Short answer
Hermes Agent and OpenClaw are both MIT-licensed, self-hosted autonomous agents. OpenClaw is built on Node.js, chains tools reactively and has the larger community and native desktop and mobile apps. Hermes is Python, learns skills from completed tasks and keeps structured memory. Pick OpenClaw for novel or one-off work, Hermes for structured, recurring tasks.
Where did Hermes and OpenClaw come from?
OpenClaw launched in November 2025 under the original name Clawdbot, built by Austrian developer Peter Steinberger. His description at launch: "an AI that actually does things." It hit 9,000 GitHub stars in its first 24 hours. By February 2026 it was past 214,000 stars, a growth rate faster than Docker, Kubernetes, or React ever achieved. It rebranded to OpenClaw and now has thousands of community-contributed skills through the AgentSkills marketplace.
Hermes Agent was released by Nous Research in February 2026, MIT-licensed. Nous Research is primarily known for the Hermes model series: fine-tuned LLMs built for tool calling and instruction following. Hermes Agent is their framework for deploying those models as persistent autonomous agents. The release framing was explicit: an agent designed to improve the longer it runs, and neither a coding copilot nor a chatbot wrapper.
Both are MIT-licensed and self-hosted. After that, they diverge significantly.
Is it Node.js or Python?
OpenClaw is written in Node.js. This is part of why it spread fast: JavaScript developers could read the source, contribute tool skills, and adapt it to an existing stack without switching languages. The runtime is lightweight, startup is fast, and the project recently added optional Podman support alongside Docker for a rootless container option.
Hermes Agent is Python. The install script handles everything (installs uv, Python 3.11, clones the repo, configures the environment) without sudo. For the AI/ML community, Python is the natural home, and it makes integration with numpy, pandas, and ML tooling more straightforward. Python environments have more surface area for dependency conflicts, though the uv-based setup mitigates most of this in practice.
The language choice reflects the target user. OpenClaw reads like a project built by and for web developers who want an agent slotting into a JavaScript ecosystem. Hermes reads like a project built by AI researchers for technical operators who care specifically about model quality, memory architecture, and task reliability over time.
What is the real difference between them?
OpenClaw's design centers on tool chaining: configure which tools the agent has access to and it chains them reactively. There is a broad community library (700+ in the AgentSkills marketplace as of early 2026) and the framework handles a wide variety of tasks without significant upfront configuration. Setup overhead is low, and that's a real reason it spread quickly.
Hermes's design centers on skill learning. When the agent completes a task, it can synthesize that experience into a Skill Document, a structured, searchable record of exactly how to approach that class of problem, including which tools it used, where it got stuck and what worked. On future similar tasks, the agent pulls these up and builds on them. No starting from scratch. One user reported that within two hours of running Hermes, the agent had created three Skill Documents from assigned tasks and completed a similar research task faster using those skills. No prompt tuning: the improvement came from doing.
OpenClaw approaches every task as a new problem using the same tools. Hermes builds up structured experience and applies it. For repetitive structured work (the same categories of tasks weekly, the same debugging patterns, the same research workflows), the compounding is a real advantage over time. For irregular, novel tasks, the difference is smaller.
How does each one remember things?
OpenClaw remembers in the sense that it keeps conversation history. Out of the box there's no fancy long-term memory layer. Context lives in stored logs, and nothing extracts and files facts for later.
Hermes uses three layers. The user model is a structured record of who you are across sessions: technical background, communication preferences, standing project context, operational patterns. Skill Documents are procedural memory in the agentskills.io open format: searchable markdown files encoding how to handle specific task types. Event memory is a timestamped log of tasks, decisions, and outcomes that allows the agent to reference its own history.
The SourceForge comparison summary describes both frameworks as supporting persistent memory. That flattens a real difference. OpenClaw's memory is closer to a conversation archive. Hermes's is a structured knowledge system that gets more useful the longer the agent runs.
What happened with ClawHavoc?
ClawHavoc was a coordinated supply chain attack on Clawhub, OpenClaw's skill marketplace. Security researchers at Immersive Labs and MITRE documented hundreds of malicious skills, built as info-stealers, published there before the marketplace had systematic security review in place.
It wasn't the only problem that winter. Between 27 January and 8 February 2026, Bitsight researchers counted more than 30,000 OpenClaw instances exposed to the public internet, and found that trivially weak tokens were accepted on exposed gateways. CVE-2026-25253 documented a one-click remote code execution flaw, patched in OpenClaw 2026.1.29.
Why does this matter for Hermes? Both frameworks use the agentskills.io standard for custom tool capabilities, so a Clawhub skill can be loaded into a Hermes instance. Take Clawhub's track record seriously before you do that.
Hermes Agent's core tools are maintained by the Nous Research team, and CVE-2026-25253 is an OpenClaw flaw that doesn't apply to Hermes. Pulling community skills from Clawhub into Hermes still needs the same vetting: read the source, review what API access the skill requests, and run it in a sandboxed environment before granting it production-level permissions.
Which chat apps do they connect to?
Both integrate with Telegram, Discord, Slack, and WhatsApp through gateway systems. OpenClaw also has iOS and Android clients, a macOS native app, and community-built web UIs (PinchChat, webclaw). The multi-client ecosystem is wider on the OpenClaw side: that large community delivers real advantages here.
Hermes Agent supports 15 platforms (Telegram, Discord, Slack, WhatsApp, Signal, email, and more) through a unified gateway that installs as a systemd service. The integration is solid, but the client ecosystem is smaller. For users whose work lives in messaging apps (particularly WhatsApp-heavy workflows), OpenClaw's native mobile support is a concrete advantage.
Which models and schedules do they support?
Both support Claude, GPT-4o, and most major models via OpenAI-compatible endpoints. Hermes connects to 400+ models through OpenRouter or directly to Anthropic, OpenAI, and Nous Portal. The Hermes model family from Nous Research is specifically fine-tuned for the agent's tool-calling format, so it tends to perform better on structured agent tasks than generic models of equivalent size. Swappable at any time.
On scheduling: OpenClaw has a community-maintained scheduler with retention, retries, and job run history. Hermes has natural-language cron scheduling built into the core, and the integration between scheduling and memory is tighter: a monitoring task that has run 50 times has 50 runs of context in the event log informing how it handles the current run.
So which one should you pick?
OpenClaw wins if you prefer Node.js, want the native desktop and mobile apps, or benefit from an enormous community for troubleshooting. For users whose work is primarily novel or one-off tasks, the reactive tool-chaining model is entirely adequate.
Hermes wins if your work involves structured, recurring tasks where an agent that improves through experience creates real value. The skill-learning system, three-tier memory architecture, and the Hermes model family's task reliability are meaningfully better for this use case. Because Hermes can pull skills from Clawhub, you get the same tool ecosystem breadth without losing Hermes's architectural advantages.
Practically: for an easy-to-install application with broad community UIs, OpenClaw is simpler. For a server-side agent running repetitive workflows and improving reliability over time, Hermes is the stronger choice. Weighing OpenClaw against a dashboard-style agent instead? We compared it with Agent Zero in Agent Zero vs OpenClaw hosting.
Common questions
Can I run both OpenClaw and Hermes Agent on the same server?
Technically yes, they're separate processes. In practice, you want at least 8 GB RAM to run both with browser automation active. Most people just run one.
Can I import my OpenClaw skill configurations into Hermes?
Yes. Both frameworks support the agentskills.io format. You can use your existing OpenClaw skills in Hermes without modification, and Hermes can pull new skills from Clawhub using the CLI.
Is OpenClaw's skill marketplace safe to use?
It requires care. Skills are third-party code running with agent-level access. The ClawHavoc supply chain attack in early 2026 surfaced real risks. Vet skills by reading their source before installing. Run OpenClaw in a sandboxed environment (Podman rootless or Docker with limited host access) to contain the attack surface.
Which framework has better community support?
OpenClaw's community is substantially larger: 214,000+ GitHub stars versus Hermes's earlier-stage numbers. For common configuration issues and tool skills, OpenClaw has more resources. Hermes has a more focused community active on memory architecture, skill design, and task reliability specifically.
I run OpenClaw today. Is it worth switching to Hermes?
Depends on your work pattern. If you run the same categories of tasks repeatedly and want an agent that improves at them over time, Hermes's skill-learning architecture is worth the migration effort. Your existing Clawhub skills import cleanly. If you rely on OpenClaw's native mobile apps, switching costs are higher.