Every AI agent you have used in the past fundamentally suffers from a critical limitation, forgetfulness. The moment you close the chat window or the session times out, the agent resets to a blank slate. It forgets your preferences, your project details, the specific constraints of your workflow, and the lessons learned during previous interactions. Hermes was engineered to solve this specific problem by being built to remember, learn, and improve continuously. The longer it runs, the more useful it becomes, evolving from a simple tool into a specialized partner tailored to your specific needs.

Imagine for a moment that you had to hire a brand new employee every single morning. Every day, you would be forced to spend the first hour explaining who you are, what your company does, what projects are currently active, and how you prefer to work. This employee might be incredibly smart, helpful, and capable, but by the end of the day, they suffer total amnesia. When you arrive the next morning, they have absolutely no recollection of who you are or what you worked on yesterday. You have to start over from the very beginning.

While this scenario sounds absurd for a human workforce, it is exactly how almost every AI agent works today. Each session starts from absolute zero. The agent possesses no memory of what it did last week, let alone last month. It does not know that you prefer concise bullet points over long, winding paragraphs. It does not remember that your internal project requires a specific file naming convention. It does not recall that a particular coding approach or strategy failed the last time you tried it, so it suggests the same mistake again. For the user, every conversation feels like the first conversation, leading to a repetitive and inefficient cycle of re context.

Nous Research, an AI research lab deeply focused on open source models and agent infrastructure, built Hermes Agent to solve this exact problem. The core concept is straightforward to understand but profound in its implications. Hermes is designed as an agent that grows more capable the longer it runs, specifically because it actually remembers and learns from its own accumulated experience.

With over 120k stars on GitHub, contributions from more than 700 contributors, and a current release at version 0.11.0, Hermes stands as one of the most actively developed and robust open source agents available today. It is completely free to use, runs entirely on your own infrastructure to ensure privacy, and is compatible with over 200 different AI models, giving you unprecedented control over your AI operations.

Most commercial AI agents are what software engineers refer to as stateless. This technical term means that the agent does not carry anything forward from one session to the next. While building stateless systems is simpler and cheaper for developers, it imposes a high cost on the user because the agent never improves from experience. It treats every interaction as an isolated event rather than part of an ongoing relationship.

Hermes is built around a fundamentally different architecture that Nous Research calls a closed learning loop. This consists of four interconnected systems that work together to ensure the agent gets smarter over time.

When you work with Hermes and share something important, whether it is a specific project detail, a stylistic preference, or a hard constraint regarding compliance, Hermes stores this information. Crucially, it does not store it just for the current session. It stores it permanently in a dedicated long term memory system.

Think of this like a notebook that the agent keeps and updates continuously. Every time you initiate a session, the agent reads through this notebook to ground itself in your context before it starts working. As you interact and it learns new useful information, it adds entries to the notebook automatically.

However, what truly differentiates this from a simple static notes system is that Hermes nudges itself. At regular intervals, it autonomously reviews its own memory and asks critical questions, such as "Is this information still relevant?", "Does this need updating based on recent events?", or "Did I learn something today that is important enough to save for the future?". This self maintenance process is entirely automatic. You do not need to manage it, clean it up, or organize it. The agent takes full responsibility for maintaining its own knowledge base.

Why this matters at scale: In a large enterprise setting, the cost of re explaining context is enormous and often hidden. Every time a new analyst, assistant, or team member joins a project, there is a significant onboarding cost in terms of time and lost productivity. AI agents running on standard stateless architectures impose that exact same onboarding cost every single time a session is started. By eliminating this need to repeat yourself, Hermes drastically reduces friction and allows workflows to proceed immediately without the usual warm up period.

One of the most powerful features of Hermes is its ability to create its own skills. When Hermes completes a complex task, for example, running a comprehensive competitive analysis across five different industry reports, it does not just move on. It evaluates the approach it used to solve the problem. It asks itself, "Was this approach efficient enough to reuse in the future?". If the answer is yes, it creates a skill, which is a structured, reusable procedure stored in its own skill library.

The next time a similar task arises, Hermes does not need to re derive the approach from scratch. It retrieves the stored skill and applies it directly to the new problem. This capability represents the massive difference between a junior employee who has to figure everything out fresh each time and a senior employee who has spent years building up a reliable playbook of standard operating procedures.

More importantly, these skills improve during use. If Hermes uses a skill and the result is not quite right or could be optimized, it revises the skill based on what actually happened during the execution. The procedure gets sharper and more efficient with each iteration.

This system is fully compatible with the open agentskills.io standard. This means the skills your agent creates can be exported and shared with the broader community. Conversely, skills that other teams have built and refined can be imported into your agent. The knowledge does not just compound within your own organization, it accumulates across the entire user base, creating a network effect of intelligence.

Hermes possesses the ability to search its own past conversations using a robust technology called FTS5, which is essentially a fast, full text search engine built directly into its memory system. When the agent encounters a new problem, it does not just guess at the solution based on its pre training. It searches its own history to see how it solved similar problems for you in the past.

However, it does not simply retrieve old sessions and paste them word for word. It passes the relevant past conversations through an AI summarization layer that intelligently extracts what is actually useful for the current situation. This process is much closer to how an experienced human engineer searches their memory, pulling out the relevant insight and the core logic without needing to recall the full transcript of the previous meeting.

A practical example: Consider a team that uses Hermes to assist with monthly financial reporting. In month one, the agent works through the process slowly, makes several adjustments based on user feedback, and learns the specific format and tone preferences required. By month six, when the user starts the reporting process, Hermes searches its history, instantly finds all the relevant context and formatting rules from the previous five months, and starts generating the report from a position of accumulated knowledge rather than from zero.

Hermes integrates deeply with Honcho, an open source user modeling system built by Plastic Labs. Honcho utilizes a sophisticated technique called dialectic modeling to build a progressively more accurate picture of how you think, what you care about most, and how you prefer to communicate.

This is not a static profile that you fill out once when you register. It is a dynamic, living model that updates continuously based on every single interaction you have with the agent. Over time, the agent's understanding of you deepens. After six months of regular use, Hermes has a genuinely sophisticated model of your working style, your vocabulary, and your preferences. It uses this model to calibrate every response, ensuring that the output feels natural and tailored specifically to you.

Understanding Hermes's architecture does not require an engineering background, but seeing how the pieces connect helps illustrate the robustness of the system.

The Gateway Layer acts as the universal interface for how you talk to Hermes. Whether you send a message from Telegram, Discord, Slack, WhatsApp, Signal, or a command line interface, a single gateway process handles all of it simultaneously. This allows for continuity across platforms. You can start a complex task from your terminal at the office, commute home, and check on its progress from your phone via Telegram. The agent does not lose context or drop the ball when you switch platforms.

The Agent Core is where the actual thinking happens. The memory system, the skills library, and the user model all reside here. This is the primary differentiator between Hermes and other agents. These three components run continuously as background processes, rather than just existing temporarily during a chat session.

The Tool Layer represents what the agent can actually act upon. Hermes includes over 40 integrated tools, covering web search, browser automation, vision capabilities, image generation, text to speech, file operations, and code execution. These tools are organized into configurable toolsets, allowing you to enable exactly what your workflow requires and leave the rest disabled to keep the system focused.

The Model Layer is the AI brain, the large language model that actually generates text and makes decisions. Hermes is designed to be model agnostic. It works seamlessly with OpenRouter's catalog of over 200 models, OpenAI, NVIDIA NIM, and any custom endpoint you might want to use. You can switch models with a single command. This requires no code changes and results in no vendor lock in.

The Execution Layer is where the agent physically runs in your infrastructure. Six different backend options provide flexibility from a local laptop to a massive cloud cluster. You can choose local execution for development, Docker for containerized isolation, SSH for remote machines, and Modal for serverless deployment where the agent hibernates between tasks to save money.

The most important enterprise property of Hermes is its ability to operate without a human in the loop. It runs as a service on your server, utilizing your cloud resources and your infrastructure. It can execute scheduled tasks unattended and deliver results to whatever platform your team uses, whether that is Slack, email, or a custom dashboard.

This differs significantly from most standard AI tools, which are session based. In those tools, a user must open a chat, request a result, and then close the tab. Hermes runs like a background service or a daemon. You configure it once, it runs continuously, and it reports back only when it has results or encounters a critical issue.

Practical example: A team can configure Hermes to run a competitive monitoring process every morning at 6 AM. It can pull relevant industry news, compare it against a list of tracked competitors, and deliver a concise summary to the team's Slack channel before 9 AM. No human needs to initiate it, log in, or trigger the process. It just runs.

One of the most significant and under appreciated costs in any organization is knowledge loss. When a key team member leaves a project or the company, their institutional knowledge often leaves with them. This includes the shortcuts they knew, the specific processes they had figured out, and the nuanced lessons learned from past mistakes.

An agent with persistent memory and a growing skill library accumulates that knowledge in a format that stays within the company. If the person who set up a particular workflow leaves, the agent still retains the knowledge of how that workflow runs. The skills it built are preserved. The memory of past decisions and why they were made remains searchable and accessible to the rest of the team.

This is not intended to be a replacement for human knowledge management, but rather a powerful backstop. It serves as a running, editable record of what the agent has learned while working alongside your team, ensuring that valuable insights are not lost to turnover.

Enterprise procurement teams are acutely aware of the risks associated with vendor lock in. If a business builds a critical workflow around a proprietary agent that only works with one specific model provider, they have created a dependency that is expensive and difficult to exit later.

Hermes's model agnostic architecture mitigates this risk. It allows you to switch the underlying AI model without having to rebuild your workflows, retrain your skills, or lose your accumulated memory. If a better model is released by a different provider, you can adopt it immediately with a single command. Your agent's accumulated knowledge and skills carry forward regardless of which specific model is currently running it.

For organizations with strict data residency requirements, regulatory constraints such as GDPR or HIPAA, or security policies that prevent sending proprietary data to third party cloud services, Hermes's self hosted architecture is essential. Your data never leaves your infrastructure. You maintain full control over what data is processed and what, if anything, is sent externally.

The Docker and SSH backends make integration with existing IT environments straightforward and secure. Additionally, the Singularity backend supports high performance computing environments that are common in research labs, universities, and scientific organizations, bringing advanced AI capabilities to secure, high security enclaves.

For complex tasks that can be broken down into parallel pieces, such as processing multiple documents simultaneously, running analyses across different data sets, or handling multiple distinct workstreams at once, Hermes can spawn isolated subagents. Each subagent operates independently with its own conversation context and terminal instance.

Python scripts can call tools via Remote Procedure Calls, keeping context costs low even as the number of parallel tasks scales up significantly.

Enterprise example: A legal team using Hermes for contract review can configure it to spawn one dedicated subagent per contract during a batch review process. Each subagent reads and analyzes a specific contract independently and in parallel. The results are then collected and consolidated by the primary agent. A process that would typically take many hours of sequential work can be completed in a fraction of the time.

Hermes includes a built in cron scheduler that allows you to automate recurring tasks. You describe the schedule in plain, natural language, and Hermes handles the technical execution. Daily reports, nightly database backups, weekly security audits, and monthly summaries can all be configured without writing a single line of scheduling script code.

Results can be delivered automatically to whichever platform the recipient prefers, whether that is Slack, email, Telegram, or Discord. The scheduling and delivery configuration is performed once and then runs unattended indefinitely.

The tool layer is what fundamentally separates an AI that merely talks about tasks from an AI that can actually execute them. Here is a detailed look at what Hermes can act upon directly.

Web and Research

Content and Communication

Code and Files

Integration

Scheduling

Tools are grouped into configurable toolsets. You have the control to turn on exactly what your workflow needs and disable everything else. This keeps the agent focused, reduces the potential for errors, and avoids unnecessary tool overhead.

Installation is achieved with a single command:

This installation script works seamlessly on Linux, macOS, and WSL2 (Windows Subsystem for Linux). The installer automatically handles all dependencies, including Python, Node.js, and other necessary libraries. After the installation completes, you can proceed with the following steps:

The setup wizard guides you through the entire process in a single pass. It covers provider selection, model choice, and platform connections. You do not need to manually edit complex configuration files or scripts.

Day to day management commands:

Inside a conversation, slash commands work universally on both CLI and messaging platforms:

Nous Research did not build Hermes solely as a commercial product. They built it as a sophisticated research instrument designed to advance the field of artificial intelligence.

Hermes includes features such as batch trajectory generation, which is a way to automatically create high quality training data by recording exactly how the agent solves complex tasks. It also includes Atropos RL environments, which allow the agent to be used as a training environment for reinforcement learning algorithms. Furthermore, it includes trajectory compression, a technique for condensing complex, multi step agent behavior into formats that are useful for training the next generation of AI models.

The practical implication of this research foundation is that the skills your agent creates and the trajectories it generates while solving real world problems can feed back into model improvement. The agentskills.io open standard creates a shared ecosystem where skill quality improves across the entire user community, not just within individual isolated deployments.

This represents a longer term investment than most commercial agent projects. Most products optimize for capability at the exact moment of deployment. Nous Research is optimizing for capability over time, both within individual deployments through the learning loop, and across the entire field through the underlying research infrastructure.

Teams running recurring workflows that currently require re explaining context every session. The break even point is lower than you might expect. A workflow you run weekly that currently costs 20 minutes of setup time is highly worth automating into a persistent skill.

Organizations with data residency or security requirements that strictly prevent the use of cloud based AI services. Because Hermes runs entirely on your infrastructure, it is an ideal solution for secure environments.

Teams that need model flexibility and do not want to be forced to rebuild their workflows every time a new, better model ships from a competitor.

Research and technical teams doing heavy batch processing, parallel analysis, or any work that benefits from spawning multiple agents working simultaneously on different parts of a problem.

Operations teams that want scheduled, unattended automation with delivery to the platforms their team already uses daily.

The learning loop is the primary feature worth watching closely. The difference between an agent that resets every session and an agent that accumulates knowledge is barely noticeable on day one. However, it becomes extremely noticeable at month six. Teams that start building with Hermes now are compounding that advantage from the very beginning, creating a significant gap between themselves and competitors still relying on stateless tools.

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