str, reason: str = None) -> str
Cancel currently running executions of the same script.
def cancel_running() -> dict
Get job details by ID.
Args:
job_id: UUID of the job
Returns:
Job details dictionary
def get_job(job_id: str) -> dict
Get the root job ID for a flow hierarchy.
Args:
job_id: Job ID (defaults to current WM_JOB_ID)
Returns:
Root job ID
def get_root_job_id(job_id: str | None = None) -> dict
Get an OIDC JWT token for authentication to external services.
Args:
audience: Token audience (e.g., "vault", "aws")
expires_in: Optional expiration time in seconds
Returns:
JWT token string
def get_id_token(audience: str, expires_in: int | None = None) -> str
Get the status of a job.
Args:
job_id: UUID of the job
Returns:
Job status: "RUNNING", "WAITING", or "COMPLETED"
def get_job_status(job_id: str) -> JobStatus
Get the result of a completed job.
Args:
job_id: UUID of the completed job
assert_result_is_not_none: Raise exception if result is None
Returns:
Job result
def get_result(job_id: str, assert_result_is_not_none: bool = True) -> Any
Get a variable value by path.
Args:
path: Variable path in Windmill
Returns:
Variable value as string
def get_variable(path: str) -> str
Set a variable value by path, creating it if it doesn't exist.
Args:
path: Variable path in Windmill
value: Variable value to set
is_secret: Whether the variable should be secret (default: False)
def set_variable(path: str, value: str, is_secret: bool = False) -> None
Get a resource value by path.
Args:
path: Resource path in Windmill
none_if_undefined: Return None instead of raising if not found
interpolated: if variables and resources are fully unrolled
Returns:
Resource value dictionary or None
def get_resource(path: str, none_if_undefined: bool = False, interpolated: bool = True) -> dict | None
Set a resource value by path, creating it if it doesn't exist.
Args:
value: Resource value to set
path: Resource path in Windmill
resource_type: Resource type for creation
def set_resource(value: Any, path: str, resource_type: str)
List resources from Windmill workspace.
Args:
resource_type: Optional resource type to filter by (e.g., "postgresql", "mysql", "s3")
page: Optional page number for pagination
per_page: Optional number of results per page
Returns:
List of resource dictionaries
def list_resources(resource_type: str = None, page: int = None, per_page: int = None) -> list[dict]
Set the workflow state.
Args:
value: State value to set
path: Optional state resource path override.
def set_state(value: Any, path: str | None = None) -> None
Get the workflow state.
Args:
path: Optional state resource path override.
Returns:
State value or None if not set
def get_state(path: str | None = None) -> Any
Set job progress percentage (0-99).
Args:
value: Progress percentage
job_id: Job ID (defaults to current WM_JOB_ID)
def set_progress(value: int, job_id: Optional[str] = None)
Get job progress percentage.
Args:
job_id: Job ID (defaults to current WM_JOB_ID)
Returns:
Progress value (0-100) or None if not set
def get_progress(job_id: Optional[str] = None) -> Any
Set the user state of a flow at a given key
def set_flow_user_state(key: str, value: Any) -> None
Get the user state of a flow at a given key
def get_flow_user_state(key: str) -> Any
Get the Windmill server version.
Returns:
Version string
def version()
Convenient helpers that takes an S3 resource as input and returns the settings necessary to
initiate an S3 connection from DuckDB
def get_duckdb_connection_settings(s3_resource_path: str = '') -> DuckDbConnectionSettings | None
Convenient helpers that takes an S3 resource as input and returns the settings necessary to
initiate an S3 connection from Polars
def get_polars_connection_settings(s3_resource_path: str = '') -> PolarsConnectionSettings
Convenient helpers that takes an S3 resource as input and returns the settings necessary to
initiate an S3 connection using boto3
def get_boto3_connection_settings(s3_resource_path: str = '') -> Boto3ConnectionSettings
Load a file from the workspace s3 bucket and returns its content as bytes.
'''python
from wmill import S3Object
s3_obj = S3Object(s3="/path/to/my_file.txt")
my_obj_content = client.load_s3_file(s3_obj)
file_content = my_obj_content.decode("utf-8")
'''
def load_s3_file(s3object: S3Object | str, s3_resource_path: str | None) -> bytes
Load a file from the workspace s3 bucket and returns the bytes stream.
'''python
from wmill import S3Object
s3_obj = S3Object(s3="/path/to/my_file.txt")
with wmill.load_s3_file_reader(s3object, s3_resource_path) as file_reader:
print(file_reader.read())
'''
def load_s3_file_reader(s3object: S3Object | str, s3_resource_path: str | None) -> BufferedReader
Write a file to the workspace S3 bucket
'''python
from wmill import S3Object
s3_obj = S3Object(s3="/path/to/my_file.txt")
# for an in memory bytes array:
file_content = b'Hello Windmill!'
client.write_s3_file(s3_obj, file_content)
# for a file:
with open("my_file.txt", "rb") as my_file:
client.write_s3_file(s3_obj, my_file)
'''
def write_s3_file(s3object: S3Object | str | None, file_content: BufferedReader | bytes, s3_resource_path: str | None, content_type: str | None = None, content_disposition: str | None = None) -> S3Object
Permanently delete a file from the workspace S3 bucket.
'''python
from wmill import S3Object
s3_obj = S3Object(s3="/path/to/my_file.txt")
client.delete_s3_object(s3_obj)
'''
def delete_s3_object(s3object: S3Object | str, s3_resource_path: str | None = None) -> None
Sign S3 objects for use by anonymous users in public apps.
Args:
s3_objects: List of S3 objects to sign
expiry_secs: How long the signature stays valid, in seconds
(defaults to 43200 = 12h, clamped to [60, 604800])
Returns:
List of signed S3 objects
def sign_s3_objects(s3_objects: list[S3Object | str], expiry_secs: int | None = None) -> list[S3Object]
Sign a single S3 object for use by anonymous users in public apps.
Args:
s3_object: S3 object to sign
expiry_secs: How long the signature stays valid, in seconds
(defaults to 43200 = 12h, clamped to [60, 604800])
Returns:
Signed S3 object
def sign_s3_object(s3_object: S3Object | str, expiry_secs: int | None = None) -> S3Object
Generate presigned public URLs for an array of S3 objects.
If an S3 object is not signed yet, it will be signed first.
Args:
s3_objects: List of S3 objects to sign
base_url: Optional base URL for the presigned URLs (defaults to WM_BASE_URL)
expiry_secs: How long the signatures stay valid, in seconds
(defaults to 43200 = 12h, clamped to [60, 604800])
Returns:
List of signed public URLs
Example:
>>> s3_objs = [S3Object(s3="/path/to/file1.txt"), S3Object(s3="/path/to/file2.txt")]
>>> urls = client.get_presigned_s3_public_urls(s3_objs)
def get_presigned_s3_public_urls(s3_objects: list[S3Object | str], base_url: str | None = None, expiry_secs: int | None = None) -> list[str]
Generate a presigned public URL for an S3 object.
If the S3 object is not signed yet, it will be signed first.
Args:
s3_object: S3 object to sign
base_url: Optional base URL for the presigned URL (defaults to WM_BASE_URL)
expiry_secs: How long the signature stays valid, in seconds
(defaults to 43200 = 12h, clamped to [60, 604800])
Returns:
Signed public URL
Example:
>>> s3_obj = S3Object(s3="/path/to/file.txt")
>>> url = client.get_presigned_s3_public_url(s3_obj)
def get_presigned_s3_public_url(s3_object: S3Object | str, base_url: str | None = None, expiry_secs: int | None = None) -> str
Get the current user information.
Returns:
User details dictionary
def whoami() -> dict
Get the current user information (alias for whoami).
Returns:
User details dictionary
def user() -> dict
Get the state resource path from environment.
Returns:
State path string
def state_path() -> str
Get the workflow state.
Returns:
State value or None if not set
def state() -> Any
Set the state in the shared folder using pickle
def set_shared_state_pickle(value: Any, path: str = 'state.pickle') -> None
Get the state in the shared folder using pickle
def get_shared_state_pickle(path: str = 'state.pickle') -> Any
Set the state in the shared folder using pickle
def set_shared_state(value: Any, path: str = 'state.json') -> None
Get the state in the shared folder using pickle
def get_shared_state(path: str = 'state.json') -> None
Get URLs needed for resuming a flow after suspension.
Args:
approver: Optional approver name
flow_level: If True, generate resume URLs for the parent flow instead of the
specific step. This allows pre-approvals that can be consumed by any later
suspend step in the same flow.
Returns:
Dictionary with approvalPage, resume, and cancel URLs
def get_resume_urls(approver: str = None, flow_level: bool = None) -> dict
Get the resume URLs bound to one wait_for_approval step of this workflow.
Args:
step_key: Checkpoint key of the approval step, as passed to
wait_for_approval(key=...)
approver: Optional approver name
Returns:
Dictionary with approvalPage, resume, and cancel URLs
def get_approval_urls(step_key: str = 'approval', approver: str = None) -> dict
Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.
[Enterprise Edition Only] To include form fields in the Slack approval request, use the "Advanced -> Suspend -> Form" functionality.
Learn more at: https://www.windmill.dev/docs/flows/flow_approval#form
:param slack_resource_path: The path to the Slack resource in Windmill.
:type slack_resource_path: str
:param channel_id: The Slack channel ID where the approval request will be sent.
:type channel_id: str
:param message: Optional custom message to include in the Slack approval request.
:type message: str, optional
:param approver: Optional user ID or name of the approver for the request.
:type approver: str, optional
:param default_args_json: Optional dictionary defining or overriding the default arguments for form fields.
:type default_args_json: dict, optional
:param dynamic_enums_json: Optional dictionary overriding the enum default values of enum form fields.
:type dynamic_enums_json: dict, optional
:raises Exception: If the function is not called within a flow or flow preview.
:raises Exception: If the required flow job or flow step environment variables are not set.
:return: None
Usage Example:
>>> client.request_interactive_slack_approval(
... slack_resource_path="/u/alex/my_slack_resource",
... channel_id="admins-slack-channel",
... message="Please approve this request",
... approver="approver123",
... default_args_json={"key1": "value1", "key2": 42},
... dynamic_enums_json={"foo": ["choice1", "choice2"], "bar": ["optionA", "optionB"]},
... )
Notes:
- This function must be executed within a Windmill flow or flow preview.
- The function checks for required environment variables (WM_FLOW_JOB_ID, WM_FLOW_STEP_ID) to ensure it is run in the appropriate context.
def request_interactive_slack_approval(slack_resource_path: str, channel_id: str, message: str = None, approver: str = None, default_args_json: dict = None, dynamic_enums_json: dict = None) -> None
Send a message to a Microsoft Teams conversation with conversation_id, where success is used to style the message
def send_teams_message(conversation_id: str, text: str, success: bool = True, card_block: dict = None)
Get a DataTable client for SQL queries.
Args:
name: Database name (default: "main")
Returns:
DataTableClient instance
def datatable(name: str = 'main')
Get a DuckLake client for DuckDB queries.
Args:
name: Database name (default: "main")
Returns:
DucklakeClient instance
def ducklake(name: str = 'main')
def init_global_client(f)
def deprecate(in_favor_of: str)
Get the current workspace ID.
Returns:
Workspace ID string
def get_workspace() -> str
def get_version() -> str
Create a script job and return its job ID.
Args:
hash_or_path: Script hash or path (determined by presence of '/')
args: Script arguments
scheduled_in_secs: Delay before execution in seconds
tag: Override the worker tag the job runs on
Returns:
Job ID string
def run_script_async(hash_or_path: str, args: Dict[str, Any] = None, scheduled_in_secs: int = None, tag: str = None) -> str
Run a script synchronously by hash and return its result.
Args:
hash: Script hash
args: Script arguments
verbose: Enable verbose logging
assert_result_is_not_none: Raise exception if result is None
cleanup: Register cleanup handler to cancel job on exit
timeout: Maximum time to wait
tag: Override the worker tag the job runs on
Returns:
Script result
def run_script_sync(hash: str, args: Dict[str, Any] = None, verbose: bool = False, assert_result_is_not_none: bool = True, cleanup: bool = True, timeout: dt.timedelta = None, tag: str = None) -> Any
Run a script synchronously by path and return its result.
Args:
path: Script path
args: Script arguments
verbose: Enable verbose logging
assert_result_is_not_none: Raise exception if result is None
cleanup: Register cleanup handler to cancel job on exit
timeout: Maximum time to wait
tag: Override the worker tag the job runs on
Returns:
Script result
def run_script_by_path_sync(path: str, args: Dict[str, Any] = None, verbose: bool = False, assert_result_is_not_none: bool = True, cleanup: bool = True, timeout: dt.timedelta = None, tag: str = None) -> Any
Convenient helpers that takes an S3 resource as input and returns the settings necessary to
initiate an S3 connection from DuckDB
def duckdb_connection_settings(s3_resource_path: str = '') -> DuckDbConnectionSettings
Convenient helpers that takes an S3 resource as input and returns the settings necessary to
initiate an S3 connection from Polars
def polars_connection_settings(s3_resource_path: str = '') -> PolarsConnectionSettings
Convenient helpers that takes an S3 resource as input and returns the settings necessary to
initiate an S3 connection using boto3
def boto3_connection_settings(s3_resource_path: str = '') -> Boto3ConnectionSettings
Get the state resource path from environment.
Returns:
State path string
def get_state_path() -> str
Parse resource syntax from string.
def parse_resource_syntax(s: str) -> Optional[str]
Parse S3 object from a s3://<storage>/<key> URI string (s3:///<key>
for the default storage) or S3Object format. Any other string raises
rather than falling back to an auto-generated key: an auto key is
requested by omitting the object, and a fallback would silently misplace
the upload on any typo.
def parse_s3_object(s3_object: S3Object | str) -> S3Object
Parse variable syntax from string.
def parse_variable_syntax(s: str) -> Optional[str]
Append a text to the result stream.
Args:
text: text to append to the result stream
def append_to_result_stream(text: str) -> None
Stream to the result stream.
Args:
stream: stream to stream to the result stream
def stream_result(stream) -> None
Execute a SQL query against the DataTable.
Args:
sql: SQL query string with $1, $2, etc. placeholders
*args: Positional arguments to bind to query placeholders
Returns:
SqlQuery instance for fetching results
def query(sql: str, *args) -> SqlQuery
Idempotently materialize the rows of select_sql into ducklake
table for one partition (or the whole table when partition is
None). Client-side equivalent of the // materialize engine: with
unique_key it upserts within the slice (delete-by-key + insert);
without it, it replaces (whole table → CREATE OR REPLACE; partition →
delete the partition + insert). Re-running the same slice is safe — the
backfill / failure-recovery contract.
The partition value is bound as a DuckDB arg (never string-interpolated)
so it cannot inject SQL. select_sql is trusted (your own query).
def upsert_partition(table: str, select_sql: str, partition: str = None, unique_key: str = None, partition_col: str = '_wm_partition', schema: str = None)
INSERT-only materialization (no dedup / no replace) for an immutable
event-log table — for one partition, or the whole table when
partition is None. NOTE: unlike upsert_partition, re-running the same
slice duplicates rows — use only for append-only sources.
def append_partition(table: str, select_sql: str, partition: str = None, partition_col: str = '_wm_partition', schema: str = None)
Read a materialized ducklake table, optionally a single partition.
def read(table: str, partition: str = None, partition_col: str = '_wm_partition', schema: str = None)
Execute query and fetch results.
Args:
result_collection: Optional result collection mode
Returns:
Query results
def fetch(result_collection: str | None = None)
Execute query and fetch first row of results.
Returns:
First row of query results
def fetch_one()
Execute query and fetch first row of results. Return result as a scalar value.
Returns:
First row of query result as a scalar value
def fetch_one_scalar()
Execute query and don't return any results.
def execute()
DuckDB executor requires explicit argument types at declaration
These types exist in both DuckDB and Postgres
Check that the types exist if you plan to extend this function for other SQL engines.
def infer_sql_type(value) -> str
def parse_sql_client_name(name: str) -> tuple[str, Optional[str]]
Decorator that marks a function as a workflow task.
Works in both WAC v1 (sync, HTTP-based dispatch) and WAC v2
(async, checkpoint/replay) modes:
- v2 (inside @workflow): dispatches as a checkpoint step.
- v1 (WM_JOB_ID set, no @workflow): dispatches via HTTP API.
- Standalone: executes the function body directly.
A task runs as its own job, so its result is always encoded as JSON and
decoded back before the caller sees it: a datetime comes back as a
string, a tuple as a list.
retry re-dispatches the task after a failure, inside @workflow only.
Every attempt is a step of its own (call_api, call_api#2, ...) and
the wait between two of them is a durable sleep, so a retrying task holds no
worker while it backs off. Keys: attempts (retries after the first
failure, a whole number from 0 to 100), delay (seconds before the first
retry, sub-second delays dropped), multiplier (applied to the delay
after each attempt, 1 keeps it constant), max_delay (ceiling in
seconds). attempts is required, and an out-of-range or unknown key is
rejected where the policy is written.
A workflow sleeps once per round, so tasks backing off in the same fan-out
wait one after another rather than together: the delay before a fan-out
retries is the sum of every backoff pending in it, not the longest one, and
it grows with both the width of the fan-out and attempts. Retries with
no delay all go out in a single round.
Usage::
@task
async def extract_data(url: str): ...
@task(path="f/external_script", timeout=600, tag="gpu")
async def run_external(x: int): ...
@task(retry={"attempts": 3, "delay": 30, "multiplier": 2})
async def call_api(payload: dict): ...
def task(_func = None, path: Optional[str] = None, tag: Optional[str] = None, timeout: Optional[int] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)
Create a task that dispatches to a separate Windmill script.
retry takes the same policy as :func:task.
Usage::
extract = task_script("f/data/extract", timeout=600)
@workflow
async def main():
data = await extract(url="https://...")
def task_script(path: str, timeout: Optional[int] = None, tag: Optional[str] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)
Create a task that dispatches to a separate Windmill flow.
retry takes the same policy as :func:task.
Usage::
pipeline = task_flow("f/etl/pipeline", priority=10)
@workflow
async def main():
result = await pipeline(input=data)
def task_flow(path: str, timeout: Optional[int] = None, tag: Optional[str] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)
Decorator marking an async function as a workflow-as-code entry point.
The function must be deterministic: given the same inputs it must call
tasks in the same order on every replay. Branching on task results is fine
(results are replayed from checkpoint), but branching on external state
(current time, random values, external API calls) must use step() to
checkpoint the value so replays see the same result.
def workflow(func)
Execute fn inline and checkpoint the result.
On replay the cached value is returned without re-executing fn.
Use for lightweight deterministic operations (timestamps, random IDs,
config reads) that should not incur the overhead of a child job.
fn's result is encoded as JSON and decoded back before it is returned,
so the round that runs the body sees the same types every replay sees:
a datetime comes back as a string, a tuple as a list.
async def step(name: str, fn)
Server-side sleep — suspend the workflow for the given duration without holding a worker.
Inside a @workflow, the parent job suspends and auto-resumes after seconds.
Outside a workflow, falls back to asyncio.sleep.
async def sleep(seconds: int)
Suspend the workflow and wait for an external approval.
Pass key to name the step, then get_approval_urls(key) yields the URLs
that resume exactly this approval — route them through your own channel.
Without a key the steps are named approval, approval_2, ...
Returns a dict with value (form data), approver, and approved.
Args:
timeout: Approval timeout in seconds (default 1800).
form: Optional form schema for the approval page.
self_approval: Whether the user who triggered the flow can approve it (default True).
key: Optional checkpoint key naming this approval step.
Example::
urls = await step("urls", lambda: get_approval_urls("manager"))
await step("notify", lambda: send_email(urls["resume"], urls["cancel"]))
result = await wait_for_approval(key="manager", timeout=3600)
async def wait_for_approval(timeout: int = 1800, form: dict | None = None, self_approval: bool = True, key: str | None = None) -> dict
Process items in parallel with optional concurrency control.
Each item is processed by calling fn(item), which should be a @task.
Items are dispatched in batches of concurrency (default: all at once).
Example::
@task
async def process(item: str):
...
results = await parallel(items, process, concurrency=5)
async def parallel(items, fn, concurrency: Optional[int] = None)
Commit Kafka offsets for a trigger with auto_commit disabled.
Args:
trigger_path: Path to the Kafka trigger (from event['wm_trigger']['trigger_path'])
topic: Kafka topic name (from event['topic'])
partition: Partition number (from event['partition'])
offset: Message offset to commit (from event['offset'])
def commit_kafka_offsets(trigger_path: str, topic: str, partition: int, offset: int) -> None