Hermes Agent: The Self-Improving AI That Remembers Everything

By early 2026, Hermes Agent had become one of the most visible open-source agent frameworks from Nous Research. It does not look like a product launch. It looks like a bet.

The person worth naming is Teknium, also known as @Teknium: Nous Research co-founder and lead engineer on Hermes Agent. His public GitHub profile describes him as a Python programmer, AI enthusiast, and Nous Research co-founder, which matters because Hermes is not a detached corporate lab demo. It comes from the same open-source model-building culture that made the Hermes model family hard to ignore.

Hermes Agent writes its own skills, remembers everything across sessions, and is strongest when it runs close to your actual working environment. It reportedly crossed 100,000 GitHub stars in seven weeks. In May 2026, it overtook OpenClaw as the most active agent framework on OpenRouter, processing 224 billion tokens daily to OpenClaw's 186 billion.

The bet is simple: AI agents should not be stateless chatbots that forget everything when the tab closes. They should be persistent systems that compound capability the longer they run.

The Problem: AI Agents Have Goldfish Memory

Every major AI assistant suffers from the same flaw. You spend an hour debugging a problem with ChatGPT. You close the tab. You come back tomorrow. It remembers nothing. Not the file structure, not the failed approaches, not the constraints you explained three times.

This is not a bug. It is architecture. Most agents are stateless by design. They process a prompt, generate a response, and discard the context. Any "memory" is bolted on: a vector database of snippets, a JSON file of facts, or a summary written by the model itself. These systems do not learn. They retrieve.

The result is maintenance fatigue. Developers using older agent frameworks such as OpenClaw report spending more time managing skills and memory files than building. The agent does not get better at your workflows. You get better at managing the agent.

Nous Research looked at this and asked a different question: what if the agent maintained itself?

How Hermes Works: The Four-Layer Memory Stack

Hermes Agent runs on a memory architecture that most developers have never seen in an open-source tool. It has four distinct layers, each with a different temperature and purpose.

The critical layer is number four. After complex tasks, Hermes can turn repeated workflows into reusable skills. Over time, those skills become a procedural memory layer: not just facts about the user, but tested ways of doing work. The next time it encounters a similar task, it does not reason from scratch. It loads the skill.

This is not RAG. Retrieval-Augmented Generation pulls snippets from a database and hopes they are relevant. Skills are procedural: step-by-step workflows that the agent wrote for itself, tested, and improved. They are stored using the agentskills.io open standard, making them portable across Hermes instances.

The Learning Loop in Practice

Here is what this looks like in practice. A developer asks Hermes to set up a new React project with TypeScript, Tailwind, and a specific folder structure. The agent does it. Then it writes a skill.

The skill file contains the exact commands, the folder structure, the common errors, and the fixes. The next time the developer asks for a React setup, Hermes loads the skill and completes the task in seconds instead of minutes. After seven uses, the agent has refined the skill twice based on edge cases it encountered.

This is the compounding effect. The agent does not just remember what you told it. It gets better at helping you.

Hermes vs OpenClaw: Architecture vs Integration

OpenClaw is the incumbent. Its strength is breadth: integrations, community workflows, and marketplace-style extensibility. It connects to 50+ platforms and still has more GitHub stars than Hermes.

Hermes is the specialist. It connects to 20+ platforms, has 156,000 stars, and generates its own skills. The comparison is not about feature count. It is about philosophy.

OpenClaw's approach is gateway-first: connect everything, let the community build skills. Hermes' approach is agent-first: give the agent memory, skills, profiles, and long-running workflows as first-class architecture.

The numbers that matter are recall behavior and architecture. Hermes uses SQLite with full-text search so it can search past sessions locally without dumping every old conversation into the model context. OpenClaw stores conversation history in JSONL files that must be loaded into context, which scales poorly as history grows.

On security, Hermes supports container isolation through Docker and other sandboxed backends. OpenClaw's marketplace model allows third-party skills, which adds trust complexity: you are running code written by strangers. Hermes is less marketplace-centric. Its strongest skills are self-generated or installed from trusted sources.

Where Hermes Runs

Hermes is designed to live where you work. It supports six terminal backends:

The decoupled interface means you can interact with the agent via Telegram while it runs on your local machine, a workstation, or a cloud VM. You start a task from your phone, the agent works for an hour, and you get a notification when it is done. This is not a chatbot. It is a persistent worker with a memory.

The Gateway: 20+ Platforms

Hermes connects to Telegram, Discord, Slack, WhatsApp, Signal, Matrix, SMS, Microsoft Teams, Google Chat, and email. It also has a voice mode for real-time interaction on CLI, Telegram, and Discord voice channels.

The gateway is not the point. It is the pipe. The point is that the agent maintains state across all of them. You can start a conversation on Slack, continue on Telegram, and finish via voice on Discord. The agent knows what you are talking about because its memory is not tied to a platform. It is tied to you.

MCP and the Tool Ecosystem

Hermes ships with a broad built-in tool system, including web search, browser automation, file operations, terminal access, vision, image generation, cron, delegation, and messaging. It also supports the Model Context Protocol (MCP), which lets it connect to additional external tool servers safely.

The delegation feature is worth noting. Hermes can spawn isolated subagents for parallel workstreams. You ask it to research five different databases. It spawns five subagents, each with its own context and tools. They run in parallel. You get a consolidated answer.

This is where local execution becomes interesting. Hermes benefits from sitting near your files, shells, repos, browsers, and long-running processes. Cloud instances are useful for always-on work, but the best version of Hermes is the one wired into the machine where your real work happens.

The RL Pipeline: Atropos

Nous Research has also worked on reinforcement-learning infrastructure such as Atropos, which matters because tool-using agents need more than good chat behavior. They need reliable multi-step execution. But Hermes' practical advantage today is simpler: it gives the model memory, tools, skills, and durable workflows.

The broader ReAct pattern (Reasoning and Acting) is the right frame for understanding why this matters. Successful tool-use trajectories can become reusable procedures, so the agent gets a better starting point the next time it sees a similar task.

Growth and Adoption

The growth curve is unusual. Most open-source projects see a spike at launch, then a plateau. Hermes accelerated after week 5. The reason is word-of-mouth from developers who noticed their agent was getting faster, not slower, the more they used it.

The OpenRouter numbers tell the same story. Hermes processes 224 billion tokens per day, more than OpenClaw's 186 billion. It is the most active agent framework by token volume, despite having half the GitHub stars.

The Hybrid Setup: Using Both

The most sophisticated users do not choose. They run both agents simultaneously via the Agent Communication Protocol (ACP). OpenClaw handles orchestration: planning, task decomposition, multi-platform routing. Hermes handles execution: fast, repeatable task loops where its learning advantage compounds.

This is not a compromise. It is an admission that different problems need different architectures. OpenClaw is better at connecting things. Hermes is better at learning things. Together, they form a system that neither can build alone.

Why Persistent Agents Change Everything

The shift from stateless to persistent agents is not a feature upgrade. It is a category change. Stateless agents are tools. Persistent agents are teammates.

A tool does not get better with use. A screwdriver is a screwdriver. A teammate learns your preferences, anticipates your needs, and improves at the tasks you give them. That is what Hermes is building toward.

The implications for software development are significant. If your agent remembers every bug, every architectural decision, every constraint, it stops being a search engine and starts being a collaborator. It can maintain codebases it did not write. It can onboard new developers by explaining decisions made six months ago. It can spot patterns across projects that no human would notice.

This is the local-first insight. Hermes does not need to live in a remote product silo because its value comes from proximity: your files, your shell, your browser, your history, your tools. It loads what matters: your skills, your history, your patterns. The rest is noise.

The Risks

No system is perfect. Hermes has had disclosed security issues, which is normal for software that touches files, terminals, browsers, and messaging platforms. The memory system, while fast, is opaque: you cannot easily inspect what the agent knows about you without querying it. And the skill generation, while impressive, can produce overfitted workflows that work for your specific setup but break on generic cases.

The bigger risk is dependency. The more you use Hermes, the more it knows about your workflows, your code, your preferences. Migrating to another agent means losing that accumulated knowledge. The agentskills.io standard helps, but procedural memory is not fully portable yet.

The Point

Hermes Agent is not the most connected agent. It is now the more-used agent by OpenRouter token volume, and it is the one that learns.

For developers tired of managing agents instead of building, that is the feature that matters. The 156,000 GitHub stars and 224 billion daily tokens are side effects of a simple truth: when your agent remembers everything, you stop repeating yourself.

Last updated: May 19, 2026

Related Reading:

• The Complete OpenClaw Guide: Proactive Autonomous Agents
• Your AI Team Across Every Channel
• Hermes Agent Documentation