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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.
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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.
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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.
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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.
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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.
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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.