Hermes

You already chat to Hermes Agent from your phone with text. Now you want to talk to it directly and get spoken replies back. That is usually the right move, especially if you already use Hermes as a persistent self-hosted assistant. Typing long prompts on a small screen is slow and error-prone

Hermes Agent ships with a Kanban-style board and the Hermes Gateway that can saturate your self-hosted LLM if too many tasks are dispatched at once.

Hermes Agent treats skills as the default way to teach repeatable workflows. Official documentation describes them as on-demand knowledge documents aligned with the open agentskills.io shape, loaded through progressive disclosure so the model sees a small index first and only pulls full instructions when a task actually needs them.

Hermes Agent from Nous Research is a model-agnostic, tool-using assistant you run locally or on a VPS.

Modern assistants still forget everything when you close the tab unless something persists beyond the context window. Agent memory providers are services or libraries that hold facts and summaries across sessions — often wired in as plugins so the framework stays thin while memory scales.

You know the drill. You open a chat with an AI agent, explain your project, share your preferences, get some work done, and close the tab. Come back the following week and it’s like talking to a stranger — all context gone, every preference forgotten, the project re-explained from scratch.

Hermes AI assistant, officially documented as Hermes Agent, is not positioned as a simple chat wrapper.

Hermes Agent is a self-hosted, model-agnostic AI assistant that runs on a local machine or low-cost VPS, works through terminal and messaging interfaces, and improves over time by turning repeated tasks into reusable skills.

Most local AI setups start with a model and a runtime.