Search Outlook emails with natural language queries using GPT-4o
This workflow allows you to search your Outlook mailbox with natural language. You type what you’re looking for (e.g., “invoice from last week”), and the workflow:
- Uses OpenAI to generate a search query.
- Searches Outlook emails and ranks them by relevance.
- Outputs results in a simple table with score + link to each email.
👉 The number of results can be changed (default: 5).
⚙️ Setup Instructions
1️⃣ Set Up OpenAI Connection
- Go to OpenAI Platform
- Navigate to OpenAI Billing
- Add funds to your billing account
- Copy your API key into the OpenAI credentials in n8n
2️⃣ Set Up Outlook Connection
- In n8n → Credentials → New → Microsoft Outlook OAuth2 API
- Log in with your Outlook account & approve access
- Attach this credential to the Search Outlook node in the workflow
- (Optional) Change the
limitparameter in the node if you want more/less than 5 results
📬 Contact Information
Need help customizing this workflow or building similar automations?
Search Outlook Emails with Natural Language Queries Using GPT-4o
This n8n workflow allows you to search your Microsoft Outlook emails using natural language queries, powered by an AI Agent (GPT-4o). It simplifies finding specific emails by understanding your conversational requests and translating them into actionable search parameters.
What it does
This workflow automates the following steps:
- Listens for a chat message: It acts as a conversational interface, waiting for a user's natural language query.
- Initializes an AI Agent: Sets up an AI agent (using GPT-4o) with a simple memory to understand the context of the conversation.
- Defines a structured output for the AI: Instructs the AI to extract specific search parameters (like sender, subject, body keywords, date range) from the natural language query.
- Searches Microsoft Outlook: Uses the extracted parameters to perform a targeted search within your Outlook emails.
- Aggregates search results: Collects all found emails into a single output.
- Merges results: (Though not explicitly connected in the provided JSON, a Merge node is present, implying a potential for combining results from multiple sources or refining the output.)
Prerequisites/Requirements
To use this workflow, you will need:
- n8n instance: A running n8n instance.
- Microsoft Outlook Account: Configured as a credential in n8n.
- OpenAI API Key: To use the GPT-4o chat model, configured as a credential in n8n.
Setup/Usage
- Import the workflow: Download the provided JSON and import it into your n8n instance.
- Configure Credentials:
- Set up your Microsoft Outlook credential.
- Set up your OpenAI API Key credential.
- Activate the workflow: Once credentials are set, activate the workflow.
- Send a chat message: Use the "When chat message received" trigger to initiate a query. For example, send a message like:
- "Find emails from John Doe about project updates last week."
- "Show me emails with 'invoice' in the subject from January 2023."
- "What emails did I get from Sarah Miller today?"
The AI Agent will interpret your query, search Outlook, and return the relevant email results.
Related Templates
Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax
Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions
AI-powered code review with linting, red-marked corrections in Google Sheets & Slack
Advanced Code Review Automation (AI + Lint + Slack) Who’s it for For software engineers, QA teams, and tech leads who want to automate intelligent code reviews with both AI-driven suggestions and rule-based linting — all managed in Google Sheets with instant Slack summaries. How it works This workflow performs a two-layer review system: Lint Check: Runs a lightweight static analysis to find common issues (e.g., use of var, console.log, unbalanced braces). AI Review: Sends valid code to Gemini AI, which provides human-like review feedback with severity classification (Critical, Major, Minor) and visual highlights (red/orange tags). Formatter: Combines lint and AI results, calculating an overall score (0–10). Aggregator: Summarizes results for quick comparison. Google Sheets Writer: Appends results to your review log. Slack Notification: Posts a concise summary (e.g., number of issues and average score) to your team’s channel. How to set up Connect Google Sheets and Slack credentials in n8n. Replace placeholders (<YOURSPREADSHEETID>, <YOURSHEETGIDORNAME>, <YOURSLACKCHANNEL_ID>). Adjust the AI review prompt or lint rules as needed. Activate the workflow — reviews will start automatically whenever new code is added to the sheet. Requirements Google Sheets and Slack integrations enabled A configured AI node (Gemini, OpenAI, or compatible) Proper permissions to write to your target Google Sheet How to customize Add more linting rules (naming conventions, spacing, forbidden APIs) Extend the AI prompt for project-specific guidelines Customize the Slack message formatting Export analytics to a dashboard (e.g., Notion or Data Studio) Why it’s valuable This workflow brings realistic, team-oriented AI-assisted code review to n8n — combining the speed of automated linting with the nuance of human-style feedback. It saves time, improves code quality, and keeps your team’s review history transparent and centralized.
Auto-reply & create Linear tickets from Gmail with GPT-5, gotoHuman & human review
This workflow automatically classifies every new email from your linked mailbox, drafts a personalized reply, and creates Linear tickets for bugs or feature requests. It uses a human-in-the-loop with gotoHuman and continuously improves itself by learning from approved examples. How it works The workflow triggers on every new email from your linked mailbox. Self-learning Email Classifier: an AI model categorizes the email into defined categories (e.g., Bug Report, Feature Request, Sales Opportunity, etc.). It fetches previously approved classification examples from gotoHuman to refine decisions. Self-learning Email Writer: the AI drafts a reply to the email. It learns over time by using previously approved replies from gotoHuman, with per-classification context to tailor tone and style (e.g., different style for sales vs. bug reports). Human Review in gotoHuman: review the classification and the drafted reply. Drafts can be edited or retried. Approved values are used to train the self-learning agents. Send approved Reply: the approved response is sent as a reply to the email thread. Create ticket: if the classification is Bug or Feature Request, a ticket is created by another AI agent in Linear. Human Review in gotoHuman: How to set up Most importantly, install the gotoHuman node before importing this template! (Just add the node to a blank canvas before importing) Set up credentials for gotoHuman, OpenAI, your email provider (e.g. Gmail), and Linear. In gotoHuman, select and create the pre-built review template "Support email agent" or import the ID: 6fzuCJlFYJtlu9mGYcVT. Select this template in the gotoHuman node. In the "gotoHuman: Fetch approved examples" http nodes you need to add your formId. It is the ID of the review template that you just created/imported in gotoHuman. Requirements gotoHuman (human supervision, memory for self-learning) OpenAI (classification, drafting) Gmail or your preferred email provider (for email trigger+replies) Linear (ticketing) How to customize Expand or refine the categories used by the classifier. Update the prompt to reflect your own taxonomy. Filter fetched training data from gotoHuman by reviewer so the writer adapts to their personalized tone and preferences. Add more context to the AI email writer (calendar events, FAQs, product docs) to improve reply quality.