AI-Powered Linear Issue Sentiment Monitor & Alerter

Unlock Proactive Customer Support with this AI Agent

This AI Agent automates the process of monitoring customer and user sentiment within your Linear issues. It periodically fetches recently updated issues, analyzes the comment threads using OpenAI's powerful language models to determine sentiment (positive, negative, or neutral) and generate a summary. The results are logged in Airtable, and crucial sentiment shifts (e.g., from neutral/positive to negative) trigger real-time Slack notifications to your team. This allows for timely intervention in potentially escalating customer support or project situations. Its core abilities include issue monitoring, AI-driven sentiment analysis, sentiment trend tracking, and intelligent alerting.

Key Features & Benefits

• Automated Issue Ingestion: Regularly fetches recently updated issues from Linear via GraphQL (default every 30 minutes).
• AI-Powered Sentiment Analysis: Employs an OpenAI language model (via Langchain's Information Extractor node) to accurately determine the sentiment (Positive, Negative, Neutral) of ongoing discussions within each issue's comments.
• Sentiment Summarization: Generates a concise AI summary of the overall sentiment in the comments, providing quick insights.
• Airtable Integration for Tracking: Logs issue details, current and previous sentiment, and sentiment summaries into a designated Airtable base, creating a historical record for review and trend analysis. (Sample Airtable structure can be seen at: https://airtable.com/appViDaeaFw4qv9La/shrq6HgeYzpW6uwXL)
• Proactive Slack Notifications: Instantly alerts a specified Slack channel when an issue's sentiment transitions from non-negative to negative.
• Deduplicated Alerts: Prevents notification fatigue by ensuring alerts for the same issue update (based on Issue ID and Last Modified timestamp) are not sent repeatedly.
• Enhanced Team Awareness: Keeps your support, product, or project teams informed about critical shifts in customer or user mood, helping to prioritize urgent matters.
• Improved Response Times: Enables quicker reactions to negative feedback or escalating problems.
• Customizable Monitoring: Easily adjust the monitoring frequency and Linear issue filters to suit your team's specific needs.

• An n8n instance (Cloud or self-hosted).
• Linear API Key (for GraphQL access to fetch issues).
• OpenAI API Key with access to a chat model (e.g., gpt-3.5-turbo, gpt-4) for sentiment analysis.
• Airtable account with a Personal Access Token. You'll need to use or create an Airtable Base (the template uses Base ID: appViDaeaFw4qv9La) and a Table (template uses Table ID: tblhO0sfRhKP6ibS8). The table must be structured to store issue data and sentiment. Refer to the sample Airtable view for the required schema (columns like 'Issue ID', 'Current Sentiment', 'Previous Sentiment', 'Summary', etc.).
• Slack API credentials and the ID of a target channel for notifications.

1. Download the ai-linear-issue-sentiment-monitor-v1.0.0.json n8n workflow template file.
2. Import the workflow into your n8n instance.
3. Configure Credentials: a. Open the 'Fetch Active Linear Issues' node. Under 'Authentication', select 'Header Auth' and create or select credentials containing your Linear API Key. b. Open the 'OpenAI Chat Model' node. Create or select your OpenAI API credentials. c. Open the 'Get Existing Sentiment', 'Update Row', and 'Airtable Trigger' nodes. For each, create or select your Airtable Personal Access Token credentials. d. Open the 'Report Issue Negative Transition' (Slack) node. Create or select your Slack API credentials.
4. Configure Node Settings: a. 'Schedule Trigger': Adjust the polling interval if the default 30 minutes is not suitable for your needs. b. 'Fetch Active Linear Issues' (GraphQL): Verify the endpoint (default: https://api.linear.app/graphql). Modify the variables (GraphQL filter) if you need to target specific Linear projects, teams, or issue states. c. 'Get Existing Sentiment', 'Update Row', and 'Airtable Trigger' nodes: Ensure the Base ID (default: appViDaeaFw4qv9La) and Table ID (default: tblhO0sfRhKP6ibS8) match your Airtable setup. If using a different base/table, update these IDs. Critically, ensure your Airtable table schema includes all fields mapped in the 'Update Row' node (e.g., Issue ID, Current Sentiment, Previous Sentiment, Summary, Issue Title, Issue Created, Issue Updated, Assigned). Refer to the sample Airtable link in Prerequisites for guidance. d. 'Report Issue Negative Transition' (Slack) node: In the 'Channel ID' field, enter the ID of the Slack channel where notifications should be sent.
5. Review the 'Sentiment over Issue Comments' (Information Extractor) node's Attributes parameter to understand the sentiment categories (sentiment, sentimentSummary) being extracted. The prompt for sentiment is defined within this node and can be customized if needed.
6. (Optional) Customize the Slack message format (Blocks UI) in the 'Report Issue Negative Transition' node to match your team's preferences.
7. Ensure all necessary nodes are correctly configured, then activate the workflow.