Nous Research’s Hermes Agent is aimed squarely at one of the biggest complaints Openclaw users have raised in public: AI agents that lose context between sessions unless memory is manually configured. Hermes is designed to close that gap. The framework is released under the MIT license, launched in February 2026, and gathered 22,000 GitHub stars and 242 contributors within weeks.
Hermes is built around a closed learning loop
The project centers on what Nous Research describes as a closed learning loop. After completing a task, Hermes writes the process into a reusable Markdown skill file, stores the result in persistent memory, and adjusts how it approaches similar work in the future. There is no YAML requirement and no manual configuration step for this behavior; the system manages it internally through SQLite full-text search and large language model summarization.
That design lets the agent carry memory across sessions, platforms, and devices. It also scans for prompt injection and builds a user profile over time. If the agent handles a research task once, it can save both the outcome and the method, then execute similar work faster when the same type of request appears again.
Openclaw and Hermes are being used for different layers
The source describes Openclaw as a breakout framework from early 2026, known for connecting large language models to apps such as Whatsapp, Telegram, Slack, Signal, Discord, and iMessage. It can carry out real-world actions including email management, browser automation, shell commands, and cron jobs. Its Clawhub marketplace also gives it a broad skill ecosystem.
Still, users have repeatedly pointed to one limitation: memory between sessions often needs manual setup. Hermes was built to address that issue. Community discussion across Reddit, YouTube, and X does not frame Hermes as a direct replacement for Openclaw. The more common view is that the two are complementary. Openclaw is used for multi-channel orchestration, team workflows, and ecosystem breadth, while Hermes handles persistent memory, auto-generated skills, model reasoning, and long-horizon tasks. One common setup uses Hermes as a high-level planner on top of Openclaw’s tools.
Low-friction migration and broad model support
Installation is described as a single command. For existing Openclaw users, the built-in migration path allows hermes claw migrate to pull in skills, memories, and settings from an existing setup, lowering the switching cost. The article says many users have already taken that route.
Hermes supports more than 200 models through Nous Portal, Openrouter, OpenAI-compatible endpoints, and local Ollama. It also includes more than 40 built-in tools covering terminal access, file operations, browser automation, code execution, image generation, and natural-language scheduling. Deployment options include a $5 VPS, local machines, Docker, and SSH.
Security hardening was a major focus in v0.5
Nous Research treated v0.5 as a hardening release. According to the source, it included more than 200 pull requests, patched a LiteLLM credential exposure issue, added fixes for path traversal, and improved container security. Built-in protections include command approval flows, dangerous-pattern blocking, and memory scanning for injection attempts. The same source says there are no public records of widespread breaches.
The project is still moving quickly. Nous Research, led by co-founder and CEO Jeffrey Quesnelle, focuses on open-source model development and reinforcement learning. Quesnelle has demonstrated Hermes autonomously completing a 79,000-word novel across iterative sessions. By late March 2026, Hermes v0.6.0 had shipped with MCP support and typed SDK workflows, showing that development is still advancing at a rapid pace.