The AI That Makes Other AI Obsolete

Most popular AI agents, including widely used platforms like ChatGPT and Claude, operate with a fundamental flaw: they are stateless. Each interaction, every new session, begins from a completely blank slate. Users must consistently re-establish context, re-explain project goals, and repeat instructions, as the AI retains no memory of previous conversations or learned preferences. When you ask one of these agents a question today, and then the same question next week, it effectively starts from zero, having forgotten everything from your prior engagement.

This current, chaotic, but innovative AI agent landscape strikingly mirrors the nascent web browser wars of 1995. Just as companies like Netscape and Microsoft battled for dominance, today countless developers frantically build new agent frameworks. However, no industry consensus exists on the core functionalities that truly matter for these advanced AI tools, particularly concerning persistent memory and long-term learning. The ecosystem currently prioritizes a broad array of capabilities over the deep, continuous understanding that could genuinely transform user experience.

This pervasive amnesia carries a significant, often hidden, cost for individuals and businesses integrating AI into their workflows. Users waste valuable time reiterating preferences, re-explaining complex project parameters, and repeatedly uploading essential data. Without the ability to recall past conversations, understand evolving context, or build upon previous outcomes, these AI tools remain perpetually generic. They function as powerful, single-use calculators, demanding constant re-education rather than evolving into truly intelligent assistants.

This leads to a critical, unanswered question for the future of AI: what if an agent could remember? What if an AI could not only recall every past interaction, success, and failure, but also autonomously develop new skills and adapt to your unique working style over time? Imagine an AI assistant that truly understood your context, anticipated your needs, and became increasingly valuable with every passing day. This paradigm shift from a disposable utility to a continuously evolving, intelligent partner represents the next, essential frontier in artificial intelligence.

Hermes Agent, developed by Nous Research, directly confronts the 'amnesia problem' prevalent in popular AI agents. This open-source, self-improving AI agent framework stands out as the only one with a built-in learning system designed to genuinely improve over time, representing a new generation of AI.

Its core philosophy posits that an AI agent should learn from every interaction, steadily increasing its utility and value. This isn't merely a marketing claim; it's a fundamental architectural commitment that redefines agent capabilities, ensuring the agent becomes progressively more valuable the longer you use it.

Hermes represents a fundamentally different bet on how agents should operate. Most frameworks treat agents as ephemeral tools, resetting with each session, effectively starting from zero every time. Hermes, however, integrates a sophisticated, persistent multi-level memory system to combat this inherent limitation.

This robust memory system includes FTS5 Session Search with LLM Summarization, allowing the agent to quickly search and summarize all past conversations and tasks. It curates `MEMORY.md` and `USER.md` files, injecting environmental facts, lessons learned, and your specific preferences directly into its system prompt for consistent context.

Furthermore, Hermes leverages Honcho for advanced user modeling, building a dynamic profile of your preferences, decision history, and task patterns that persists across sessions. This architecture, primarily built in Python (92.5% of its codebase), enables deep integration and extensibility. Hermes also autonomously creates and refines "skills" from completed tasks. For instance, if you task it with analyzing monthly revenue from Stripe and HubSpot, it packages this intricate logic as a reusable, testable function, storing and refining it for subsequent, more efficient use.

This continuous learning transforms the user experience from a static utility into an evolving, personalized assistant. Instead of requiring constant re-instruction, Hermes understands context, anticipates needs, and proactively suggests optimizations.

Where agents like ChatGPT or Claude reset after each interaction, Hermes gets smarter. It tracks patterns in your working style, learns your preferences, and suggests new skills based on observed behavior. This compounding effect ensures the agent becomes increasingly tailored, efficient, and indispensable the longer you engage with it.

Hermes Agent fundamentally redefines AI interaction by learning and evolving with each engagement. Unlike typical stateless agents, Hermes doesn't discard its operational knowledge after a session. Instead, it meticulously records every step, decision, and tool invocation, transforming raw experience into tangible expertise over time. This continuous feedback loop allows Hermes to not just complete tasks, but to genuinely improve its future performance.

Central to this learning mechanism is the trajectory—a comprehensive, immutable record of every action Hermes takes to fulfill a request. This detailed log captures everything: the initial prompt, the internal thought process, the sequence of tools employed (such as web search, terminal commands, or API calls), the data exchanged, and the final output. Each trajectory serves as a granular blueprint of a solved problem, providing the agent with rich contextual data for later analysis.

Hermes doesn't just store these trajectories; it actively analyzes them. Its internal learning system sifts through accumulated task histories to identify recurring patterns, common problem-solving approaches, and inefficiencies. This deep analysis allows Hermes to abstract generalizable workflows from specific instances. It looks for sequences of actions that, when combined, consistently lead to a successful outcome, signaling potential for automation and optimization.

Upon identifying a robust, repeatable pattern, Hermes initiates the creation of a skill. A skill is essentially a packaged, reusable function derived from a successful trajectory, designed to address a specific type of task more efficiently. This process involves formalizing the logic, testing the newly formed skill against various scenarios to ensure reliability, and then storing it locally within the agent's knowledge base. For a deeper dive into its open-source architecture, consult Hermes Agent: AI That Learns & Grows With You | Open Source.

Once a skill exists, Hermes no longer starts from scratch when encountering a similar request. Instead, it intelligently recognizes the relevance of an existing skill and deploys it, often refining the execution for greater speed and accuracy. For instance, if asked to "analyze my monthly revenue from Stripe and HubSpot and send a weekly summary," the first execution might involve 20 steps and multiple API calls. Subsequent, similar requests leverage the newly created skill, streamlining the entire process and making Hermes progressively more proficient and personalized. This continuous self-improvement is key to its long-term value.

Hermes Agent’s true power emerges in its ability to transform complex, multi-step tasks into single, reusable skills. Consider a common business need: generating a weekly revenue summary from Stripe and HubSpot, complete with three key insights, delivered every Tuesday. A traditional, stateless AI agent would re-learn this entire process from scratch each time, leading to inconsistent results and repetitive computation.

On its inaugural attempt, Hermes tackles this challenge by executing a laborious, multi-stage workflow. This might involve upwards of 20 distinct steps, including numerous API calls to both Stripe and HubSpot, data extraction, analysis, and then the synthesis of insights. The agent performs extensive research, navigating through various tools and data points to fulfill the user's initial, broad request.

Crucially, Hermes doesn't simply complete the task and forget it. Following this initial execution, it meticulously analyzes its own successful workflow. The system then synthesizes this entire operational logic, packaging it as a reusable function—a "skill" document. This skill is rigorously tested, validated, and stored within Hermes's persistent memory architecture, ready for future deployment.

This synthesis yields an immediate and significant payoff. When subsequently asked for a similar revenue report, Hermes Agent doesn't re-engineer the solution. Instead, it instantly recalls and executes the pre-packaged skill. This dramatically reduces processing time, ensures consistent report generation, and significantly boosts reliability, transforming a cumbersome process into an instantaneous command.

Hermes’s capacity to convert experience into automated expertise fundamentally distinguishes it from other AI agents. This continuous learning loop and skill accumulation, backed by its unique memory system, allow it to become progressively more efficient and tailored to user preferences, embodying its core promise of self-improvement over time.

Hermes Agent shatters the conventional limitations of AI deployment, moving far beyond the local terminal. Unlike most agent frameworks tethered to notebooks or specific machines, Hermes is fundamentally platform-agnostic, designed for robust, continuous operation regardless of the underlying hardware. This flexibility ensures your AI doesn't power down when your laptop does.

Developers can deploy Hermes across six distinct environments. These options range from cost-effective solutions to enterprise-grade infrastructure, providing unparalleled control over performance and scalability. You can install it on: - A local terminal for development - A cheap Virtual Private Server (VPS) - Docker containers for isolated environments - SSH for remote management - GPU clusters for intensive tasks - Serverless infrastructure like Modal or Daytona for elastic scaling

This versatility extends to communication channels through its powerful built-in messaging gateway. Hermes unifies conversations from disparate platforms into a single, cohesive stream, ensuring no interaction is lost. Users can seamlessly engage with their AI assistant from their terminal, email, or popular chat applications.

The gateway supports direct connections to: - Telegram - Discord - Slack - WhatsApp - Signal - Email

This unified approach means your personalized AI brain is always accessible, continuously learning and adapting to your preferences across all devices and workflows. Hermes Agent transforms from a mere tool into an ever-present, evolving assistant, ensuring its accumulated expertise is always at your fingertips, ready to tackle tasks from any platform you choose.

Hermes Agent fundamentally redefines the AI agent paradigm, setting itself apart from established frameworks like OpenClaw and popular conversational models such as ChatGPT or Claude. While OpenClaw excels at orchestrating external tools and APIs through pre-defined workflows, Hermes distinguishes itself through an inherent, persistent learning system. OpenClaw facilitates powerful automation by connecting pre-configured tools; Hermes, conversely, absorbs experience, autonomously developing and refining its own capabilities by pushing intelligence into the agent itself.

Traditional agent frameworks often demand extensive manual configuration for plugins and external tools, requiring users to define API calls, set parameters, and manage complex integrations. Hermes, however, observes user interactions and task completions, identifies recurring patterns, and autonomously creates and tests reusable skills. For instance, a multi-step financial reporting process (like pulling Stripe and HubSpot data) transforms from 20 manual steps into a single, self-optimized command, becoming faster and more reliable with each execution.

Directly comparing Hermes to stateless models like ChatGPT or Claude presents an architectural mismatch. These conversational agents reset with every session, possessing no long-term memory or cumulative understanding. They perform impressive feats based on their vast training data but lack persistent self-improvement or a tailored grasp of user context. Hermes Agent, by Nous Research, builds an evolving internal model of user preferences, past tasks, and environmental facts, making it progressively more effective. For additional insight into foundational AI models, refer to OpenAI | OpenAI.

Hermes' sophisticated memory system includes FTS5 Session Search with LLM summarization, agent-curated memory that prevents prompt injection, and dedicated `MEMORY.md` and `USER

Despite its groundbreaking capabilities, Hermes Agent operates within defined current limitations. Nous Research continues to refine the framework, but users should understand these constraints for optimal deployment and expectation management.

Native Windows support remains absent for Hermes Agent, a critical consideration for many developers. Windows users must deploy the framework within the Windows Subsystem for Linux 2 (WSL2), making it a mandatory prerequisite for local installation and development environments. This adds an extra layer of complexity to the initial setup process.

Furthermore, the sophisticated learning loop excels at creating skills for sufficiently complex, multi-step tasks. It does not generate new, reusable skills from simple, single-query interactions. The agent focuses its self-improvement on intricate workflows, like the Stripe/HubSpot revenue report example, where repeated execution benefits from optimized, encapsulated logic.

Crucially, Honcho, Hermes Agent's powerful, built-in learning engine, ships disabled by default. New users expecting immediate, persistent learning capabilities must manually enable Honcho in the configuration. This oversight can lead to initial frustration, as the agent will behave like a stateless system until activated, missing its core differentiator.

These current caveats underscore Hermes Agent's position as an evolving, cutting-edge framework. While immensely powerful, it still requires users to navigate specific technical requirements and configuration nuances to unlock its full, self-improving potential.

Hermes Agent’s evolution accelerates at a breakneck pace, underscoring its commitment to continuous self-improvement. Recent updates, like the v0.7.0 'resilience release', fortified the framework’s robustness, allowing agents to navigate complex tasks with greater stability and fewer interruptions. This rapid iteration cycle ensures the platform remains at the bleeding edge of autonomous AI.

Expanding beyond its initial confines, Hermes Agent now supports a wider array of underlying models, significantly boosting user choice and computational power. Through the Nous Portal and direct integration with Hugging Face, developers gain unparalleled flexibility, selecting the optimal large language model for specific applications and optimizing performance. This open ecosystem fosters innovation, moving past proprietary lock-ins.

The agent’s capacity for autonomous skill development now extends to truly advanced applications. Demonstrating its deep learning capabilities, a Hermes Agent recently undertook the monumental task of autonomously writing a 79,000-word novel. This feat showcases not just the agent’s ability to execute multi-step processes, but also its potential for sustained, creative output over extended periods without direct human intervention.

This impressive trajectory points towards Hermes Agent’s ultimate, ambitious goal: establishing a self-training loop. In this vision, agents wouldn’t merely learn from human interactions; they would actively generate novel, high-quality data through their own experiences and explorations. This generated data would then serve as the training bedrock for the *next generation* of more capable and intelligent AI models, creating an exponential growth cycle.

Nous Research envisions an ecosystem where agents continuously refine their internal architectures, develop new tools, and even contribute to their own foundational model improvements. This radical paradigm shift moves beyond static model releases, promising an era of truly self-evolving AI, where the system itself drives its future development, leading to unprecedented levels of autonomy and intelligence. The implications for AI development, and indeed for human-AI collaboration, are profound and transformative.

Hermes Agent targets a distinct user: developers, automation experts, and researchers who find traditional AI agents frustratingly stateless. Unlike ChatGPT or OpenClaw, which reset with every session, Hermes by Nous Research offers a fundamentally different architecture designed for persistent learning and continuous improvement. It builds an enduring knowledge base, storing every interaction and task, allowing it to remember past solutions and evolve.

Imagine building a truly personal AI assistant that intimately understands your preferences, specific workflows, and operational context. Hermes excels here, automating complex business intelligence tasks like generating weekly revenue summaries from Stripe and HubSpot. This process, which might initially involve 20 manual steps, becomes a single, refined command over time as Hermes creates and tests reusable skills from learned experiences.

Adopting Hermes demands a crucial mindset shift. You do not just use Hermes; you actively develop it. This agent represents an investment, growing in capability as you curate its `MEMORY.md` and `USER.md` files, and as it tracks your patterns and decision history via its Honcho user modeling. Its value offers a compounding benefit week after week, becoming increasingly tailored and efficient for your specific needs.

Consider if its initial setup and learning curve justify the profound, long-term payoff of a bespoke AI teammate. While installation takes about 60 seconds, leveraging its full potential requires engagement and a commitment to its growth. For those seeking an AI that truly evolves with them, rather than a transient tool like many other LLMs, including those accessed via Claude.ai, Hermes offers a compelling, self-evolving alternative. This is about building expertise, not just executing commands.

The emergence of persistent AI agents like Hermes Agent signals a profound shift in the artificial intelligence landscape. For years, leading models such as ChatGPT and OpenClaw operated with a fundamental flaw: they forgot everything between sessions. This "AI amnesia problem" limited their utility, forcing users to re-establish context for every interaction.

Nous Research's Hermes Agent shatters this paradigm by embedding a core architectural choice: the agent remembers. This isn't merely an upgrade; it represents a major inflection point, transforming AI from static, task-specific tools into dynamic, evolving partners. The shift moves beyond simple orchestration to genuine self-improvement, allowing an agent to compound its knowledge and skills over time.

Consider the future implications. An agent that accumulates years of personal and institutional knowledge becomes an invaluable asset. Hermes Agent's sophisticated memory system—including FTS5 Session Search, Agent-curated memory, and Honcho for User Modeling—enables it to learn user preferences, understand complex workflows, and recall historical context. This capability fosters an AI teammate deeply integrated into operations, continuously refining its expertise based on real-world interactions.

This architectural commitment to memory and continuous learning could define the next decade of artificial intelligence. Hermes Agent demonstrates the immense power of an AI that truly grows with its user, moving beyond transient interactions to build an enduring, intelligent partnership. We are witnessing the dawn of a new era where AI agents don't just execute tasks, but evolve into indispensable, self-improving entities.

What is Hermes Agent?

Hermes Agent is an open-source, self-improving AI framework from Nous Research. Its core feature is persistent memory, allowing it to learn from past interactions and autonomously create reusable skills, making it more personalized and efficient over time.

How is Hermes Agent different from ChatGPT or Claude?

The primary difference is memory. While models like ChatGPT and Claude are stateless (they forget past conversations), Hermes is stateful. It's designed to build a long-term memory of your interactions, preferences, and workflows, enabling it to improve continuously.

Is Hermes Agent free to use?

Yes, the Hermes Agent framework is open-source under the MIT license, making it free for personal and commercial use. However, you are still responsible for the costs of the underlying language models (via API calls) and the infrastructure you run it on (like a VPS or serverless compute).

What are 'skills' in Hermes Agent?

Skills are reusable functions that Hermes autonomously creates after successfully completing a complex, multi-step task. It packages the entire workflow—API calls, code execution, decisions—into a tested 'skill' that it can run instantly the next time you ask for a similar task, making it much faster and more reliable.