Integrate the Slack Bot API with the Python API

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

Popular Ways to Connect Slack Bot with Python#

Run Python Code with Python API on New Direct Message from Slack Bot API

Slack Bot + Python

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Run Python Code with Python API on New Message In Channel from Slack Bot API

Slack Bot + Python

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Popular Slack Bot and Python Triggers#

New Direct Message from the Slack Bot API

Emit new event when a message is posted in a direct message channel (Bot). To open a conversation, use the Pipedream Action "Send a Direct Message" to send a message from the Bot, or enable direct messages to the Bot in your App Settings (Settings->App Home->Show Tabs->Messages Tab), and send a direct message to the Bot.

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New Message In Channel from the Slack Bot API

Emit new event when a new message is posted to a public, private or group channel (Bot)

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Popular Slack Bot and Python Actions#

Archive Channel with the Slack Bot API

Archive a channel (Bot). See docs here

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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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Create a Channel with the Slack Bot API

Create a new channel (Bot). See docs here

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Delete File with the Slack Bot API

Delete a file (Bot). See docs here

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Delete Message with the Slack Bot API

Delete a message (Bot). See docs here

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Overview of Slack Bot#

Slack Bot APIs offer powerful and flexible ways to automate repetitive tasks
and integrate workflows from various services. With the help of these APIs, it
is possible to develop Slack bots capable of exiting and entering specific
channels as well as performing various actions requested from users.

A Slack bot can be used to automate specific tasks and carry out routine admin
jobs such as checking the team’s working time, organizing team meetings,
managing customer inquiries and so on. These bots can be programmed to inform
the members of the team when tasks are completed. Some of the examples of what
you can create using the Slack Bot APIs are:

Automated reminders
Productivity and time tracking
Data analysis and processing
Internal communication releases
Integrations with external tools and services
Conversation and inquiry management
Automation of customer service
Scheduling and tracking of tasks
Personalised customer recommendations

Connect Slack Bot#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    slack_bot: {
      type: "app",
      app: "slack_bot",
    },
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://slack.com/api/auth.test`,
      headers: {
        Authorization: `Bearer ${this.slack_bot.$auth.bot_token}`,
      },
      method: 'post',
    })
  },
})

Overview of Python#

Python API on Pipedream offers developers to build or automate a variety of
tasks from their web and cloud apps. With the Python API, users are able to
create comprehensive and flexible scripts, compose and manage environment
variables, and configure resources to perform a range of functions.

By using Pipedream, you can easily:

Create automated workflows that run on a specific schedule
Compose workflows across various apps and services
React to events in cloud services or form data
Automatically create content and notifications
Construct classifications and predictions
Analyze and react to sentiment, sentiment analysis and sentiment score
Connect backends to the frontend with serverless functions
Work with files and databases
Perform web requests and fetch data
Integrate third-party APIs into your apps
Orchestrate data processing tasks and pipelines
Create powerful application APIs with authentication and authorization
Design CI/CD pipelines and Continuous Delivery services
Connect databases like MongoDB and MySQL
Monitor connections and events
Generate alerts and notifications for corresponding events

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}}