Artificial intelligence tools are increasingly allowing non-technical users to build software and automate tasks that previously required programming knowledge, and a new open-source AI agent called Hermes is becoming a major example of that shift.
The discussion gained momentum this week after reports circulated about a 78-year-old marketing executive with no coding background successfully creating a robotics application using only natural-language instructions. The application was reportedly built through the Reachy Mini ecosystem developed by Hugging Face, whose robot app marketplace has surpassed 300 live applications and approximately 10,000 deployed robots worldwide.
According to the shared account, the individual did not use Python programming or specialized robotics software during development. Supporters of AI-assisted development tools pointed to the example as evidence that conversational AI systems are reducing technical barriers that traditionally slowed software creation.
The development also reflects a broader trend across the AI industry. Newer AI agents are increasingly designed to retain information from previous interactions, improve their own workflows, and adapt to user behavior over time. Earlier this week, Anthropic introduced a feature called “Dreaming,” which allows AI agents to process earlier sessions in the background and generate new memory structures automatically. Meanwhile, Hermes Agent from Nous Research is pursuing a similar idea through persistent task learning and automated skill generation.
Hermes Agent, first released in February 2026, has quickly gained traction within the open-source AI community. The project reportedly has more than 135,000 GitHub stars and is distributed under the MIT license. It also includes over 40 built-in skills, which function as reusable instruction modules that help the system repeat previously learned workflows more efficiently.
One of Hermes’ defining features is its self-improving learning architecture. After completing a difficult or multi-step task, the agent enters what developers call a “Reflective Phase.” During this process, the system reviews its own actions, identifies successful execution patterns, and converts those patterns into reusable skill files. When a related task appears later, Hermes can retrieve the previously learned solution instead of generating a new workflow from the beginning.
The platform also uses a layered memory structure consisting of temporary session memory, long-term episodic memory stored through SQLite databases, and procedural memory tied to learned skills. Developers say the software can operate on low-cost virtual private servers, large GPU clusters, or serverless cloud environments. Hermes is also model-agnostic, allowing users to connect the framework to providers such as OpenAI, Anthropic, OpenRouter, Kimi, MiniMax, GLM, Nous Portal, or privately hosted AI endpoints.
Users can access the agent through Telegram, Discord, Slack, WhatsApp, Signal, email services, or command-line interfaces. The project’s latest update, v0.13.0, internally referred to as “The Tenacity Release,” reportedly introduced Google Chat integration as its twentieth supported platform. The update also added durable multi-agent coordination tools, automatic task recovery systems, retry budgeting controls, hallucination filtering mechanisms, persistent goal tracking for long-running tasks, automatic linting after file edits, and session recovery after unexpected gateway interruptions.
According to project details shared by contributors, the release included 864 code commits from 295 contributors in a single week and resolved eight critical security issues. One patched vulnerability reportedly involved a Discord-related flaw that could allow bots to message users across servers outside their intended access scope.
The installation process has also been simplified significantly. Hermes now uses a one-line curl installer that automatically configures dependencies such as Python 3.11, Node.js, ripgrep, and ffmpeg. During setup, the software can automatically detect existing OpenClaw environments and offer to import prior settings, memories, skills, and API credentials.
The growing comparison between Hermes and OpenClaw highlights a design shift occurring within the AI assistant ecosystem. OpenClaw originally gained attention by focusing heavily on messaging integrations and centralized orchestration across communication platforms. Hermes, by contrast, places continuous learning and automated self-improvement at the center of its architecture.
In practical terms, OpenClaw skills are generally predefined instruction sets written manually by users or generated beforehand through prompting. Hermes instead attempts to build those reusable workflows automatically by analyzing completed tasks after roughly every 15 tool interactions or after especially complex operations. Supporters argue this creates a compounding learning effect where the agent gradually improves with repeated use.
Despite the growing interest around Hermes, some developers caution against viewing it as a complete replacement for OpenClaw. OpenClaw still supports more than 24 messaging integrations, offers greater transparency through inspectable file-based memory systems, and has undergone broader public security review. Community discussions suggest that many advanced users currently operate both systems together, using OpenClaw for orchestration while relying on Hermes for adaptive learning capabilities.
Researchers tracking the rapid development of AI agents believe these systems are moving beyond traditional chatbot behavior and evolving into persistent digital assistants capable of handling long-running, multi-step workflows. However, cybersecurity analysts also warn that systems with autonomous memory creation and broad platform access may introduce additional security and privacy risks if governance and safeguards fail to evolve alongside the technology.