On February 25, a team called Nous Research quietly pushed v0.1.0 on GitHub. Initially, the Hermes model was introduced with just a single installation command and a one-line product positioning: "An agent that grows with you."

At the time, few noticed it—despite Nous Research's established reputation in the model community, their Hermes series had already accumulated 33 million downloads on HuggingFace. However, the entire developer ecosystem remained fixated on OpenClaw—"the crayfish"—which was revered as a deity. In just 33 days, OpenClaw surpassed React to become the fastest-growing project in GitHub history, peaking at 710 stars per hour. Yet, during this same period, security researchers disclosed vulnerabilities at an average rate of 2.2 CVEs per day, totaling 138 security flaws over 63 days. The community began re-evaluating a critical question: Can this even be used in production environments?

Under such circumstances, Hermes Agent—as a direct competitor—finally found its opening and entered its first major growth phase.

Hermes integrated a one-click migration tool from OpenClaw into its codebase. Developers who had left OpenClaw sought a new home, and Hermes Agent quickly became a word-of-mouth favorite.

From early March onward, Hermes Agent surged into GitHub Trending, reaching as high as #11 and surpassing 2,200 stars. AwesomeAgents dubbed it "the most ambitious open-source agent release to date in 2026." As of now, Hermes has 69.9k Stars and 9k Forks on GitHub.

Today, BlockBeats speaks with you about what makes this agent truly different.

Hermes Agent is an AI autonomous agent built by Nous Research, and the only one currently featuring an embedded learning loop.

It autonomously creates skills from usage experience, continuously improves them during operation, proactively crystallizes knowledge into reusable assets, retrieves past conversation histories, and deepens its understanding of you across multiple sessions.

In short, the core advantage of Hermes Agent is simple: the more you use it, the smarter and smoother it becomes.

Its positioning is not as an IDE-bound coding assistant or a chat wrapper for a single API—but as a true autonomous agent residing on your server, capable of remembering what it learns and growing stronger the longer it runs.

Nous Research has always positioned itself as an open-source-first, decentralized AI laboratory, aiming to build user-controlled AI rather than concentrating intelligence in the hands of a few closed corporations. Their early work focused on the Hermes model series, while heavily investing in infrastructure and system-level foundations. They also explored DisTrO—a technology enabling distributed training across global consumer-grade GPUs—and developed simulation environments like WorldSim and Doomscroll for multi-agent interaction and long-term behavioral modeling.

The Hermes Agent team consists of the same group behind Nomos, Psyche, and other foundational models.

The core mechanism of Hermes Agent lies in its memory and skill systems. The agent maintains two minimal yet critical files: MEMORY.md stores environmental context, agreements, and insights derived from past tasks; USER.md holds your preferences and communication style. These files are automatically injected into the system prompt at the start of each session, serving as the agent’s "long-term working memory." Additionally, all historical conversations are stored in an SQLite full-text search database, enabling retrieval of dialogues weeks old.

Regarding the skill system, after completing complex tasks (typically involving five or more tool calls), the agent autonomously generates a structured Markdown "skill document" recording procedures, known facts, and validation methods for future reuse. Skill files follow a progressive disclosure pattern: by default, the agent only accesses the skill name and description (~3000 tokens), loading the full content only when needed—thus controlling token consumption.

On the tool front, Hermes Agent includes over 40 built-in tools covering web search, browser automation, visual understanding, image generation, text-to-speech, and natural language-based scheduling of recurring tasks—enabling the agent to autonomously execute report generation, data backup, and system monitoring without human supervision.

The most popular tools—those used most frequently by the community, receiving the highest feedback, and aligned with Hermes’ functional architecture and typical developer needs—are these:

Hindsight is currently the hottest single tool in the ecosystem and the official recommended long-term memory plugin. It automatically recalls relevant context before every LLM call, supports local PostgreSQL or cloud deployment, and is now natively integrated into Hermes as a Memory Provider.

Anthropic-Cybersecurity-Skills is the highest-starred skill pack in the ecosystem, containing over 753 structured cybersecurity skills fully mapped to the MITRE ATT&CK framework—ideal for security research and penetration testing scenarios.

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

Hermes Agent Self-Evolution is an evolutionary self-improvement technique leveraging 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.

Beyond this, it can spawn independent sub-agents, each with its own conversation context, dedicated terminal, and Python RPC scripts, enabling zero-context-cost parallel pipelines.

In terms of infrastructure flexibility, it supports six backend terminals: local execution, Docker, SSH remote, Daytona serverless, Singularity container, and Modal cloud functions. Daytona and Modal enter sleep mode when idle, resulting in nearly zero cost. You can run it on a $5 VPS or GPU cluster, issue commands via Telegram, and have it operate on cloud servers you never directly SSH into.

Hermes Agent currently stands as the most direct competitor to OpenClaw—both being open-source agent frameworks aimed at developers.

Their architectural philosophies differ fundamentally: OpenClaw centers around a "control plane"—a unified long-running process managing sessions, routing, tool execution, and state, with all operations flowing through this central controller. Hermes, in contrast, builds everything—gateway, scheduler, runtime—around the agent’s own execution loop: a continuous cycle of “act, learn, improve.”

The difference in skill systems is especially pronounced: OpenClaw’s skills are mostly manually written and loaded from various levels—workspace, personal, shared, or plugins. Hermes takes a different approach: letting the agent generate skills autonomously from experience, forming a genuine self-learning closed loop.

Setup is extremely simple. A single command — curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash — completes installation, supporting Linux, macOS, and WSL2. Hermes Agent auto-configures everything—no manual intervention required.

After installation, run hermes setup to launch the guided setup. Choose your LLM provider (supporting Nous Portal, OpenRouter, OpenAI, or any custom endpoint), connect your messaging platform (Telegram, Discord, Slack, or WhatsApp), then begin your first conversation. From the very first interaction, Hermes Agent immediately enters learning mode—building memory, creating skills, and becoming increasingly capable with every session.

Core daily commands include:

hermes (start a conversation),

hermes model (select LLM provider and model),

hermes tools (configure which tools to enable),

hermes gateway (launch message gateway to integrate platforms like Telegram, Discord),

hermes setup (run complete setup wizard, configure all settings at once),

hermes claw migrate (migrate from OpenClaw),

hermes update (update to latest version),

hermes doctor (diagnose issues);

Hermes Agent suits scenarios requiring cross-session contextual memory and continuous capability improvement—general-purpose AI assistants; custom agent workflows combining tools, plugins, MCP servers, browsers, or shell commands; deploying agents on local hardware, cloud VMs, or low-cost serverless infrastructure; and persistent assistants maintaining searchable conversation history and learned skills across platforms.

More specifically, you can use it to converse via Telegram while having it execute tasks on a cloud VM, set up automations, and push reports to any platform; delegate periodic tasks to it; integrate it into Slack or Discord to provide AI collaboration support for entire teams; or leverage its trajectory export functionality to generate training data for next-generation tool-calling models using RL training.