SlothChat

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

What I Built

SlothChat is an automated, local AI agent designed to embrace absolute laziness. Instead of spending 20 minutes every morning scrolling through hundreds of chaotic, missed unread messages across various group chats (Slack, Discord, and WhatsApp), SlothChat handles the filtering for you.
Running silently in the background overnight, it aggregates message histories, extracts key context, eliminates noise (like memes or small talk), and generates a strict two-sentence morning digest featuring actionable highlights and direct mentions. It saves community managers, developers, and casual users from text fatigue, letting them catch up on hours of conversation in less than five seconds.

Demo

You can view the functional application and its interactive dashboard live here:
👉 Live Deployment: https://slothchat-digest.tech
(Note: The dashboard showcases an interactive timeline view of generated daily summaries alongside real-time pipeline status checks.)

Code

The complete codebase, configurations, and environment setups are fully open-source and hosted on GitHub:
👉 GitHub Repository: github.com
bash

Quickstart Installation

git clone https://github.com
cd slothchat-agent
npm install
ollama run gemma2
npm run dev
Use code with caution.

How I Built It

SlothChat is engineered entirely around a robust open-source AI and automation architecture:
• Core LLM: I utilized Gemma 2 (9B) running completely locally via Ollama. It handles all semantic parsing and text summarization without sending text data to external third-party services.
• Agent Framework: Built using the Mastra framework to orchestrate the internal tool layout, schedule the cron schedules, and route message payloads seamlessly to the local model.
• Durable Orchestration: The collection, formatting, and delivery pipelines are wrapped inside a Temporal workflow. This guarantees that if a local machine loses network connectivity or restarts overnight, the agent automatically retries the tool calls and resumes exactly where it left off without losing state.
• Data & Memory Layer: Message logs, conversational context vectors, and historical digests are managed using MongoDB Atlas via Atlas Vector Search to keep an optimized local window index.
• Telemetry: Instrumented using Sentry Agent Tracing to carefully monitor pipeline performance, pipeline exceptions, token counts, and execution latency.

Why Does Open Innovation Matter?

Open innovation is the absolute foundation of SlothChat. Building this on a closed-source API ecosystem would pose severe roadblocks:

1. Absolute Privacy: Group chats contain private conversations, proprietary code, and sensitive personal information. Passing thousands of daily chat logs to a closed API vendor raises major privacy concerns. Running Gemma locally ensures that every single byte of message history stays entirely on local storage.
2. Predictable Cost-Efficiency: Parsing thousands of messages every single night would quickly rack up massive token bills on paid closed APIs. Leveraging open-weight models means the system scales effortlessly, running continuously for zero operational API costs.
3. Local Offline Resiliency: Because the core compilation engine operates directly on-device, the parsing engine functions even without an active internet connection, providing true computing autonomy.

My Agent Session

To see how the agent reasons through raw chat data, filters out chatter, and maps messages to structural summaries, you can inspect the full execution logs here:
👉 DevRelay Agent Session Link: devrelay.ai

The Lazy Payoff

SlothChat turns grueling catch-up routines into an instant glance. During testing this week, the agent processed 412 missed unread messages across three active Discord servers overnight.
Instead of spending an average of 18 minutes wading through backlogged chatter, the agent delivered a clean, accurate 2-sentence summary right at 8:00 AM:
"The engineering team decided to delay the beta release until Thursday due to a caching bug found in the API layer. Action Required: @developer needs to review the updated PR #402 on GitHub before the afternoon standup."
Total time saved: ~17 minutes per day, scaling up to nearly 2 hours of focus time saved every single week.

Prize Categories

This project is entering the following specific partner categories:
• Best Use of Gemma: Utilizes the Gemma 2 open-weight model running locally via Ollama for private token processing.
• Best Use of Mastra: Uses Mastra to build, structure, and orchestrate the core agent workflows and tools.
• Best Use of Temporal: Wraps the background chat parsing ingestion inside durable workflows to survive failures and handle state retries gracefully.
• Best Use of MongoDB Atlas: Employs MongoDB Atlas as the primary data and vector memory layer for active message log indexing.
• Best Use of Sentry Agent Tracing: Implements detailed tracing to track latency performance and debug token operational overhead.
• Best Use of a .tech Domain: Deployed live on a custom .tech domain registered specifically for the challenge.