LangChain Automate

LangChain Automate streamlines task management by converting voice or text messages from Telegram into actionable Todoist tasks. It intelligently transcribes voice messages, analyzes the content, and formats tasks with priority levels, ensuring efficient project organization and enhanced productivity.

7/8/2025
13 nodes
Medium
manualmediumlangchaintelegramtriggertelegramtodoiststicky noteadvancedcommunicationbotlogicrouting
Categories:
Communication & MessagingManual TriggeredMedium WorkflowProject Management
Integrations:
LangChainTelegramTriggerTelegramTodoistSticky Note

Target Audience

This workflow is ideal for:
- Project Managers who need to efficiently break down tasks into manageable sub-tasks.
- Team Leaders looking to streamline project planning and task delegation.
- Individual Contributors who want to improve their productivity by utilizing a structured task management approach.
- Developers interested in integrating AI tools for project management in their applications.
- Telegram Users who prefer voice or text communication for task management.

Problem Solved

This workflow addresses the challenge of task decomposition in project management. It allows users to:
- Convert vague project descriptions into clear, actionable sub-tasks formatted for Todoist.
- Efficiently manage tasks by utilizing AI to analyze and break down project requirements, ensuring no important details are overlooked.
- Facilitate communication through Telegram, making it easier for teams to collaborate and stay organized.

Workflow Steps

  • Receive Messages: The workflow starts by listening for incoming messages on Telegram. It can handle both voice and text messages.
    2. Determine Message Type: A switch node identifies whether the incoming message is a voice message or text. If it's a voice message, it fetches the audio file; if it's text, it prepares the text for processing.
    3. Transcribe Voice Messages: If the message is a voice note, it transcribes the audio into text using OpenAI's Whisper API.
    4. Prepare Input for LLM: The workflow prepares the text (either from the original message or the transcribed voice) for processing by the LLM (Language Model).
    5. Process with LLM: The text is analyzed by the OpenAI Chat Model, which generates structured sub-tasks formatted for Todoist.
    6. Extract Tasks: The output from the LLM is parsed to extract individual tasks.
    7. Create Todoist Tasks: Each extracted task is sent to Todoist, where it is created with the specified priority.
    8. Send Confirmation: Finally, a confirmation message is sent back to the user on Telegram, providing details about the created tasks and their links.

    • Customization Guide

    Users can customize this workflow by:
    - Modifying the System Prompt: Adjust the system prompt in the Basic LLM Chain to change how tasks are decomposed or to include additional fields in the output.
    - Changing Priority Levels: Customize the priority levels assigned to tasks based on specific project requirements or team preferences.
    - Integrating Additional Tools: Expand functionality by integrating other project management tools or communication platforms as needed.
    - Adjusting Message Handling: Modify the conditions in the Voice or Text? node to handle other types of incoming messages or formats.
    - Personalizing Confirmation Messages: Tailor the confirmation message sent via Telegram to fit the tone and style of communication preferred by the team.