Integrate the CIRCL Hash Lookup API with the Python API

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

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Popular CIRCL Hash Lookup 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 CIRCL Hash Lookup#

The CIRCL Hash Lookup API offers a way to check files against known hashes to detect potential threats or verify file integrity. Within Pipedream's serverless platform, you can leverage this API to automate security checks, integrate with your existing cloud storage solutions, or enhance your incident response workflows by cross-referencing file hashes against CIRCL's extensive database. Pipedream's ability to connect to hundreds of apps allows you to create multifaceted workflows that trigger on various events, process data, and invoke the Hash Lookup API to provide real-time security analysis.

Connect CIRCL Hash Lookup#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    circl_hash_lookup: {
      type: "app",
      app: "circl_hash_lookup",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://hashlookup.circl.lu/lookup/md5/{your_md5_hash}`,
      headers: {
        "accept": `application/json`,
      },
    })
  },
})

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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