# Trail Council: Four Tiny AI Agents on My Laptop That Decide If I Should Go Hiking > Source: > Published: 2026-10-07 15:08:01+00:00 ## What I Built I built **Trail Council**, a panel of four small AI agents that runs on my laptop with no internet and decides whether I should go on a hike today, and which route to take. Four agents each look at the plan from one angle: **Weather**, **Route**, **Gear** and **Safety**. They debate, and a final "lead" agent gives one verdict: *Go*, *Go with a shorter route*, *Gather more info*, or *Stay home*. The output is a **one-page printable plan** with the route, turnaround time, gear checklist and the reasoning. I print it, close the laptop, and walk out the door. The screen is the shortest part of the trip. I use it for about two minutes before I leave, and I can regenerate the plan at the trailhead with zero signal. It's for anyone who plans outings in places with bad connectivity: hikers, trail runners, and weekend trekkers. ## Code I reused the architecture of an earlier project of mine ([AI Boardroom](https://github.com/krushnakodgirwar/AI-Boardroom), a multi-agent business decision simulator). Trail Council is a **new repository** with new agents, new data, new prompts and a new output format. Only the staged pipeline idea carries over. ## How I Built It **Stack** - **Model:** Qwen2.5-3B-Instruct, an open-weight model, loaded in 4-bit (NF4) with PyTorch and`transformers` - **Hardware:** a laptop with an RTX 3050 (4GB VRAM). No cloud GPU. - **Backend:** FastAPI + Uvicorn - **Retrieval:**`all-MiniLM-L6-v2` embeddings + ChromaDB over a small local knowledge base ([YOUR DATA: e.g. my own route notes, sunrise/sunset table, gear checklists, local trail descriptions]) - **Frontend:** [HTML/JS page or Streamlit] **The four-stage pipeline** 1. **Agent analysis:** Weather, Route, Gear and Safety each read the same trip request plus their own retrieved notes and return structured JSON. 2. **Debate:** Agents flag disagreements, for example Safety objecting that the long loop finishes after sunset. 3. **Lead decision:** One agent weighs the analyses and picks a verdict. 4. **Validation:** A check confirms the verdict is one of the four allowed categories and that the next step actually follows from it. Otherwise it retries. **What was hard** A 3B model on 4GB of VRAM drops instructions, cuts off mid-answer and drifts from the format. Three things fixed most of it: - JSON-schema-constrained outputs - Short, role-specific prompts with grounding hints - Compact "cards" passed between stages instead of full text, plus a retry when output is truncated [ADD 1-2 REAL NUMBERS FROM YOUR MACHINE: time to generate a full plan, VRAM used, how often validation had to retry.] ## Field Test I took it to **[PLACE]** on **[DATE]**. - **What worked:** [e.g. it flagged that my planned loop would end after sunset and suggested a shorter one] - **What didn't:** [e.g. the Weather agent was confidently wrong about X, because my data was stale] - **What I changed on the trail:** [e.g. regenerated the plan at the trailhead with no signal, and it worked] - **Did I follow its advice?** [yes/no, and what happened] ## Why Does Open Innovation Matter? - **It worked with no signal.** The model, the embeddings and the vector database all live on my laptop. A hosted-API version of this would be useless at exactly the place it's needed, a trailhead with no coverage. - **My plans stay mine.** Where I go, when I'll be alone, and when I'll be back are sensitive details. Nothing is sent to a server I don't control. - **It costs nothing to run.** No API key and no per-request bill. I regenerated plans [N] times while testing. - **I could tune the behavior.** I rewrote agent roles, swapped the model between sizes, and changed the validation rules. I couldn't have done that with a closed black box. - **The honest tradeoff:** A 3B model is less capable than a frontier model. I had to design around its limits with schemas, short prompts and validation. For this task I think that's a fair trade, but it is a trade. ## Prize Categories - **Best Use of Render:** [ONLY IF you really host the demo frontend on Render; say what runs there and what runs locally]