On February 25, a team called Nous Research quietly pushed a v0.1.0 to GitHub. Initially, the Hermes model had only a one-line installation command and a one-liner product positioning: "An agent that grows with you."

At that time, few people paid attention to it, even though Nous Research had a certain reputation in the model circle, and their Hermes series models had already accumulated 33 million downloads on HuggingFace. However, the entire developer community's attention was focused on the revered OpenClaw "Lobster." Surpassing React in just 33 days, the "Lobster" became the fastest-growing project in stars in GitHub history, with a peak of 710 stars per hour. However, it was at this time that a security researcher continued to disclose vulnerabilities at an average rate of 2.2 CVEs per day during the same time window, accumulating 138 security vulnerabilities in 63 days. The entire community began to rethink a question: Can this thing really be used in a production environment?

Against this backdrop, Hermes Agent, as a competitor, finally found an opportunity and ushered in its first period of rapid growth.

Hermes embedded a tool in its code for one-click migration from OpenClaw. The developers who left OpenClaw needed a place to land, and Hermes Agent became a widely recommended choice.

So, starting from early March, Hermes Agent stormed into GitHub Trending, reaching as high as 11th place with over 2200 stars. AwesomeAgents dubbed it the "most ambitious open-source Agent release to date." Currently, Hermes' GitHub has 69.9k Stars and 9k Forks.

Today, Rhythm BlcokBeats will talk to everyone about what sets this Agent apart.

What is Hermes Agent?

Hermes Agent is a self-evolving AI agent built by Nous Research and is currently the only Agent with a built-in learning loop.

It can automatically create skills from usage experience, continuously improve these skills during use, proactively solidify knowledge into reusable assets, retrieve its own past conversation history, and deepen its understanding of you as a user through multiple sessions.

So, in simple terms, the biggest advantage of the Hermes Agent is: the more you use it, the smarter and more convenient it becomes.

Its positioning is not a programming assistant embedded in an IDE, nor a chat wrapper for a single API, but a truly self-resident agent on your server that can remember what it has learned, with its capabilities growing stronger over time.

Nous Research has positioned itself from the start as an open-source-first, decentralized AI lab with the goal of building user-controlled AI, rather than concentrating intelligence in the hands of a few closed companies. Their early work focused on the Hermes model series, with significant investments in infrastructure and system-level work. They also explored DisTrO technology for model training using globally distributed consumer-grade GPUs, as well as simulation environments for multi-agent interactions and long-term behaviors like WorldSim and Doomscroll.

The Hermes Agent team is the same group of people behind the Nomos and Psyche model series.

What are the useful tools?

The core mechanism of the Hermes Agent is its memory system and skill system. The Agent maintains two concise core files: MEMORY.md stores environment information, conventions, and summarized experiences from past tasks; USER.md stores your preferences and communication style. These two files are automatically injected into the system prompts at the beginning of each session, acting as the Agent's "long-term working memory." Additionally, all historical sessions are stored in an SQLite full-text search database, allowing the Agent to retrieve conversations from weeks ago.

Regarding the skill system, each time a complex task is completed (usually more than 5 tool calls), the Agent autonomously creates a structured Markdown "skill document," recording operation steps, known content, and verification methods for future reuse. The skill files follow a progressive disclosure pattern: the Agent defaults to only viewing the skill name and description (about 3000 tokens), loading the full content of a skill only when needed to control token consumption.

On the tooling side, the Hermes Agent comes with built-in support for over 40 tools, covering tasks such as web scraping, browser automation, computer vision, image generation, text-to-speech, and even natural language scheduling for automation tasks like report generation, data backups, system monitoring, and more.

Among these tools, the most popular ones—those most frequently used by the community, with high feedback and alignment with Hermes' architecture and developer community needs—are:

Hindsight is currently the most popular standalone tool in the ecosystem, serving as Hermes' recommended long-term memory plugin. It automatically recalls relevant contextual information before each long-lasting memory (LLM) call, supports local PostgreSQL or cloud deployment, and has been natively integrated as the Memory Provider within Hermes.

Anthropic-Cybersecurity-Skills is the ecosystem's top-starred skill set, featuring 753+ structured cybersecurity skills that map directly to the MITRE ATT&CK framework, making it suitable for security research and penetration testing scenarios.

mission-control is the most popular agent orchestration dashboard in the ecosystem, supporting agent fleet management, task distribution, cost tracking, and multi-agent collaborative workflows, highly recommended by the community as the standard for production deployments.

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

Hermes Workspace is the native workspace within Hermes, integrating a chat interface, terminal, and skill manager, serving as the most popular graphical entry point.

Furthermore, it can spawn individual child agents, each with its own conversation context, independent terminal, and Python RPC scripts, enabling zero-context-switching cost parallel pipelines.

In terms of infrastructure flexibility, it supports six terminal backends: local, Docker, SSH remote, Daytona serverless, Singularity container, and Modal cloud functions. Daytona and Modal go to sleep when idle, incurring minimal costs. You can run it on a $5 VPS or GPU cluster, issue commands via Telegram, and have it work on cloud servers where you never directly SSH into.

The Hermes Agent currently has the most direct competitive relationship with OpenClaw, both of which are open-source Agent frameworks for developers.

Their architectural philosophies are vastly different: OpenClaw's core design is a "control plane," a unified long-running process responsible for managing sessions, routing, tool execution, and state, with everything flowing through this central controller. Hermes, on the other hand, places the Agent's own execution loop at its core, building around this "do, learn, improve" iterative cycle with components like gateways, a job scheduler, and a tool runtime.

The difference in their skill systems is particularly significant: OpenClaw's skills are mostly manually authored, loaded from different levels such as workspace, personal, shared, or plugins; Hermes, on the other hand, aims for the Agent to generate skills from experience, forming a true autonomous learning loop.

How to Install and Use

Getting started is extremely simple. With a single command "curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash," you can complete the installation. It supports Linux, macOS, and WSL2. The Hermes Agent will automatically handle all configurations without manual intervention.

Once the Hermes Agent installation is complete, run "hermes setup" to start the setup wizard. Choose your model provider (supporting Nous Portal, OpenRouter, OpenAI, or any custom endpoint), connect to your messaging platform (Telegram, Discord, Slack, or WhatsApp), and then begin your first conversation. From the first interaction, the Hermes Agent immediately enters learning mode, starting to build memory, create skills, and become more capable after each session.

Core commands for daily use include:

hermes (start a conversation),

hermes model (select an LLM provider and model),

hermes tools (configure which tools are enabled),

hermes gateway (start message gateway, integrating with platforms such as Telegram, Discord),

hermes setup (run full setup wizard, configuring all content in one go),

hermes claw migrate (migrate from OpenClaw),

hermes update (update to the latest version),

hermes doctor (diagnose issues);

The Hermes Agent is suitable for scenarios such as: a general AI assistant that needs to remember context across sessions and continuously improve; a custom Agent workflow that combines tools, plugins, MCP servers, browsers, or shells; deploying the Agent on local hardware, cloud VMs, or low-cost serverless infrastructure; and scenarios that require a persistent assistant to maintain searchable conversation history and learned skills across platforms.

More specifically, it can be used to have a conversation with it on Telegram while having it perform tasks on a cloud VM, set up automation and push reports to any platform, or have it take over periodic tasks; integrate it into Slack or Discord to provide AI collaboration support for the entire team; or utilize its trace export feature to generate training data for RL training of the next-generation tool-calling model.