Mastering LLM Chaining with n8n and Anthropic Claude
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Explore the power of LLM chaining by building a workflow in n8n that leverages Anthropic's Claude models. This example demonstrates how to orchestrate multiple AI prompts to extract specific information and generate creative outputs from web content.
About This Workflow
This n8n workflow showcases advanced Large Language Model (LLM) chaining, specifically utilizing Anthropic's Claude models. It begins with a manual trigger and an HTTP request to fetch content from a blog. The core of the workflow involves orchestrating a series of Claude AI calls to perform distinct tasks: summarizing content, listing authors, identifying posts, and generating a humorous joke based on the page's information. These prompts are dynamically managed using a 'Set' node to define a system prompt and sequential instructions, which are then processed and split for individual LLM interaction. Memory is incorporated for potential future conversation management, and a merge node consolidates the outputs before they are potentially displayed or further processed. This workflow is a prime example of how to build complex AI-driven automation with n8n.
Key Features
- Multi-LLM Orchestration: Chain multiple calls to Anthropic's Claude models for sequential AI processing.
- Dynamic Prompt Management: Utilize n8n's 'Set' node to define and structure complex prompts and system instructions.
- Data Extraction and Summarization: Extract key information like authors and posts, and generate summaries from web pages.
- Creative AI Generation: Leverage LLMs for creative tasks, such as generating jokes based on provided content.
- Workflow Flexibility: Start with a manual trigger for easy testing and experimentation.
How To Use
- Trigger: Begin by clicking the 'Test workflow' button on the manual trigger node.
- Web Content Retrieval: The 'HTTP Request' node fetches content from
https://blog.n8n.io/. - Prompt Setup: Use the 'Initial prompts' node to define your
system_promptand sequentialstepinstructions for the LLMs. - Prompt Structuring: The 'Reshape' node transforms these prompts into a structured JSON array of
stepobjects, including thesystem_prompt. - Prompt Splitting: The 'Split Out' node separates the
system_promptand prepares the array of steps for sequential LLM calls. - LLM Interaction: Connect each 'Anthropic Chat Model' node to process a specific prompt instruction. Ensure your Anthropic credentials are set up.
- Output Merging: Use the 'Merge' node to combine the outputs from all LLM nodes.
- Memory Management (Optional): Configure the 'Simple Memory' and 'Clean memory' nodes to manage conversation history if needed.
- Output Formatting: The 'Markdown' node can be used to process and display the LLM outputs in a desired format.
Apps Used
Workflow JSON
{
"id": "30acf082-a73b-4596-b733-3a2d329000ff",
"name": "Mastering LLM Chaining with n8n and Anthropic Claude",
"nodes": 12,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 30acf082-a73b...
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