FarmRoute Agent — AI crop delivery planner that learns from errors

Mohamed Suhail — Sun, 31 May 2026 18:28:03 +0000

This is a submission for the Hermes Agent Challenge

What I Built

FarmRoute Agent v4.0 — an AI-powered agricultural delivery planner
that decides which crops to deliver first, plans optimal routes across
10 villages, handles real-world disruptions like flooding and road
closures, and self-improves by learning from past delivery errors.

The entire project was built with Hermes Agent. I gave Hermes a skill
file describing the problem — Hermes planned the architecture, wrote
873 lines of backend code, built the animated UI, and ran the first
delivery simulation entirely by itself.

The problem it solves:
A farmer has 8 types of perishable crops (strawberry spoils in 12h,
potato lasts 96h) that need delivering to 10 villages with different
weather conditions, road risks, and time windows. Which crops go first?
Which route wastes the least time? What if a bridge closes?

FarmRoute Agent answers all of this in real time.

Demo

Code

https://github.com/Suhail-26/farmroute-agent/

My Tech Stack

Hermes Agent (Nous Research) — built the project + powers delivery planning
owl-alpha via OpenRouter — free LLM reasoning backbone
Python 3.14 + Flask — simulation engine (873 lines)
Vanilla HTML/CSS/JS — animated terminal-style UI (28KB, zero dependencies)
memory.json — persistent self-learning across sessions

How I Used Hermes Agent

Hermes Built the Entire Project

I created one skill.md file and typed this in Hermes:
Hermes read the skill, wrote app.py (873 lines), built
the animated UI (28KB), created the memory system, started
the Flask server, tested all API endpoints, and delivered
the first complete delivery plan — without me writing
a single line of code.

2. Hermes Reasons Through Every Delivery

Every RUN triggers 25+ reasoning steps:

Recalls past sessions and learned lessons from memory
Checks spoilage urgency for all 8 crops
Checks weather conditions at all 10 villages
Detects road disruptions before planning
Runs LLM chain-of-thought planning via OpenRouter
Generates scored delivery report with bonuses

Every step is visible in the animated reasoning feed —
you watch Hermes think in real time.

3. Hermes Learns From Its Own Errors

Every session is saved to memory.json. The agent learns from:

Disruptions → village risk score increases next run
Critical spoilage → crop priority bias increases
Low scores → human-readable lesson generated

Bias decays 0.95× per run so learning stays fresh. After
10 runs you can watch the agent improve — priority bars
shift, lessons accumulate, scores trend upward. This is
Hermes Agent's memory system applied to real logistics.

DEV Community: Mohamed Suhail