Hermes seems to be more effective at tool calling with low-end models than OpenClaw. My setup is bas
agents
| Source: Mastodon | Original article
Hermes, the open‑source function‑calling harness released by Nous Research, is gaining traction after users reported that it outperforms OpenClaw on low‑end language models. In a recent community post, a developer noted that a modest setup using a 7‑billion‑parameter model consumed noticeably fewer tokens with Hermes than with OpenClaw, and that the Hermes harness “gets its own changes right first time more often.” The claim rests on practical tests rather than formal benchmarks, but the anecdotal evidence aligns with Hermes’s design focus on token‑efficient prompt engineering and robust change detection.
The development matters because tool calling is the linchpin of today’s agentic AI. By allowing a model to invoke external APIs—search, databases, or custom functions—developers can build assistants that act autonomously. Low‑end models are the workhorses of on‑premise deployments and cost‑conscious startups; any reduction in token usage translates directly into lower compute bills and faster response times. If Hermes consistently delivers tighter integration and fewer retry cycles, it could shift the balance away from larger, cloud‑only offerings and accelerate the democratisation of agentic AI across the Nordics and beyond.
What to watch next is the emergence of systematic comparisons. Researchers are expected to publish head‑to‑head evaluations on standard tool‑calling suites such as the Function‑Calling v1 dataset, and both Hermes and OpenClaw teams have hinted at upcoming releases—Hermes v2 with expanded schema support and OpenClaw’s next‑generation runtime. Integration with popular orchestration layers like LangChain or the GitHub Copilot CLI will also be a litmus test for real‑world adoption. Stakeholders should keep an eye on community‑driven benchmark results and any announcements from cloud providers that might incorporate Hermes‑style calling into their APIs.
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