Integrate the Lawmatics API with the Python API

Setup the Lawmatics API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Lawmatics and Python remarkably fast. Free for developers.

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Popular Lawmatics and Python Actions#

Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

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Overview of Lawmatics#

Lawmatics is a platform tailored for legal firms, providing tools for CRM, marketing automation, and intake management. Its API enables the automation of routine tasks, data syncing across various platforms, and personalized client interactions without manual intervention. With Pipedream's serverless execution model and its capability to connect to hundreds of apps, one can automate workflows like client data synchronization, case status updates, or even trigger custom marketing campaigns based on client interaction. The API’s ability to communicate with Pipedream allows for an efficient and programmable way to handle legal operational processes.

Connect Lawmatics#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    lawmatics: {
      type: "app",
      app: "lawmatics",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.lawmatics.com/v1/users`,
      headers: {
        Authorization: `Bearer ${this.lawmatics.$auth.oauth_access_token}`,
      },
    })
  },
})

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}}

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