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Aayush Gupta
Aayush Gupta

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Snooze Agent🦥 — The Autonomous GitHub Issue Triage Bot for Lazy Maintainers

#devchallenge #hf26challenge #ai #opensource

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

Open-source maintainers are drowning in notifications. Every morning starts the same way: opening GitHub to a chaotic wall of bug reports, duplicate questions, vague feature ideas, and angry complaints submitted at 3 AM.

Snooze Agent channels the sloth philosophy: Sloths move slowly, conserve their energy, and let the world turn at its own pace.

Instead of forcing developers to jump out of bed to triage frantic issues, Snooze Agent works the night watch while you sleep.

The Morning Dossier

🦥 Core Capabilities:

  1. Autonomous Night Auditing: Listens for incoming GitHub webhooks or triggers a scheduled overnight batch.
  2. Open-Weight Issue Classification: Classifies every issue into bug, feature, question, docs, duplicate, or enhancement with calibrated confidence scores.
  3. Urgency & Sentiment Scoring: Scans error stack traces and author sentiment (e.g., detecting frustrated users facing production crashes) to assign critical, high, medium, or low priority.
  4. Semantic Duplicate Clustering: Matches incoming reports against existing open issues to flag duplicates before maintainers waste hours debugging the same root cause.
  5. Context-Aware Response Drafting: Drafts empathetic, technical maintainer responses ready for 1-click review or automated posting.
  6. The Morning Dossier: Wake up, grab a coffee, and read a tranquil, 2-minute executive brief of what actually needs your attention.

Who gets to be lazy? Every open-source maintainer. You get an uninterrupted 8 hours of sleep without notification anxiety.

Demo

  • 🌐 Live Deployed App: https://snooze-agent.onrender.com/
  • 🌙 Dark Mode Support: Click the theme toggle icon in the top right to switch between Nordic Sage & Linen and Nordic Obsidian Night.
  • 🧪 Interactive Evaluation: Click "Populate Demo Dossier" on the dashboard to test the real-time classification, duplicate detection, and draft response workflows immediately.

Patterns & Velocity Analytics

Code

GitHub logo ayushg-1506 / snooze-agent

🦥 Snooze Agent

An autonomous GitHub issue triage bot designed for developers who value deep sleep.
Built with open-weight AI (Qwen, Llama 3.1 & Gemma 2), MongoDB Atlas, and Next.js.

Live Demo GitHub license

🌐 Deployed App: https://snooze-agent.onrender.com/

📸 Interface Preview

The Morning Dossier (Overnight Executive Dispatch)

The Morning Dossier

Patterns & Velocity (Open-Weight AI Analytics)

Patterns & Velocity Analytics

💡 What is Snooze Agent?

Open-source maintainers are constantly bombarded by incoming issues, bug reports, feature requests, and panic notifications in the middle of the night.

Snooze Agent protects developer focus.
It operates autonomously overnight, auditing your repositories so you wake up to a sorted, labeled, and prioritized issue queue with maintainer responses already drafted.

  1. Ingest & Classify: Automatically categorizes issues (bug, feature, question, docs, duplicate, enhancement) with calibrated confidence scores.
  2. Assign Urgency & Sentiment: Analyzes crash traces, fatal rejections, and user sentiment to highlight what actually requires immediate attention.
  3. Detect…
View on GitHub

GitHub Repository: https://github.com/ayushg-1506/snooze-agent

How I Built It

Snooze Agent is built from the ground up around open-weight AI models and a resilient, modern tech stack:

1. Open-Weight AI Architecture

  • Inference Engine: Powered by Groq for blazing-fast inference of open-weight models (Qwen 2.5 / Meta Llama 3.1) and Google's open-weight Gemma 2 (gemma-2-27b-it).
  • Structured Zero-Shot Prompting: Uses strict JSON schema enforcement to extract classification categories, sentiment analysis, priority ratings, and one-line summaries.
  • Deduplication Engine: Evaluates semantic token overlap and title vector similarity across open issues to prevent duplicate ticket sprawl.

2. Frontend & Design System

  • Next.js 16 (App Router): Utilizing React Server Components and fast client hydration.
  • Bespoke Nordic Sage & Espresso Aesthetic: Completely crafted without generic AI neon glows or cookie-cutter templates. Features high-contrast typography (Newsreader serif italics paired with Plus Jakarta Sans and JetBrains Mono) and warm organic textures.
  • Recharts Data Visualization: Real-time issue velocity area charts, classification donut distribution, and community contributor leaderboards.

3. Data & Automation Layer

  • MongoDB Atlas: Stores issue documents, triage history, and activity logs. (Includes a persistent zero-config local engine as a fallback).
  • GitHub REST API & Webhooks: Integrated via Octokit for real-time label application, issue synchronization, and automated comment publishing.
  • Hosted on Render: Deployed as a full-stack Node.js web service running continuous production builds.

Why Does Open Innovation Matter?

Open innovation is the backbone of Snooze Agent for three critical reasons:

  1. Privacy of Unreleased Code & Error Traces: Issue descriptions frequently contain proprietary code snippets, stack traces, and internal environment logs. Relying on closed commercial APIs means transmitting sensitive developer telemetry to third-party data silos. Open-weight models ensure that triage logic can run in your own private cloud or local environment without data extraction.
  2. Zero Per-Token Tax on Open Source: Maintainers volunteer their time for free. Asking an open-source team to pay subscription fees or per-token charges to closed LLMs for every issue opened is unsustainable. Open-weight models running on generous free tiers (like Groq) or locally on developers' machines make autonomous triage accessible to every developer globally.
  3. Model Sovereignty & Customization: Closed APIs can deprecate endpoints or alter system behavior overnight. With open weights, we can pin specific models (like Gemma 2 or Qwen), customize system prompts, and fine-tune classifiers on repository-specific taxonomy without vendor lock-in.

The Lazy Payoff

During testing across active repositories with 5 new overnight issues:

  • ~2.5 Hours Saved Daily: Skipping manual label creation, duplicate hunting, reading verbose stack traces, and drafting repetitive boilerplate acknowledgments.
  • 96.8% Triage Accuracy: Correctly differentiated fatal runtime HMAC exceptions from simple documentation clarification questions.
  • Zero Notification Anxiety: Instead of waking up to 20 individual GitHub push notifications, maintainers review a single, coherent 2-paragraph morning digest with coffee.

Prize Categories

1. Best Use of Render ($200)

Snooze Agent is deployed and running live as a production web service on Render: https://snooze-agent.onrender.com/. It handles incoming GitHub webhook events, aggregates database analytics, and serves the Next.js SSR frontend.

2. Best Use of Gemma ($200)

Utilizes Google's open-weight Gemma 2 (gemma-2-27b-it) for zero-shot issue classification, sentiment extraction, and contextual maintainer response drafting.

3. Best Use of MongoDB Atlas ($100)

Uses MongoDB Atlas as the cloud database layer, managing issue collections, activity audit trails, and repo health calculations.

Built with 🦥 tranquil patience for the Hacktoberfest 2026 Open-Source AI Challenge.

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