Touch Grass: an open-source AI agent that plans your way outside 🌿

My submission for the Hacktoberfest Open-Source AI Challenge: Week 1 — "Touch Grass".

The prompt was simple: build something with open-source AI at its core that gets people off the screen and into the world. So I built the smallest agent I could that does exactly that — you tell it where you are and how much time you have, and it plans a real outdoor micro-adventure: when to go, where to stand, what to look for, and what to do with your phone (spoiler: airplane mode).

Touch Grass is a tiny Python agent (~200 lines, one dependency). It runs a tool-using loop over any OpenAI-compatible chat endpoint:

Real run, tonight, no cherry-picking — asked for a 45-minute sunset walk with birding near Olympic Forest Park, Beijing:

When: 17:05 → 17:50. Sunset is 17:46, so this window catches warm pre-sunset light and ends in the afterglow. Overcast, 21°C, 0% precipitation across the next 12 hours — dry and calm. Honest caveat: overcast may swallow the sun disc itself. That's fine — it's actually better for birds.

Where: the east shore of Aohai lake, looking west across the water. Enter at the south gate, 8 minutes' walk to the shoreline.

The plan: timed steps from gate to shoreline birding (17:10–17:28), then 5 minutes north to a west-facing photo gap, shooting the afterglow 5–10 minutes after official sunset.

Look for: grey herons and great egrets in the shallows, little grebes and coots on open water, azure-winged magpies in the treeline, the lake going two-tone in the wind, early-October migrants in the willows.

Leave the phone: airplane mode in your pocket until 17:44 — the camera is the only phone use, and it starts when the light does.

Rain check: not needed today. If the sky fully greys out, drop the sunset shot and walk the wetland boardwalk instead.

What I like about this output: the agent actually used its tools. It geocoded the park, pulled real sunset/weather data, and reasoned about it — noticing that overcast hurts the sunset photo but helps the birding, and adjusting the plan accordingly. That's the agent earning its keep, not just filling a template.

This project only makes sense because it's open, in three concrete ways:

Closed models could generate the same words. They couldn't give you the same guarantees. For a tool whose whole job is getting you outside, offline and private aren't features — they're the point.

pip install -r requirements.txt export OPENAI_BASE_URL = http://localhost:11434/v1 # or any OpenAI-compatible endpoint export OPENAI_MODEL = qwen2.5:3b python -m touchgrass.cli --place "your park here" --minutes 45 \ --interests "birding, sunset photos" Enter fullscreen mode Exit fullscreen mode Repo: https://github.com/986036734/touch-grass

I validated the plan logic against real data tonight; the agent geocoded, checked sunset and weather, and reasoned over them rather than filling a template.