💬 Route Chatwoot customer chats to AI and live agents with Groq and Gemini
⚡ 544 views · 💬 Support Chatbots
Description
Quick overview
This workflow automates customer chat with Chatwoot & n8n AI agent that handles incoming chats, qualifies leads, answers FAQs from Pinecone knowledge base, and escalates to a live human agent when one is online. Works with any Chatwoot inbox: web widget, WhatsApp, Telegram, and more.
How it works
- Receives a POST webhook from Chatwoot when a message is created in a conversation.
- Extracts message and sender fields into a clean structure, then ignores outgoing messages to prevent the bot from replying to its own messages in an infinite loop.
- Stops processing if the Chatwoot conversation already has a human assignee (or is escalated to a human during chat) to avoid interrupting live support.
- Fetches available Chatwoot agents, selects a random online agent (if any), and passes the availability status to the AI assistant.
- Generates a response using Groq (chat model) with Postgres (Superbase) chat history, optionally retrieving answers from a Pinecone knowledge base using Google Gemini embeddings and chat completions.
- When escalation is required, assigns the selected Chatwoot agent to the conversation or sends an internal summary email via Gmail.
- Splits the AI output into short chunks and posts them back to the Chatwoot conversation for human-like short replies
Setup
- Chatwoot Agent Bot: In Chatwoot SuperAdmin Console → Agent Bots, create a new bot, and paste the n8n Webhook URL as Outgoing URL. Assign the bot to your inbox(es) in Bot Configuration settings of those inboxes.
- Credentials: Add credentials for Groq API, PostgreSQL (Superbase), Pinecone API, Google AI Studio (Gemini embeddings/chat), and Gmail OAuth2.
- Chatwoot config: Update the Chatwoot base URL and API access token in Chatwoot HTTP requests and the reply-sending code, and set your internal notification email address in the Gmail step.
- Knowledge base: Create and populate a Pinecone index (and namespace) for your FAQs/knowledge documents, then update the index/namespace values in the Pinecone vector store configuration.
Requirements
- Chatwoot instance (self-hosted or Chatwoot Cloud) with at least one active inbox.
- Groq API key, or feel free to use any other AI Provider
- PostgreSQL database for conversation memory (Supabase free tier works well)
- Pinecone account with an index populated with your FAQ documents
- Google AI Studio API key for Gemini embeddings
- Gmail account with OAuth2 configured for internal lead notifications
Customization
- Swap the LLM: Replace the Groq Chat Model node with any LangChain-compatible model — OpenAI, Anthropic Claude, Google Gemini, or a local Ollama model
- Swap the memory store: Replace Postgres Chat Memory with Redis or n8n’s In-Memory store for lighter setups
- Swap the vector store: Replace Pinecone with Supabase Vector, or if you prefer a different provider
- Add or edit tools to the agent: Extend with a Google Calendar node for appointment booking, a HubSpot/CRM node for automatic lead creation, or any HTTP Request tool for custom APIs
- Adjust reply chunking: Change the 300ms delay in the Send Reply node or the split pattern to control message pacing
Additional info
This template is production-tested and was built to solve a real problem: most Chatbot AI integrations are basic single-node setups that break when including multiple sources like Whatsapp, Live Chat, Telegram etc. for a seamless experience, and have long answers that don’t feel human-like.
This workflow handles these — through Chatwoot as a medium to connect multiple inboxes and using n8n to power those with Back-end automation, this workflow can help users handle incoming chats across multiple platforms. Plus, it handles human handovers, and only replies when there’s no human assigned to the chat.
🔗 Nodes Used
HTTP Request, Webhook, AI Agent, Pinecone Vector Store, Embeddings Google Gemini, Google Gemini Chat Model
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File