Nexus

This is a submission for the Hermes Agent Challenge

What I Built

Nexus is an autonomous research digest agent powered by Hermes.
In a single command, Nexus plans a search strategy, searches the web, reads the top sources, clusters findings into themes, and generates a clean HTML digest with citations and a what to read next queue. No hand holding. No prompting for each step.

Demo

$ python main.py AI safety and alignment

Nexus Research Digest
Topic: AI safety and alignment
Date: 2026-05-30
Model : openrouter/auto

[step 1] Thinking... Tool: search_web
[step 2] Thinking... Tool: search_web
[step 3] Thinking... Tool: fetch_url
[step 4] Thinking... Tool: cluster_findings
[step 5] Thinking... Tool: save_digest

Digest saved
Done. Check the output/ folder.

Code
GitHub: https://github.com/redwanshahriarshubho/Nexus
My Tech Stack
• Python 3.10+
• Hermes Agent via OpenRouter API
• scikit-learn (TF-IDF clustering)
• urllib (web search + fetching)
• Custom HTML renderer (no frameworks)

How I Used Hermes Agent

Nexus uses three core Hermes agentic capabilities:

Multi-step planning: Hermes doesn't just answer one prompt. It plans which search queries to run, decides which URLs to fetch, and sequences its own actions autonomously.

Tool use: Four custom tools are registered: search_web, fetch_url, cluster_findings, and save_digest. Hermes decides when to call each one and with what arguments — no hardcoded flow.

Autonomous completion: The agent runs for up to 20 steps, self-corrects when tools return errors or results are sparse, and always finishes by saving the digest.

The result is a clean HTML report with a summary, key findings, themed sections, citations, and a what to read next queue generated entirely by Hermes reasoning through the problem step by step.