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Extract and self-correct meeting action items with OpenRouter and webhooks

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1. Workflow Overview

Quick Overview This webhook driven workflow extracts structured action items from meeting notes using an OpenRouter chat model, validates the JSON against a strict schema, and iteratively self corr...

Best for

  • Document Extraction automation workflows
  • AI Summarization automation workflows
  • intermediate n8n builders looking for reusable templates

Tools used

n8n-nodes-base.webhook, n8n-nodes-base.code, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.memorybufferwindow, n8n-nodes-base.if, n8n-nodes-base.respondtowebhook, @n8n/n8n-nodes-langchain.lmchatopenrouter, n8n-nodes-base.stickynote

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Elvis Sarvia.

Original n8n.io source

1.1 Workflow description

Title
Extract and self-correct meeting action items with OpenRouter and webhooks
Workflow name
Extract and self-correct meeting action items with OpenRouter and webhooks

Quick Overview

This webhook-driven workflow extracts structured action items from meeting notes using an OpenRouter chat model, validates the JSON against a strict schema, and iteratively self-corrects with feedback while using session-based window memory to keep IDs consistent across requests.

How it works

  1. Receives a POST webhook request containing meeting notes, a sessionId, and optional maxAttempts.
  2. Normalizes the payload into a standard state object (request/session identifiers, attempt counters, and placeholders for extraction and validation results).
  3. Uses an OpenRouter chat model with session-keyed window buffer memory to extract action items as strict JSON (id, title, assignee, deadline, priority, context).
  4. Parses the model output and validates each action item against required fields, deadline format, allowed priority values, and context length.
  5. If validation fails and attempts remain, feeds the numbered validation errors back into the next extraction attempt and retries until it passes or reaches maxAttempts.
  6. Returns a JSON response to the webhook caller with the validated action items on success, or failure details (errors, attempts used, and last raw extraction) for human review.

Setup

  1. Add an OpenRouter API credential and select it in the OpenRouter Chat Model configuration.
  2. Activate the workflow and copy the webhook URL from the Webhook trigger.
  3. Configure your source system to POST to the webhook with meetingNotes and sessionId (and optionally maxAttempts as a number).

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Webhook - Meeting Notes

Type / Role
n8n-nodes-base.webhook - webhook
Config choices
Version 2

Block 2 - Normalize Request

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 3 - Extraction Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 1.7

Block 4 - Window Buffer Memory

Type / Role
@n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices
Version 1.3

Block 5 - Parse + Validate

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 6 - Validated or Max Attempts?

Type / Role
n8n-nodes-base.if - if
Config choices
Version 2.2

Block 7 - Finalize Response

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 8 - Respond to Client

Type / Role
n8n-nodes-base.respondToWebhook - respondToWebhook
Config choices
Version 1.1

Block 9 - Increment + Feedback

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 10 - OpenRouter Chat Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 11 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

3. Summary Table

Workflow Extract and self-correct meeting action items with OpenRouter and webhooks
Complexity intermediate
Nodes 11
Categories Document Extraction, AI Summarization
Author Elvis Sarvia
Published 28 May 2026

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/15995/15995.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Extract and self-correct meeting action items with OpenRouter and webhooks do?

Quick Overview This webhook driven workflow extracts structured action items from meeting notes using an OpenRouter chat model, validates the JSON against a strict schema, and iteratively self corr...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI Summarization use case.