Hermes Agent, an open-source framework developed by Nous Research, has crossed 140,000 GitHub stars in under three months and is now the most-used agent on OpenRouter. Designed for reliability and self-improvement, Hermes is provider- and model-agnostic, optimized for always-on local use on NVIDIA RTX PCs, RTX PRO workstations, and DGX Spark systems.
What Hermes Does
Like other popular agents, Hermes integrates with messaging apps, accesses local files and applications, and runs 24/7. Four capabilities set it apart:
- Self-Evolving Skills: Hermes writes and refines its own skills. When it encounters a complex task or receives feedback, it saves learnings as a skill, adapting and improving over time.
- Contained Sub-Agents: Sub-agents are short-lived, isolated workers dedicated to a sub-task with a focused context and tool set. This keeps task organization tidy and allows Hermes to run with smaller context windows — ideal for local models.
- Reliability by Design: Nous Research curates and stress-tests every skill, tool, and plug-in shipped with Hermes. The framework works reliably even with 30-billion-parameter-class local models, without the constant debugging required by other agent frameworks.
- Same Model, Better Results: Developer comparisons using identical models across frameworks consistently show stronger results in Hermes. The framework is an active orchestration layer, not a thin wrapper, enabling persistent, on-device agents instead of task-by-task execution.
Hardware Requirements
Hermes and the underlying LLM are built to run locally. NVIDIA RTX GPUs are purpose-built for this workload. The new Qwen 3.6 models from Alibaba are ideal for local agents like Hermes:
- Qwen 3.6 35B: Runs on roughly 20GB of memory while surpassing 120-billion-parameter models that require 70GB+.
- Qwen 3.6 27B: A dense model with more active parameters, matching the accuracy of 400-billion-parameter models like Qwen 3.5 397B while being one-sixteenth the size.
NVIDIA DGX Spark, with 128GB of unified memory and 1 petaflop of AI performance, can run 120-billion-parameter mixture-of-experts models all day. The Qwen 3.6 35B model runs faster on DGX Spark, allowing concurrent workloads.
Getting Started
Visit the Hermes GitHub repository and pair it with a preferred local model and runtime. Hermes ships with LM Studio and Ollama support out of the box. Run Hermes alongside Qwen 3.6 via llama.cpp, LM Studio, or Ollama.
Bottom Line
Hermes Agent offers a reliable, self-improving local AI agent framework that works well with current-generation open-weight models. The combination of Nous Research's curated skills and NVIDIA's local hardware provides a practical foundation for persistent, on-device agentic workflows.