Recursive Multi-Agent Writing and Editing Workflow
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This n8n workflow automates a recursive writing and editing process using AI agents. It allows for iterative refinement of content through a loop of writing, editing, and incorporation of feedback.
About This Workflow
The Recursive Multi-Agent Writing and Editing Workflow is an advanced automation designed to leverage the power of AI for content creation and refinement. It starts with an initial chat message triggering a writing agent to produce a blurb based on user input and optional edits. This output is then passed to an editing agent that analyzes the text, provides feedback in the form of suggested edits, and determines if the status is 'complete' or 'incomplete'. If the status is 'incomplete', the edits are incorporated into the writing agent's next iteration. This cycle continues until the editing agent deems the content satisfactory, ensuring high-quality, polished output through intelligent, iterative refinement.
Key Features
- Automated Content Generation: Leverages AI to write initial blurbs based on user prompts.
- Iterative Editing Loop: Features a recursive process where content is reviewed and refined by an editing agent.
- Smart Edits Incorporation: Automatically integrates suggested edits from the editing agent into subsequent writing passes.
- Structured Output: Utilizes structured output parsing to ensure predictable and usable data from AI agents.
- Configurable Memory: Employs a window buffer memory to maintain conversational context across iterations.
How To Use
- Trigger Setup: Configure the
When chat message receivednode to listen for incoming chat messages that will initiate the workflow. - Initial Input: The
chatInputnode captures the user's initial prompt for the writing agent. - Writing Agent Configuration: Set up the
Writing Agentnode with your desired prompt, system message, and the topic input fromchatInput. - Editing Agent Configuration: Configure the
Editing Agentto analyze theWriting Agent's output. Define the JSON schema for its output, includingstatus(complete/incomplete) andedits. - Memory Management: Connect the
Window Buffer Memorynode to maintain conversation history, linking it to the chat session ID. - Conditional Logic: Use the
If Status Completenode to check thestatusoutput from theEditing Agent. - Output Handling: If the status is 'complete', the
chatOutputnode captures the final blurb from theWriting Agent. - Variable Setting: The
set variablesnode captures thestatusandeditsfrom theEditing Agentfor potential further processing or debugging.
Apps Used
Workflow JSON
{
"id": "ab55da1b-2e8a-401d-8171-4841c44f8082",
"name": "Recursive Multi-Agent Writing and Editing Workflow",
"nodes": 16,
"category": "Operations",
"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: ab55da1b-2e8a...
About the Author
SaaS_Connector
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Connecting CRM, Notion, and Slack to automate your life.
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