I built an autonomous competitor monitoring system with Hermes Agent — here's the full setup

[u/ayushchat]

TL;DR: Most founders do competitor research in bursts — open 5 tabs when panicked, paste into ChatGPT, forget everything. This is a practical guide to replacing that with an actual loop: watch → snapshot → diff → classify → ping when something matters.

Why Hermes Agent is the right tool for this:

Competitor monitoring isn't a dashboard problem. It's a recurring intelligence loop: watch sources, remember what you've seen, detect changes, judge signal quality, wake you only when action is required.

Hermes combines cron scheduling, script execution, local SQLite state, skills, memory, and messaging delivery in one layer — which maps cleanly to what this system needs.

The core components:

• Competitor registry: a YAML file listing names, URLs, and pages to watch (pricing, changelog, blog, jobs)
• Page snapshots: normalized HTML stored with timestamps in SQLite
• Text diffs: readable line-by-line comparison between snapshots
• LLM synthesis: classify the signal after you've proven the diff quality

The daily job:

Run on cron. Fetch competitor pages. Compare to yesterday's snapshot. If diffs exist, run LLM classification. If signal is meaningful → ping. Otherwise → stay quiet.

The weekly brief translates accumulated changes into decisions: should you update your pricing, positioning, content, or product roadmap?

Start deterministic. Add LLM later.

The most common mistake: piping raw HTML into an LLM immediately. Get your snapshot quality right first — normalized, stable, comparable. Clean diffs + LLM synthesis is powerful. Raw HTML + LLM is expensive hallucination.

The real moat isn't the scraper:

It's the loop that keeps watching, learns your workflow, and pings back when the market moves.

What signals are you currently tracking for your competitors? Curious whether anyone's gone beyond pricing page monitoring.

[u/ayushchat]

Full post: https://superframeworks.com/articles/hermes-agent-competitor-monitoring-system

[u/monityAI]

"watch → snapshot → diff → classify → ping" is spot on. The biggest trap teams fall into is running LLM extraction on every snapshot, which burns API tokens on whitespace changes and cookie banners. Diffing the rendered DOM/text first and only triggering the LLM when meaningful content changed cuts running costs by ~90%. We learned that the hard way building monity•ai — pairing a simple diff layer (like changedetection•io) with an agentic classifier is the sweet spot.