On February 25, a team called Nous Research quietly pushed a v0.1.0 on GitHub. The early Hermes model had only one line of installation command and a one-sentence product positioning: “An agent that grows with you.”

At that time, very few people noticed it, even though Nous Research had a certain reputation in the model community. Their Hermes series models had accumulated 33 million downloads on HuggingFace, but the attention of the entire developer community was focused on the revered OpenClaw "Little Lobster". In 33 days, it surpassed React to become the number one in history, with "Little Lobster" becoming the fastest-growing project in GitHub history, peaking at 710 stars per hour. However, during this time, security researchers continuously disclosed vulnerabilities at an average of 2.2 CVEs per day within the same time frame, accumulating 138 security vulnerabilities over 63 days. The entire community began to rethink a question: can this thing really be used in production environments?

In this context, the competing Hermes Agent finally had an opportunity and entered its first rapid growth period.

Hermes built a one-click migration tool from OpenClaw into its code. The batch of developers fleeing from OpenClaw needed a place to land, and Hermes Agent became a well-recommended choice.

Thus, starting from early March, Hermes Agent burst onto GitHub Trending, peaking at 11th place, with stars surpassing 2200. AwesomeAgents referred to it as “the most ambitious open-source Agent release since 2026,” and currently, Hermes has 69.9k stars and 9k forks on GitHub.

Today, Rhythm BlockBeats discusses what makes this Agent different.

What is Hermes Agent?

Hermes Agent is a self-evolving AI intelligent entity developed by Nous Research, and it is currently the only Agent with a built-in learning feedback loop.

It can automatically create skills from usage experiences, continuously improve these skills during use, actively solidify knowledge into reusable assets, retrieve its past dialogue history, and deepen its understanding of you as a user over multiple conversations.

So, simply put, the biggest advantage of Hermes Agent is: the more you use it, the smarter and more user-friendly it becomes.

Its positioning is not as a programming assistant bound to an IDE, nor merely a chat interface for a single API, but as a truly resident agent on your server that can remember what it learns and becomes more capable the longer it runs.

Nous Research has positioned itself as an open-source-first, decentralized AI lab from the beginning, aiming to build AI that users can control autonomously, rather than concentrating intelligence in a few closed companies. Their early work focused on the Hermes model series, while also investing heavily in infrastructure and system-level developments. They explored DisTrO technology for model training across globally distributed consumer-grade GPUs, as well as simulation environments for multi-agent interactions and long-range behaviors, such as WorldSim and Doomscroll.

The Hermes Agent team is the same group that developed the Nomos and Psyche series of models.

What are the Useful Tools?

The core mechanism of Hermes Agent is its memory system and skill system. The Agent maintains two streamlined core files: MEMORY.md stores environmental information, agreements, and experiences summarized from past tasks; USER.md stores your preferences and communication style. These two files are automatically injected into the system prompt at the beginning of each session, serving as the Agent's “long-term working memory.” Additionally, all historical sessions are stored in a SQLite full-text search database, allowing the Agent to retrieve dialogue content from weeks ago.

In terms of the skill system, each time a complex task is completed (usually more than 5 tool calls), the Agent automatically creates a structured Markdown “skill document” that records the operation steps, known content, and verification methods for future reuse. Skill files follow a progressive disclosure model: the Agent only views the skill name and description (around 3000 tokens) by default, fully loading the content of a skill only when needed, thereby controlling token consumption.

On the tools side, Hermes Agent has over 40 built-in tools that include web search, browser automation, visual understanding, image generation, text-to-speech, and support for setting scheduled tasks using natural language, allowing the Agent to automatically perform periodic tasks such as report generation, data backup, and system monitoring unattended.

The most popular tools, which are the ones used most frequently by community users, have the most feedback, and are prioritized based on Hermes' functional architecture and typical developer community needs, are these several tools:

Hindsight is currently the most popular single tool in the ecosystem and is the officially recommended long-term memory plugin for Hermes. It automatically recalls relevant context before each LLM call and supports local PostgreSQL or cloud deployment. It has been integrated into Hermes as a native Memory Provider.

Anthropic-Cybersecurity-Skills is the skill package with the highest Stars in the ecosystem, including 753+ structured cybersecurity skills and fully mapping the MITRE ATT&CK framework, suitable for security research and penetration testing scenarios.

mission-control is currently the most popular Agent orchestration dashboard in the ecosystem, supporting Agent fleet management, task distribution, cost tracking, and multi-Agent collaborative workflows, recommended by the community as a standard for production-level deployment.

Hermes Agent Self-Evolution is an evolutionary self-improvement technology that uses DSPy + GEPA to optimize skills, prompts, and code.

Hermes Workspace is the native workspace of Hermes, integrating chat interface, terminal, and skill manager, making it the most popular graphical entry point.

Additionally, it can derive independent sub-Agents, each with its own dialogue context, independent terminal, and Python RPC script, achieving a zero-context cost parallel pipeline.

In terms of infrastructure flexibility, it supports six terminal backends: local running, Docker, SSH remote, Daytona serverless, Singularity containers, and Modal cloud functions. Daytona and Modal will sleep during idle times, costing almost nothing. You can run it on a $5 VPS or GPU cluster, issuing commands through Telegram to have it work on cloud servers you never directly SSH into.

Hermes Agent currently poses the most direct competition to OpenClaw, as both are open-source Agent frameworks aimed at developers.

The architectural philosophies of the two are entirely different: OpenClaw’s design core is a “control plane,” a unified long-running process that manages sessions, routing, tool execution, and state—everything flows through this central controller. Hermes, on the other hand, builds around the execution loop of the Agent itself, surrounding this “do, learn, improve” repeated cycle with gateways, schedulers, runtime tools, and more.

The difference in skill systems is particularly significant: the skills of OpenClaw are mostly manually written, loaded from different levels such as workspace, personal, shared, or plugins; while Hermes’ approach is to allow the Agent to generate skills from experience, forming a true autonomous learning feedback loop.

How to Install and Use

Getting started is extremely simple. A single command “curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash” can complete the installation, supporting Linux, macOS, and WSL2. Hermes Agent will automatically complete all configuration without manual intervention.

Hermes Official Website

After the installation of Hermes Agent is complete, run “hermes setup” to start the guide, choose your model provider (supporting Nous Portal, OpenRouter, OpenAI, or any custom endpoint), connect your messaging platform (Telegram, Discord, Slack, or WhatsApp), and then start the first conversation. From the first interaction, Hermes Agent immediately enters learning mode, starting to build memory, create skills, and becomes more capable after each conversation.

Core commands for daily use include:

hermes (start conversation),

hermes model (select LLM provider and model),

hermes tools (configure which tools to enable),

hermes gateway (start messaging gateway, connecting to platforms like Telegram, Discord, etc.),

hermes setup (run the complete setup wizard to configure everything at once),

hermes claw migrate (migrate from OpenClaw),

hermes update (update to the latest version),

hermes doctor (diagnose problems);

Hermes Agent is suitable for scenarios including: general AI assistants that need to remember context across conversations and continuously improve capabilities; custom Agent workflows that require the use of tools, plugins, MCP servers, browsers, or Shell; deploying Agents on local hardware, cloud VMs, or low-cost serverless infrastructure; and persistent assistant scenarios requiring cross-platform searchable dialogue history and learned skills.

More specifically, you can use it to converse with it on Telegram while it executes tasks on cloud VMs, sets up automation, and pushes reports to any platform, takes over periodic tasks; or connect it to Slack or Discord to provide AI collaboration support for the entire team; or utilize its trajectory export function to generate training data for the next generation of tool-calling model RL training.

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