LogSnag
Event tracking made easy!
Integrate the LogSnag API with the Python API
Setup the LogSnag API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate LogSnag and Python remarkably fast. Free for developers.
Popular LogSnag and Python Actions#
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Overview of LogSnag#
LogSnag is a real-time event tracking API that enables developers to monitor and track events in their applications. With LogSnag, you can create simple, powerful dashboards to watch events as they happen and set up triggers to notify you of important activities. On Pipedream, LogSnag's capabilities can be harnessed to automate workflows, integrating with numerous services to create a blend of operations, such as triggering notifications, logging significant events, and gathering metrics across various platforms.
Connect LogSnag#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
logsnag: {
type: "app",
app: "logsnag",
}
},
async run({steps, $}) {
const data = {
"project": `[project_name]`,
"channel": `[channel]`,
"event": `Test request`,
"description": `Making a test request on LogSnag`,
}
return await axios($, {
method: "post",
url: `https://api.logsnag.com/v1/log`,
headers: {
Authorization: `Bearer ${this.logsnag.$auth.api_token}`,
"Content-Type": `application/json`,
},
data,
})
},
})
Overview of Python#
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
Connect Python#
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}