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william freund
william freund

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

#devchallenge #hf26challenge

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).

What I built

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

  • Tools: geocoding (OpenStreetMap), sunrise/sunset times, and a 12-hour weather outlook (Open-Meteo) — all free, keyless, open data.
  • Brain: any open-weight model behind an OpenAI-compatible API. The agent supports local Ollama for fully offline inference, or a hosted gateway. For this demo I ran it against a hosted open-weight model served over an OpenAI-compatible endpoint; swapping models is one environment variable.
  • Output: a concrete Markdown plan — time window, spot, timed steps, things to notice, a phone rule, and a rain check. No frameworks, no vendor SDKs. The whole thing is requests + a loop.

Demo

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

Lakeside Light & Last Calls — Aohai, Olympic Forest Park

  • 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.

Why open innovation matters here

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

  1. It can go where the trail goes. A closed API means the agent dies the moment you lose signal. Open weights mean the same agent runs on a laptop with Ollama, fully offline — which is exactly where a "touch grass" tool needs to work.
  2. Your location stays yours. The agent's most sensitive input is where you are and when. With open models and open data sources, none of that has to travel to a server you don't control.
  3. It costs nothing to run and anyone can improve it. The model is swappable, the data sources are free, the code is ~200 lines of MIT-licensed Python. A student, a hiking club, or a park ranger can fork it, point it at a newer open model next year, and it's better — no permission, no bill.

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.

Try it 

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"
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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.

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