@atomicbot_ai: Hermes Agent vs OpenClaw using Qwen 35B Local Model We asked agents to scrape GitHub star history for both tools, find …
Summary
A comparison of Hermes Agent and OpenClaw using Qwen 35B local model, where the agents scrape GitHub star history, identify growth spikes, and build live dashboards. OpenClaw took 12m 01s (203k tokens), Hermes took 33m 01s (257k tokens), with different approaches.
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Cached at: 05/16/26, 07:15 AM
Hermes Agent vs OpenClaw using Qwen 35B Local Model
We asked agents to scrape GitHub star history for both tools, find what caused the growth spikes, build a live dashboard in the browser.
MacBook Pro M5 Max 64Gb OpenClaw: 203k tokens, 12m 01s - wrote a bash script Hermes: 257k tokens, 33m 01s - wrote a SKILL.md
OpenClaw hit GitHub API, got truncated responses, paginated through contributors, pulled star-history JSON, found a security incident in OpenClaw’s history, fetched SVGs, fixed broken HTML from trimming, rewrote it clean.
Hermes parallel tool calls across GitHub API, web search, and browser. Hit Google rate limit, auto-switched to DuckDuckGo. Fetched article contents, mapped viral moments, then built the dashboard.
Both shipped a live dashboard with star growth charts and spike annotations
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