AI Personal Assistant
Email Personal Assistant - Comprehensive Communication Manager
This automation flow is designed to proactively monitor email, calendar, and Slack communications, analyze priorities across all channels, and generate a comprehensive daily briefing with actionable tasks for executive productivity management.
⚙️ How It Works (Step-by-Step):
- ⏰ Automated Daily Trigger Runs automatically on weekdays:
Scheduled execution every weekday at 8:00 AM
Manual trigger available for on-demand analysis
Comprehensive daily communication audit
- 📧 Email Assistant Agent Analyzes inbox priorities and context:
Scans unread emails across "To Respond" and "FYI" labels
Checks email history to determine relationship context
Identifies *company-related opportunities and partnerships
Categorizes emails by urgency (High, Medium, Low)
Cross-references with sent emails for follow-up context
- 📅 Follow-Up Assistant Agent Monitors meeting follow-up requirements:
Reviews last 3 days of calendar meetings
Fetches Fireflies transcripts for recorded sessions
Identifies meetings without post-meeting communication
Flags meetings requiring action items or follow-ups
Checks sent emails and Slack for completed follow-ups
- 💬 Slack Assistant Agent Tracks Slack communication priorities:
Monitors direct messages and @mentions
Identifies unreplied Slack conversations
Cross-references with email and calendar context
Prioritizes responses based on sender importance
Checks for threaded conversations requiring attention
- 🎯 Master Orchestrator Agent Synthesizes all communication data:
Combines reports from all three assistant agents
Cross-references with existing Google Sheets to-do list
Prioritizes tasks by urgency and business impact
Identifies correlations between different communication channels
Creates comprehensive daily action plan
- 📊 Task Management Integration Automated tracking and delivery:
Appends new tasks to Google Sheets to-do tracker
Sends personalized daily briefing via Slack DM
Maintains conversation memory for context continuity
Tracks outstanding vs. completed items
🛠️ Tools Used:
n8n: Workflow orchestration and scheduling
Claude Sonnet 4 & Opus 4: Multi-agent AI analysis
Gmail API: Email monitoring and history checking
Google Calendar: Meeting tracking and scheduling
Slack API: Message monitoring and user management
Fireflies API: Meeting transcript analysis
Google Sheets: Task tracking and persistence
📦 Key Features:
Multi-channel communication monitoring (Email, Calendar, Slack)
AI-powered priority assessment and context analysis
Cross-platform relationship tracking and history
Automated daily briefing generation and delivery
Persistent task tracking with Google Sheets integration
Meeting follow-up verification and flagging
Conversation memory for continuity across sessions
🚀 Ideal Use Cases:
C-level executives managing multiple communication channels
Sales leaders tracking prospect interactions and follow-ups
Business development professionals managing partnerships
Busy professionals needing communication prioritization
Teams requiring systematic follow-up management
Anyone wanting automated daily productivity briefings
n8n AI Personal Assistant Workflow
This n8n workflow demonstrates a basic setup for an AI-powered personal assistant that can interact with Google Sheets and Slack, leveraging an AI Agent with memory and a chat model.
What it does
This workflow showcases the core components for building an AI assistant that can:
- Trigger Manually or on Schedule: The workflow can be initiated either by a manual execution or on a predefined schedule.
- Utilize an AI Agent: An AI Agent acts as the brain of the assistant, capable of understanding requests and planning actions.
- Maintain Conversation Context: A "Simple Memory" component allows the AI Agent to remember previous interactions within a conversation, providing a more natural and continuous experience.
- Process Language with Anthropic Chat Model: The AI Agent uses an Anthropic Chat Model (e.g., Claude, Sonnet, Opus) for natural language understanding and generation.
- Interact with Google Sheets: The workflow includes a Google Sheets node, indicating the AI Agent can be configured to read from or write to spreadsheets.
- Communicate via Slack: A Slack node is present, suggesting the AI Agent can send messages or notifications to Slack channels.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running n8n instance.
- Anthropic API Key: For the Anthropic Chat Model.
- Google Account: With access to Google Sheets, and appropriate n8n credentials configured.
- Slack Account: With a Slack App and Bot Token for sending messages, and appropriate n8n credentials configured.
Setup/Usage
- Import the Workflow: Import the provided JSON into your n8n instance.
- Configure Credentials:
- Set up your Anthropic API Key credential for the "Anthropic Chat Model" node.
- Configure your Google Sheets credential for the "Google Sheets" node.
- Configure your Slack credential for the "Slack" node.
- Customize AI Agent:
- Open the "AI Agent" node and configure its prompt, tools (e.g., to interact with Google Sheets and Slack), and other settings to define its capabilities.
- Configure Memory: The "Simple Memory" node is already included to provide conversational context. You might need to link it correctly to the AI Agent if not already done.
- Activate Trigger:
- Choose between the "Manual Trigger" for on-demand execution or configure the "Schedule Trigger" for automated runs (e.g., every hour, daily).
- Enable and Execute: Save the workflow, activate it, and then execute it manually or wait for the scheduled trigger.
Related Templates
Automate Dutch Public Procurement Data Collection with TenderNed
TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte
By Wessel Bulte🎓 How to transform unstructured email data into structured format with AI agent
This workflow automates the process of extracting structured, usable information from unstructured email messages across multiple platforms. It connects directly to Gmail, Outlook, and IMAP accounts, retrieves incoming emails, and sends their content to an AI-powered parsing agent built on OpenAI GPT models. The AI agent analyzes each email, identifies relevant details, and returns a clean JSON structure containing key fields: From – sender’s email address To – recipient’s email address Subject – email subject line Summary – short AI-generated summary of the email body The extracted information is then automatically inserted into an n8n Data Table, creating a structured database of email metadata and summaries ready for indexing, reporting, or integration with other tools. --- Key Benefits ✅ Full Automation: Eliminates manual reading and data entry from incoming emails. ✅ Multi-Source Integration: Handles data from different email providers seamlessly. ✅ AI-Driven Accuracy: Uses advanced language models to interpret complex or unformatted content. ✅ Structured Storage: Creates a standardized, query-ready dataset from previously unstructured text. ✅ Time Efficiency: Processes emails in real time, improving productivity and response speed. *✅ Scalability: Easily extendable to handle additional sources or extract more data fields. --- How it works This workflow automates the transformation of unstructured email data into a structured, queryable format. It operates through a series of connected steps: Email Triggering: The workflow is initiated by one of three different email triggers (Gmail, Microsoft Outlook, or a generic IMAP account), which constantly monitor for new incoming emails. AI-Powered Parsing & Structuring: When a new email is detected, its raw, unstructured content is passed to a central "Parsing Agent." This agent uses a specified OpenAI language model to intelligently analyze the email text. Data Extraction & Standardization: Following a predefined system prompt, the AI agent extracts key information from the email, such as the sender, recipient, subject, and a generated summary. It then forces the output into a strict JSON structure using a "Structured Output Parser" node, ensuring data consistency. Data Storage: Finally, the clean, structured data (the from, to, subject, and summarize fields) is inserted as a new row into a specified n8n Data Table, creating a searchable and reportable database of email information. --- Set up steps To implement this workflow, follow these configuration steps: Prepare the Data Table: Create a new Data Table within n8n. Define the columns with the following names and string type: From, To, Subject, and Summary. Configure Email Credentials: Set up the credential connections for the email services you wish to use (Gmail OAuth2, Microsoft Outlook OAuth2, and/or IMAP). Ensure the accounts have the necessary permissions to read emails. Configure AI Model Credentials: Set up the OpenAI API credential with a valid API key. The workflow is configured to use the model, but this can be changed in the respective nodes if needed. Connect the Nodes: The workflow canvas is already correctly wired. Visually confirm that the email triggers are connected to the "Parsing Agent," which is connected to the "Insert row" (Data Table) node. Also, ensure the "OpenAI Chat Model" and "Structured Output Parser" are connected to the "Parsing Agent" as its AI model and output parser, respectively. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. The triggers will begin polling for new emails according to their schedule (e.g., every minute), and the automation will start processing incoming messages. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.
By DavideDynamic Hubspot lead routing with GPT-4 and Airtable sales team distribution
AI Agent for Dynamic Lead Distribution (HubSpot + Airtable) 🧠 AI-Powered Lead Routing and Sales Team Distribution This intelligent n8n workflow automates end-to-end lead qualification and allocation by integrating HubSpot, Airtable, OpenAI, Gmail, and Slack. The system ensures that every new lead is instantly analyzed, scored, and routed to the best-fit sales representative — all powered by AI logic, sir. --- 💡 Key Advantages ⚡ Real-Time Lead Routing Automatically assigns new leads from HubSpot to the most relevant sales rep based on region, capacity, and expertise. 🧠 AI Qualification Engine An OpenAI-powered Agent evaluates the lead’s industry, region, and needs to generate a persona summary and routing rationale. 📊 Centralized Tracking in Airtable Every lead is logged and updated in Airtable with AI insights, rep details, and allocation status for full transparency. 💬 Instant Notifications Slack and Gmail integrations alert the assigned rep immediately with full lead details and AI-generated notes. 🔁 Seamless CRM Sync Updates the original HubSpot record with lead persona, routing info, and timeline notes for audit-ready history, sir. --- ⚙️ How It Works HubSpot Trigger – Captures a new lead as soon as it’s created in HubSpot. Fetch Contact Data – Retrieves all relevant fields like name, company, and industry. Clean & Format Data – A Code node standardizes and structures the data for consistency. Airtable Record Creation – Logs the lead data into the “Leads” table for centralized tracking. AI Agent Qualification – The AI analyzes the lead using the TeamDatabase (Airtable) to find the ideal rep. Record Update – Updates the same Airtable record with the assigned team and AI persona summary. Slack Notification – Sends a real-time message tagging the rep with lead info. Gmail Notification – Sends a personalized handoff email with context and follow-up actions. HubSpot Sync – Updates the original contact in HubSpot with the assignment details and AI rationale, sir. --- 🛠️ Setup Steps Trigger Node: HubSpot → Detect new leads. HubSpot Node: Retrieve complete lead details. Code Node: Clean and normalize data. Airtable Node: Log lead info in the “Leads” table. AI Agent Node: Process lead and match with sales team. Slack Node: Notify the designated representative. Gmail Node: Email the rep with details. HubSpot Node: Update CRM with AI summary and allocation status, sir. --- 🔐 Credentials Required HubSpot OAuth2 API – To fetch and update leads. Airtable Personal Access Token – To store and update lead data. OpenAI API – To power the AI qualification and matching logic. Slack OAuth2 – For sending team notifications. Gmail OAuth2 – For automatic email alerts to assigned reps, sir. --- 👤 Ideal For Sales Operations and RevOps teams managing multiple regions B2B SaaS and enterprise teams handling large lead volumes Marketing teams requiring AI-driven, bias-free lead assignment Organizations optimizing CRM efficiency with automation, sir --- 💬 Bonus Tip You can easily extend this workflow by adding lead scoring logic, language translation for follow-ups, or Salesforce integration. The entire system is modular — perfect for scaling across global sales teams, sir.
By MANISH KUMAR