Sloth Agent — the open-source AI agent that works while you laze

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

What I Built

Sloth Agent — an open-source AI automation agent that does your overnight chores so you can keep lazing. It reads your inbox/group chats and hands you a two-line morning summary, sorts and archives your messy files, and forecasts or classifies from your CSVs using TabPFN. All of it runs on open-weight models (Gemma) on your own machine — no API keys, no subscriptions, no internet required after download.

Repo: mahilsaily306-max/sloth-agent

Demo

Deployed frontend: (link goes here — render / digitalocean / .tech domain)

Demo video: (link goes here)

Core flows, one click each:

• Overnight Summary → two-line summary of last night's messages
• Tidy Files → sort/rename/archive by type (dry-run safe by default)
• Predict (TabPFN) → forecast/classify from a CSV
• Chat (Gemma) → local open-weight model conversation
• Full Overnight Run → summarize + tidy + predict in a single call

Code

mahilsaily306-max/sloth-agent

Key files:

• backend/models/gemma.py — Gemma open-weight model (Ollama / transformers / stub)
• backend/models/tabpfn.py — TabPFN tabular foundation model (sklearn fallback)
• backend/agents/sloth_agent.py — the agent: summarize, tidy, predict, overnight
• backend/temporal/workflows.py — durable Temporal workflow (retries, resume)
• backend/sentry_config.py — Sentry agent tracing (latency, tokens, cost)
• integrations/integrations.ts — SDK wrappers for all 20 partner categories
• .github/workflows/ci.yml — GitHub Actions CI (Copilot-reviewed)
• render.yaml — Render hosting config
• backend/tests/smoke_test.py — zero-dependency smoke tests (passing)

How I Built It

Open-source AI at every layer:

• Runtime: Gemma (open-weight) via Ollama or Hugging Face transformers; falls back to a deterministic stub so the project runs with zero ML installs.
• Tabular prediction: TabPFN-3.5 (Prior Labs) for CSV forecasting; sklearn fallback if TabPFN isn't installed.
• Orchestration: Mastra-style agent pattern over open models with tools and memory.
• Durability: Temporal workflow wraps the overnight run so it survives failures and resumes where it left off.
• Tracing: Sentry captures agent setup, latency, tokens, cost, and errors.
• Frontend: Single-file HTML/JS dashboard calling the FastAPI backend.
• CI: GitHub Actions lint + smoke tests on every PR.

Why Does Open Innovation Matter?

Running open-weight models locally means:

1. No internet needed. Gemma + TabPFN run on a laptop offline after download. A closed API agent would stop working without connectivity.
2. Data stays on your machine. Your messages, files, and CSVs are never uploaded to a server you don't control.
3. Set up once, never pay again. Open-weight models cost $0 to run locally — no per-token charges, no subscriptions.
4. Swap models freely. Swap Gemma for Mistral, Llama, or any open weight; swap TabPFN for another tabular model. Closed APIs lock you into one vendor.

The open-based approach worked better here because this is a personal automation agent — privacy, offline use, and zero recurring cost matter more than a hosted SaaS.

My Agent Session

Saved with DevRelay and embedded below (Entire prize category: share the agent sessions behind your project).

(agent_session embed link goes here)

The Lazy Payoff

Before Sloth Agent, you'd wake up, scroll through last night's group chat, dig through a messy downloads folder, and manually open spreadsheets to forecast — maybe 15–20 minutes of drudgery.

After: one overnight call runs while you sleep. Morning summary in two lines, files already sorted, CSV prediction ready. The agent does the work so you don't have to — and it runs for $0 on your own hardware.

Prize Categories

Entering every applicable partner category:

Featured : Gemma, TabPFN, Render, DigitalOcean, Tinker, Arduino

Partner : Mastra, Sentry, Temporal, MongoDB Atlas, SerpApi, Browserbase, ElevenLabs, Auth0, Backboard, Entire, GitHub Copilot, .tech Domains, Tiger Data, Ultramarine

Each integration is coded in integrations/integrations.ts or configured (render.yaml, ci.yml) and documented in docs/.