Hermes Agent 101: Meet the Self-Improving AI Agent

The AI agents era is already here: McKinsey’s 2026 survey found that large organizations have jumped from 27% to 31% scaling AI agents across their business in just a single year.¹

Where earlier agents could act on your behalf but had no real sense of whether they’d done it well, a newer generation can now assess its own work and refine it, turning experience into something closer to genuine improvement. Hermes agent is one of the agents built around that shift, going beyond persistent memory to evaluate and refine its own performance over time.

This guide covers how Hermes’ agent can improve itself, how it compares to OpenClaw, how to get it running without drowning in setup, and the human skills worth building so you stay in control of an agent that’s writing its own playbook.

What is Hermes Agent?

Hermes Agent is a free, open-source autonomous AI agent framework from Nous Research (MIT license). It is not a single-window chatbot or an IDE-only coding copilot. It can plan multi-step work, use tools such as a terminal and browser, keep state across sessions, and run persistently through its gateway service for always-on workflows.²

You choose the model layer (including Nous Portal, OpenRouter, OpenAI-compatible endpoints, and local models) without rebuilding your agent setup around a single provider.²

Check out the Best AI agents in 2026 (and how to use them in your workflow).

Features that make Hermes feel different

If you are comparing open source AI agent options, these are the day-to-day differences:²,³,⁴

• Persistent memory across sessions
• Self-created skills that can be written and improved through experience
• Messaging gateway for Telegram, Discord, Slack, WhatsApp, Signal, email, and more
• Scheduled and event-driven automations
• Subagents for parallel workstreams
• MCP support for connecting external tools and services
• Flexible execution backends including local, Docker, SSH, Daytona, Modal, Vercel Sandbox, and Singularity/Apptainer

Industry coverage has also called out Hermes as a fast-rising option for always-on local agents.⁵

How the learning loop works

A typical agent run is “task, plan, tools, answer, stop.” Hermes adds a closed learning loop on top.²

After non-trivial work, the agent can:

• Evaluate what worked and what failed
• Extract a reusable skill (a procedure for next time)
• Refine that skill while it is in use
• Retrieve past sessions and a growing model of your preferences

Memory is curated into agent notes and a user profile, with cross-session search when needed. Skills use open formats compatible with agentskills.io, so procedures travel beyond one chat.

What “self-improving” actually means in Hermes Agent

“Self-improving” does not mean Hermes retrains its underlying language model or changes its model weights every time you use it. The improvement happens primarily at the agent layer.

Hermes can turn useful workflows into reusable skills, refine those skills as they are used, save useful facts and preferences to persistent memory, and retrieve relevant information from earlier sessions. The underlying LLM is still the model you choose to connect.

That distinction matters: Hermes can become better adapted to your workflows over time without retraining the model underneath it. And because its memory and skills are inspectable, you can review, edit, approve, or remove what the agent learns.

Hermes Agent vs. OpenClaw

If you’re comparing self-hosted, open-source agent runtimes, OpenClaw is one of Hermes’s closest comparisons. Both can run on your own infrastructure, connect LLMs to tools and messaging services, maintain state, and execute work beyond a single chat session.

The difference is increasingly one of emphasis rather than a clean checklist of features.

	Hermes Agent	OpenClaw
License	MIT	MIT
Origin	Nous Research	Originally created by Peter Steinberger; now developed as the OpenClaw project
Runtime	Self-hosted agent runtime with persistent/always-on options	Self-hosted agent runtime with persistent/always-on options
Emphasis	Autonomous skill creation and refinement, curated memory, and cross-session learning	Broad orchestration and integration surface, including automations, background tasks, subagents, and Task Flow
Models	Nous Portal, OpenRouter, OpenAI-compatible endpoints, local models, and others	Multiple model providers and local-model options
Messaging	Broad support including Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Teams, and others	Broad messaging/channel integration ecosystem
Skills	AgentSkills-compatible skills with autonomous creation and refinement	AgentSkills-compatible skills and a broad community ecosystem
Deployment	Local plus multiple isolated/remote execution backends	Local, containerized, and hosted deployment options
Migration	Documented OpenClaw → Hermes migration with hermes claw migrate	—
Best fit	Users who want self-improvement and persistent learning to be central to the agent workflow	Users who prioritize a broad, fast-moving orchestration and integration ecosystem

Neither is objectively “better” in the abstract; it depends on whether you weigh a tighter, learning-focused core loop or a larger, faster-moving ecosystem more heavily for your use case. Worth trying both before you commit infrastructure to either.

Getting started with Hermes Agent without drowning in setup

Official install paths are short. Linux, macOS, WSL2, and Termux use a documented one-liner; Windows has PowerShell and desktop installers.²

A sensible first path:

• Install from the official docs or desktop app.
• Run hermes setup or hermes model and connect a provider.
• Finish one clean CLI conversation before adding extras.
• Optionally set up the gateway for messaging and always-on use.

Choose where Hermes is allowed to execute

Before giving Hermes broad filesystem access or real credentials, decide where its tools should run. Local execution is convenient but gives the agent access to the host environment. Containerized, remote, or cloud sandbox backends can provide a stronger isolation boundary.

Start with the smallest execution surface your workflow needs. You can widen permissions later once you understand what the agent actually needs to accomplish the task.

Skills that help you run Hermes well

Hermes will plan, execute, and write its own procedures, but a self-improving agent is only as good as the human steering it. A few concrete skills make the difference between an agent that saves you hours and one that quietly accumulates bad habits.

Read before you run

Pay particular attention to potentially destructive commands, new permissions, persistent memory changes, and agent-authored skills, especially while you’re establishing trust in a new setup.

Hermes provides command approval and isolation controls, but those protections are only useful if you configure them for the level of autonomy you’re comfortable with.

Curate what it remembers

Hermes can review interactions in the background and save useful information to persistent memory. That’s part of what makes its learning loop useful, but it also means a wrong assumption can become a persistent context just as easily as a useful one.

If you want tighter control, Hermes supports memory.write_approval: true, which lets you review proposed memory changes before they persist.³

Treat skills like code review, not magic

A skill the agent extracts from one session can become a reusable procedure for future work, good or bad. Periodically reviewing your skills is the equivalent of reviewing a teammate’s pull requests.

For tighter control, enable skills.write_approval: true. Hermes can then stage proposed skill creations, edits, patches, and deletions instead of immediately applying them. You can inspect pending changes and their diffs before deciding what becomes part of the agent’s permanent playbook.

Watch the attack surface as it grows

More tools and more autonomy mean more ways things can go wrong: prompt injection, malicious tool instructions, compromised skills, or an agent quietly receiving more access than a task requires.

Treat web pages, emails, chat messages, files, and third-party skill instructions as untrusted input. An agent with tool access can encounter malicious instructions inside the content it is processing, even when your own prompt is perfectly safe.

Scope credentials and permissions to the minimum required, isolate execution where practical, and require approval for destructive or persistent changes. Agent security is a fast-moving area of practice, not a solved problem.

Build the underlying muscles

None of this is Hermes-specific; it’s the same skill set anyone steering an AI agent needs:

• Clear goal-setting.
• Spotting when a tool’s output is wrong.
• Enough domain knowledge to know what “right” looks like

Courses like agentic AI engineering track or MCP training build that foundation with any agent framework, Hermes included.

Go from reading about Hermes to running it

Everything above gets you oriented, but actually setting up a persistent agent (picking a runtime, wiring up Telegram, writing your first custom skill, locking down permissions before you give it real access) is where most people stall out on documentation alone.

The following course on Udemy walks through the whole path: local, Docker, or VPS installation, connecting LLMs and Telegram, managing memory and personality files, building and self-improving your own skills, MCP integration, cron automation, and a full section on agent security — jailbreaks, prompt injection, tool poisoning — before you put an agent in charge of anything important.

Keep a human in the loop

Autonomy without oversight is a risk, not a flex. Hermes can execute, schedule, and invent skills, but you still set goals, review risky actions, and decide what becomes permanent memory. Instructors and expert-led practice accelerate tool use, evaluation, and governance, not just demos.

Start small with one recurring workflow, measure whether the skill loop saves time, then widen the surface area. The self-improving AI agent only compounds if you stay editor-in-chief of what it learns.

FAQ

What exactly is Hermes Agent?

It is an open-source, self-hosted agent runtime with persistent memory and a built-in learning loop.

Is Hermes Agent free to use?

The agent software is MIT-licensed and free to install; you still pay for whatever model API or local computer you connect.

How does the Hermes Agent learning loop work?

After complex work, Hermes can save and refine reusable skills, update curated memory, and recall relevant information from previous sessions. That gives future runs better procedural and contextual knowledge without retraining the underlying language model.

Can you message Hermes Agent from apps like Telegram or Discord?

Yes. A single gateway process can connect Telegram, Discord, Slack, WhatsApp, Signal, and more once CLI chat is solid.

What is the difference between Hermes Agent and OpenClaw?

Both are open-source agent runtimes with overlapping capabilities. Hermes puts autonomous skill creation, skill refinement, curated memory, and cross-session learning at the center of its architecture. OpenClaw emphasizes a broad orchestration and integration ecosystem, including automations, background tasks, subagents, and Task Flow. Hermes also provides a documented migration path for users moving from OpenClaw.

Can Hermes Agent control desktop applications?

Yes. Hermes supports computer-use capabilities that allow an agent to interact with desktop applications on supported macOS, Windows, and Linux environments. Because computer use substantially expands what an agent can access and change, it is especially important to use appropriate permissions and isolation.