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Email parser for RAG agent powered by Gmail and Mem0

Stephan KoningStephan Koning
648 views
2/3/2026
Lead GenerationWebhookHubSpotForm SubmissionCRM
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This workflow contains community nodes that are only compatible with the self-hosted version of n8n.

**Alternatively, you can delete the community node and use the HTTP node instead. ** Most email agent templates are fundamentally broken. They're stateless—they have no long-term memory. An agent that can't remember past conversations is just a glorified auto-responder, not an intelligent system.

This workflow is Part 1 of building a truly agentic system: creating the brain.

Before you can have an agent that replies intelligently, you need a knowledge base for it to draw from. This system uses a sophisticated parser to automatically read, analyze, and structure every incoming email. It then logs that intelligence into a persistent, long-term memory powered by mem0.

The Problem This Solves

Your inbox is a goldmine of client data, but it's unstructured, and manually monitoring it is a full-time job. This constant, reactive work prevents you from scaling. This workflow solves that "system problem" by creating an "always-on" engine that automatically processes, analyzes, and structures every incoming email, turning raw communication into a single source of truth for growth.

How It Works

This is an autonomous, multi-stage intelligence engine. It runs in the background, turning every new email into a valuable data asset.

  1. Real-Time Ingest & Prep: The system is kicked off by the Gmail Trigger, which constantly watches your inbox. The moment a new email arrives, the workflow fires. That email is immediately passed to the Set Target Email node, which strips it down to the essentials: the sender's address, the subject, and the core text of the message (I prefer using the plain text or HTML-as-text for reliability). While this step is optional, it's a good practice for keeping the data clean and orderly for the AI.

  2. AI Analysis (The Brain): The prepared text is fed to the core of the system: the AI Agent. This agent, powered by the LLM of your choice (e.g., GPT-4), reads and understands the email's content. It's not just reading; it's performing analysis to:

    • Extract the core message.
    • Determine the sentiment (Positive, Negative, Neutral).
    • Identify potential red flags.
    • Pull out key topics and keywords.
    • The agent uses Window Buffer Memory to recall the last 10 messages within the same conversation thread, giving it the context to provide a much smarter analysis.

  3. Quality Control (The Parser): We don't trust the AI's first draft blindly. The analysis is sent to an Auto-fixing Output Parser. If the initial output isn't in a perfect JSON format, a second Parsing LLM (e.g., Mistral) automatically corrects it. This is our "twist" that guarantees your data is always perfectly structured and reliable.

  4. Create a Permanent Client Record: This is the most critical step. The clean, structured data is sent to mem0. The analysis is now logged against the sender's email address. This moves beyond just tracking conversations; it builds a complete, historical intelligence file on every person you communicate with, creating an invaluable, long-term asset.

Optional Use: For back-filling historical data, you can disable the Gmail Trigger and temporarily connect a Gmail "Get Many" node to the Set Target Email node to process your backlog in batches.

Setup Requirements

To deploy this system, you'll need the following:

  • An active n8n instance.
  • Gmail API credentials.
  • An API key for your primary LLM (e.g., OpenAI).
  • An API key for your parsing LLM (e.g., Mistral AI).
  • An account with mem0.ai for the memory layer.

Email Parser for RAG Agent Powered by Gmail and Mem0

This n8n workflow automates the process of extracting information from incoming emails and feeding it into a Retrieval Augmented Generation (RAG) agent for further processing or knowledge base updates. It leverages Gmail to trigger on new emails and an AI Agent (powered by Langchain) to parse and understand the email content.

What it does

This workflow performs the following key steps:

  1. Triggers on new Gmail messages: It continuously monitors a specified Gmail account for new incoming emails.
  2. Passes email content to an AI Agent: The full content of the new email is sent to an AI Agent powered by Langchain.
  3. Processes with an OpenAI Chat Model: The AI Agent utilizes an OpenAI Chat Model (or optionally a Mistral Cloud Chat Model) to understand and extract relevant information from the email.
  4. Maintains conversational context with Simple Memory: The AI Agent uses a Simple Memory buffer to maintain conversational context, which can be useful for follow-up emails or multi-turn interactions.
  5. Ensures structured output with Auto-fixing and Structured Output Parsers: It employs both an Auto-fixing Output Parser and a Structured Output Parser to ensure the AI's response is well-formatted and adheres to a predefined structure, making it easier for subsequent systems to consume.
  6. Prepares data for further processing: The extracted and structured information is then prepared for potential further actions, such as sending to a RAG system (e.g., Mem0) or another knowledge base.
  7. Placeholder for HTTP Request: Includes an HTTP Request node, likely intended for sending the parsed data to an external service or API (e.g., Mem0).
  8. Placeholder for data manipulation: Includes an "Edit Fields (Set)" node, which can be used to transform or filter the data before sending it to the final destination.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Gmail Account: A Gmail account configured as a credential in n8n.
  • OpenAI API Key: An OpenAI API key configured as a credential in n8n for the OpenAI Chat Model.
  • Langchain Integration: The Langchain nodes installed in your n8n instance.
  • (Optional) Mistral Cloud API Key: If you choose to use the Mistral Cloud Chat Model, you'll need a Mistral Cloud API key configured in n8n.
  • Target RAG System/API: An endpoint for your RAG agent (e.g., Mem0) or other system where the parsed email data will be sent (this will be configured in the HTTP Request node).

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Gmail Trigger:
    • Click on the "Gmail Trigger" node.
    • Select or create your Gmail credential.
    • Configure the trigger settings (e.g., folder to watch, filters for specific emails).
  3. Configure AI Agent:
    • Click on the "AI Agent" node.
    • Ensure the "OpenAI Chat Model" (or "Mistral Cloud Chat Model" if preferred) is correctly configured with your API key.
    • Review and adjust the agent's prompt and instructions to guide it on what information to extract from the emails.
  4. Configure Output Parsers:
    • Review the "Auto-fixing Output Parser" and "Structured Output Parser" nodes.
    • Define the desired JSON schema in the "Structured Output Parser" to ensure the AI's output matches the expected format for your RAG agent.
  5. Configure Edit Fields (Set) node:
    • Modify the "Edit Fields" node to transform or select specific data fields from the AI agent's output that you want to send to your RAG system.
  6. Configure HTTP Request node:
    • Click on the "HTTP Request" node.
    • Set the URL to your RAG agent's API endpoint (e.g., Mem0 API).
    • Set the Method (e.g., POST).
    • Configure the Headers (e.g., Content-Type: application/json, Authorization if required).
    • In the Body section, map the output from the "Edit Fields" node to the format expected by your RAG agent.
  7. Activate the workflow: Once all credentials and configurations are set, activate the workflow to start processing incoming emails automatically.

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