WhatsApp to Chatwoot message forwarder with media support
Description Automates the forwarding of messages from WhatsApp (via Evolution API) to Chatwoot, enabling seamless integration between external WhatsApp users and internal Chatwoot agents. It supports both text and media messages, ensuring that customer conversations are centralized and accessible for support teams. What Problem Does This Solve? Managing conversations across multiple platforms can lead to fragmented support and lost context. This subworkflow bridges the gap between WhatsApp and Chatwoot, automatically forwarding messages received via the Evolution API to a Chatwoot inbox. It simplifies communication flow, centralizes conversations, and enhances the support team's productivity. Features Support for plain text messages Support for media messages: images, videos, documents, and audio Automatic media upload to Chatwoot with proper attachment rendering Automatic contact association using WhatsApp number and Chatwoot API Designed to work with Evolution API webhooks or any message source Prerequisites Before using this automate, make sure you have: Evolution API credentials with incoming message webhook configured A Chatwoot instance with access token and API endpoint An existing Chatwoot inbox (preferably API channel) A configured HTTP Request node in n8n for Chatwoot API calls Suggested Usage This subworkflow should be attached to a parent workflow that receives WhatsApp messages via the Evolution API webhook. Ideal for: Centralized customer service operations WhatsApp-to-CRM/chat routing Hybrid automation workflows where human agents need to reply from Chatwoot It ensures that all incoming WhatsApp messages are properly converted and forwarded to Chatwoot, preserving message content and structure.
By Thiago Vazzoler LoureiroAuto-assign support tickets with JIRA, Supabase and AI
This n8n template builds a simple automation to ensure no JIRA issues go unassigned for more than a week to prevent them falling through the cracks. It uses AI to perform searching tasks against a Supabase Vector Store. This can be one way to help reduce the amount of manual work in managing the issue backlog for busy teams with little effort. How it works This template contains 2 separate flows which run continuously via schedule triggers. The first populates our Supabase vector store with resolved issues within the last day. This helps keep our vector store up-to-date and relevant for the purpose of finding similar issues. It does this by pulling the latest resolved issues from JIRA and populating the Supabase vectorstore with carefully chosen metadata. This will come in handy later. The second flow watches for stale, unassigned issues for the purpose of aut-assigning to a relevant team member. It does this by comparing the stale issue against our vector store of resolved issues with the goal of identifying which team member would have best context regarding the issue. In a busy team, this may net a few team members as possible candidates to assign. Therefore, we can introduce additional logic to count each team member's assigned, in-progress issues. This is intended to not overload our busiest members. The team member with the least assigned issues is pressumed to have the most capacity and therefore is assigned. A comennt is left in the issue to notify the team member that they've been auto-assigned due to age of issue. How to use Modify the project and interval parameters to match those of your use-case and team members. Add additional criteria before assigning to a team member eg. department, as required. Requirements OpenAI for LLM JIRA for Issue Management Supabase for Vector Store Customising this workflow Not using JIRA or Supabase? The beauty of these AI templates are these components are entirely interchangeable with competing services. Try Linear and Qdrant instead! Auto-assigning logic is simplified in this template. Expand criteria as required for your team and organisation. eg. Might be a good idea to pull in annual leave information from HR system to prevent assigning to someone who is on currently on holiday!
By JimleukParse CVs from emails with OCR & GPT for Notion database
It allows you to automate candidate retrieval and onboarding in your HR processes. How it works It monitors a Gmail address for new emails with a PDF attachment It expects the PDF to be a candidate’s CV, extracts the text using OCR, and then structures the data using ChatGPT Once the data is processed, it connects to Notion and adds (or updates) an entry in the specified database How to use Configure your Gmail account and provide your ChatGPT API key Provide an API key for the OCR service in a variable named OCRSPACEAPI_KEY Connect your Notion account Once everything is configured, the workflow will monitor your inbox for new emails. Just send an email with a PDF attachment to the configured address Requirements In addition to Gmail, ChatGPT, and Notion, the system uses a third-party OCR API (OCR SPACE). You’ll need to create an account and obtain an API key You must map the fields returned by ChatGPT to the Notion database, or use the same field names we are using Customising It should be easy to replace Notion with PostgreSQL or another database if needed
By Blue CodeTrack Pipedrive deals in Google Sheets for sales pipeline reporting
This workflow pulls deals from Pipedrive, categorizes them by stage, and logs them into a Google Sheet for reporting and tracking. --- ⚙️ Setup Instructions 1️⃣ Connect Pipedrive In Pipedrive → Personal preferences → API → copy your API token URL shortcut: https://{your-company}.pipedrive.com/settings/personal/api In n8n → Credentials → New → Pipedrive API Company domain: {your-company} (the subdomain in your Pipedrive URL) API Token: paste the token from step 1 → Save In the Pipedrive Tool node, select your Pipedrive credential and (optionally) set filters (e.g., owner, label, created time). --- 2️⃣ Prepare Your Google Sheet Connect your Data in Google Sheets Use this format: Sample Sheet Row 1 = column names In n8n, create credentials: Google Sheets (OAuth2) Log in with your Google account and select your Spreadsheet + Worksheet --- 🧠 How it works Get many deals (Pipedrive): Fetches all deals with stage IDs. Categorize Stages: Maps stage IDs → friendly stage names (Prospecting, Qualified, Proposal, Negotiation, Closed Won). Today's Date: Adds a date stamp to each run. Set Fields: Combines stage, deal name, and date into clean columns. Google Sheets (Append): Writes all rows to your reporting sheet. --- 📬 Contact Need help customizing this (e.g., pulling only active deals, calculating win-rates, or sending dashboards)? 📧 robert@ynteractive.com 🔗 Robert Breen 🌐 ynteractive.com
By Robert BreenCreate a knowledge base chatbot with OpenAI and Notion for website embedding
This workflow creates an AI chatbot that uses your Notion database as a knowledge base and allows for website embedding (e.g., as a customer support chatbot). The AI agent can automatically query your Notion pages and databases to provide accurate, contextual responses based on your stored content. Who's it for This template is perfect for: Support teams who maintain their knowledge base in Notion Content creators and teams managing documentation in Notion databases Businesses looking to make their Notion workspace conversationally accessible Anyone who wants to turn their Notion content into an interactive AI assistant How it works The workflow combines OpenAI's language model with Notion's database capabilities to create a smart chatbot. When users ask questions, the AI agent automatically determines which Notion pages or database entries are relevant and uses that content to generate helpful responses. The system maintains conversation history for natural, contextual interactions. How to set up Add your credentials: Configure your Notion API integration in the Set & Get Notion Database node Set up your OpenAI API credentials in the OpenAI Chat Model node Configure your Notion connection: Click the Set & Get Notion Database node Select your Notion database or page containing your knowledge base content The AI will automatically determine relevant pages - no need to specify individual page IDs Customize the AI model: Open the OpenAI Chat Model node Choose your preferred model (GPT-4, GPT-3.5-turbo, etc.) Adjust token limits if needed Test the chatbot: Click the Chat button to start a conversation Ask questions related to your Notion content Optional - Make it public: Enable public access in the Chat Trigger node Embed the provided code into your website Requirements n8n instance (cloud or self-hosted) Notion workspace with databases or pages you want to query OpenAI API key with available credits Notion API integration with proper permissions How to customize the workflow Change the AI Provider: You can replace the OpenAI Chat Model with other providers like Anthropic Claude, Google Gemini, or local models by swapping the language model node. Adjust Context Window: Modify the "Remember Chat History" node to increase or decrease how many previous messages the AI remembers (default is 10 interactions). Update System Instructions: Edit the Smart AI Agent's system message to change how the assistant behaves or add specific instructions for your use case. Connect Multiple Databases: Add additional Notion Database nodes to give the AI access to multiple databases or pages within your workspace. Add More Tools: Extend the AI agent with additional tools like web search, email sending, or integration with other services. Workflow Structure Chat Trigger → Smart AI Agent ← OpenAI Chat Model ↓ Set & Get Notion Database ↑ Remember Chat History The Smart AI Agent orchestrates the conversation, deciding when to query Notion and how to use the retrieved content in responses. The memory buffer ensures natural conversation flow by maintaining context across interactions.
By GegenfeldAI-powered customer feedback routing with Gmail, Slack, Pipedrive, Zendesk & Notion
Who’s it for This workflow is built for B2B SaaS and CX teams that are drowning in unstructured customer feedback across tools. It’s ideal for Customer Success, Product and Support leaders who want a light “voice of customer engine” without rebuilding their stack: Gmail for interactions, Slack for conversations, Pipedrive for notes and Zendesk for tickets, plus Notion for follow-up tasks. How it works / What it does The workflow runs on a schedule or manual trigger and first sets the CSM’s email address. It then uses an AI “Data agent” to pull recent customer signals from multiple sources: Gmail messages, Slack messages, Pipedrive notes and Zendesk tickets. A “Signals agent” compresses each piece of feedback into a concise, neutral summary, which is then grouped by topic via a “Clustering agent”. Each cluster gets a label, count and examples. Finally, an “Action agent” routes clusters based on their label: Create Zendesk tickets for product/performance issues Post to a dedicated Slack channel for billing / contract topics Create Notion tasks for sales-related feedback Send targeted Gmail messages to the CSM for high-risk or engagement-related items How to set up Import the workflow into n8n. Connect credentials for Gmail, Slack, Pipedrive, Zendesk, Notion and OpenAI. Update the CSM email in the “Set CSM email” node. Adjust date filters, send-to addresses and Slack channel IDs as needed. Enable the schedule trigger for weekly or daily digests. Requirements Active accounts & credentials for: Gmail, Slack, Pipedrive, Zendesk and Notion OpenAI (or compatible) API key for the LLM node At least one Slack channel for posting feedback (e.g. billing-feedback) How to customize the workflow Change the time window or filters (sender, channel, query) for each data source. Edit the clustering and routing prompts to match your own categories and teams. Add new destinations (e.g. Jira, HubSpot) by connecting more tools to the Action agent. Modify thresholds (e.g. minimum count) before a cluster triggers an action. Localize labels and email copy to your team’s language and tone.
By PollupAI