Hermes

A self-hosted AI agent needs more than a long context window. It needs a way to retain useful facts, retrieve earlier decisions, and revise what it thinks it knows as projects evolve.

Mnemosyne is a local-first memory provider for Hermes Agent, storing working memory, structured facts, temporal data, and episodic history in local SQLite — no hosted service, no mandatory network calls, and unusually granular write control.

Persistent memory turns an agent from a re-explained tool into one that carries context forward — but it opens a failure mode stateless chat avoids: an interpretation can become memory, retrieved as fact, and justify a stronger version of itself.

Migrating an AI assistant is not the same as copying an application config. The hard part is preserving identity, memory, tool behavior, scheduled work, and messaging access without two gateways acting as the same bot.

Agent Skills and MCP servers are often presented as competing ways to extend an AI agent. That framing is wrong: a skill teaches the agent how to work, while an MCP server gives it governed access to live capabilities.

Running Hermes Agent on a headless server while connecting from a desktop client on another machine requires two server processes and a single client connection.

Polling agents are one of the least glamorous parts of AI assistant architecture, but they are also one of the most useful.

AI agent architecture is starting to split into two layers.

The A2A Protocol, short for Agent2Agent Protocol, is an open standard for communication between independent AI agent systems.

Memory turns assistants from reactive to persistent, but it is also where many systems quietly rot. Surveys argue the short-term versus long-term split is no longer enough for modern agent memory; OpenAI and LangGraph SDKs point to a simpler stack — working memory, durable state, and retrieval.

A production AI assistant is not “an LLM with a prompt”. It is a system that accepts intent, keeps state, decides when to retrieve or act, and exposes enough runtime detail to debug failures.

Open-source AI agent frameworks are exploding in popularity on GitHub. Two projects at the core of the self-hosted AI systems ecosystem — OpenClaw and Hermes Agent — have pulled so far ahead that the rest of the field is fighting for a distant third place.

This page is a practical reference for agentic LLM inference tuning (temperature, top_p, top_k, penalties, and how they interact in multi-step and tool-heavy workflows).

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.