LLM Chaining examples

For platform LLM Chaining, automate complex tasks through a 38-node workflow that integrates webhooks, Markdown, and LangChain. This workflow efficiently processes data, generates insightful responses, and enhances productivity by leveraging advanced language models for seamless interaction and output generation.

7/8/2025
38 nodes
Complex
webhookcomplexmarkdownsticky notelangchainsplitoutnoopadvancedapiintegration
Categories:
Complex WorkflowWebhook Triggered
Integrations:
MarkdownSticky NoteLangChainSplitOutNoOp

Target Audience

This workflow is designed for:
- Content Creators looking to automate the extraction and processing of data from web pages.
- Developers seeking to integrate advanced AI functionalities into their applications.
- Marketers aiming to analyze web content and generate insights quickly.
- Educators who want to create interactive and informative content based on existing web resources.

Problem Solved

This workflow addresses the challenge of efficiently extracting and analyzing information from web pages. It automates the process of:
- Gathering data from a specified URL.
- Transforming HTML content into Markdown format for easier readability.
- Utilizing AI models to generate summaries, identify authors, and create engaging content like jokes based on the extracted information.

Workflow Steps

  • Trigger: The workflow starts when the user clicks 'Test workflow'.
    2. HTTP Request: An HTTP request fetches data from a specified URL (e.g., https://blog.n8n.io/).
    3. Markdown Conversion: The fetched HTML content is converted into Markdown format for easier processing.
    4. Prompt Initialization: Initial prompts are set up to guide the AI in generating responses based on the extracted content.
    5. Sequential LLM Chains: The workflow employs multiple LLM chains to:
    - Identify what is on the page.
    - List all authors.
    - List all posts.
    - Create a humorous joke based on the content.
    6. Memory Management: The workflow manages memory to retain context and improve response accuracy.
    7. Final Output: The responses from the AI models are merged and presented as the final output, which can be sent back to the user or stored for further analysis.

    • Customization Guide

    Users can customize this workflow by:
    - Modifying the URL: Change the URL in the HTTP Request node to fetch data from a different web page.
    - Adjusting Prompts: Edit the initial prompts to tailor the AI responses to specific needs or contexts.
    - Changing AI Models: Swap out the AI model in the Anthropic Chat Model nodes to utilize different capabilities or features.
    - Altering Output Format: Customize how the final output is formatted or processed by modifying the Markdown conversion or response handling sections.