Python client for TWS.
pip install tws-sdkThe library provides both synchronous and asynchronous clients for interacting with TWS.
The primary API is run_workflow, which executes a workflow configured via the TWS UI, waits for completion,
and returns the result.
from tws import Client as TWSClient
# Use the client with a context manager
with TWSClient(
public_key="your_public_key",
secret_key="your_secret_key",
api_url="your_api_url"
) as tws_client:
# Run a workflow and wait for completion
result = tws_client.run_workflow(
workflow_definition_id="your_workflow_id",
workflow_args={
"param1": "value1",
"param2": "value2"
},
)The signatures are exactly the same for async usage, but the client class is TWSAsyncClient and client
methods are awaited.
from tws import AsyncClient as TWSAsyncClient
async def main():
# Use the async client with a context manager
async with TWSAsyncClient(
public_key="your_public_key",
secret_key="your_secret_key",
api_url="your_api_url"
) as tws_client:
# Run a workflow and wait for completion
result = await tws_client.run_workflow(
workflow_definition_id="your_workflow_id",
workflow_args={
"param1": "value1",
"param2": "value2"
},
)You can specify tags, which are string key-value pairs, when calling the run_workflow method. These tags can then
be used when designing workflows in TWS to lookup and filter the results of workflow runs. This allows you to associate
the results of a workflow run with a specific entity or grouping mechanism within your system, such as a user ID or
a lesson ID.
Provide tags to the run_workflow method as a dictionary. Keep in mind that both tag keys and values must be strings
that are at most 255 characters long.
tws_client.run_workflow(
workflow_definition_id="your_workflow_id",
workflow_args={
"param1": "value1",
"param2": "value2"
},
tags={
"user_id": "12345",
"lesson_id": "67890"
}
)