TL;DR: OpenClaw wins on ecosystem size (346K stars, 44K skills, 50+ platforms) and enterprise support (NVIDIA NemoClaw). Hermes Agent wins on self-improvement (agents that learn from tasks), security (zero agent CVEs), and cost efficiency ($5/mo serverless hosting). Both are MIT licensed, model-agnostic, and support MCP. If you need broad channel reach, go OpenClaw. If you want an agent that gets smarter over time, go Hermes. You can run both on the same server.
Two open-source AI agent platforms. Both MIT licensed. Both model-agnostic. Both connect to messaging apps and run on your server. And both showed up in the same 90-day window between late 2025 and early 2026.
OpenClaw exploded. 346,000 GitHub stars. 38 million monthly visitors. An OpenAI acquisition. NVIDIA building an enterprise stack on top of it. Hermes Agent grew quietly. 23,400 stars. No viral moment. But it shipped something OpenClaw still doesn't have: agents that teach themselves.
I've spent the last few weeks digging through both. The GitHub repos, the docs, the community posts, the security advisories, the actual install experience. This isn't a feature table you could generate from two README files. It's a breakdown of where each one actually matters, and when you'd pick one over the other.
The 30-second version
OpenClaw is the platform that connects to everything. Fifty-plus messaging channels, 44,000 skills on ClawHub, a TypeScript codebase with 1,200 contributors. It's the biggest open-source agent project on the planet right now.
Hermes Agent is the platform that remembers everything. Built by Nous Research (the $50M-funded AI lab behind the Hermes LLM series), it creates skills from your conversations, improves them as it works, and builds a user model that deepens across sessions. Python-based, 245 contributors, 3,150 commits. Smaller. But doing things differently.
At a glance: April 2026
Where each one wins
Editorial scores, 0–10. Based on docs, GitHub data, and hands-on testing.
Best use cases
- You need 50+ messaging channels
- Enterprise security (NVIDIA NemoClaw)
- 44K+ ready-made ClawHub skills
- Multi-model routing across providers
- Largest community for troubleshooting
- Agent should learn from experience
- Terminal-first workflow (full TUI)
- Clean security profile is a must
- RL training or trajectory generation
- Serverless hibernation for low costs
Monthly cost breakdown
Tech stack at a glance
Where OpenClaw wins (and it's not close)
Channel reach. OpenClaw supports over 50 messaging platforms. WhatsApp, Telegram, Discord, Slack, Signal, iMessage, Matrix, IRC, WeChat, Teams, Line, email. Hermes covers six: Telegram, Discord, Slack, WhatsApp, Signal, and email. If your agent needs to live on iMessage or WeChat or Matrix, that conversation ends fast.
Ecosystem volume. ClawHub lists 44,000+ skills as of this month. That's up from 5,700 in early February. The Hermes ecosystem is growing through agentskills.io (an open standard that works across multiple agent platforms), but the catalog is nowhere near that size yet. An awesome-hermes-agent list on GitHub tracks the ecosystem at 725 stars. It's active, but early.
Enterprise backing. NVIDIA announced NemoClaw at GTC 2026. That's a full enterprise security stack wrapping OpenClaw with audit logs, access controls, and guardrails. Box, Cisco, and others are running it. Hermes has nothing in that tier. Nous Research is a 20-person research lab, not an enterprise vendor.
Community scale. 1,200+ contributors. 58,000+ forks. When something breaks, someone on GitHub or Discord has already hit the same wall. With Hermes at 245 contributors, you're more likely to be the first person to encounter an edge case. The OpenClaw subreddit, Discord, and Stack Overflow presence dwarfs anything Hermes has built so far.
Multi-model routing. Both platforms are model-agnostic. But OpenClaw's architecture lets you assign different models to different agents in the same instance. A coding agent on GPT-5.4. A scheduling agent on Haiku. A research agent on DeepSeek for bulk work. Hermes supports model switching too (via hermes model), but the per-agent routing isn't as mature.
Where Hermes Agent wins (and it's not subtle)
The learning loop. This is Hermes's one thing that nobody else does well. After you finish a complex task, Hermes creates a skill document from what it did. Next time you give it a similar task, it references that skill and finishes faster. The Substack comparison from MLearning.ai reported a 40% speed improvement on repeated tasks with no prompt tuning. The agent literally taught itself.
That's not marketing copy. The mechanism is concrete: autonomous skill creation after tasks, skill self-improvement during use, agent-curated memory with periodic nudges, FTS5 session search with LLM summarization for cross-session recall, and Honcho dialectic user modeling.
It remembers who you are, what you prefer, and how you work. OpenClaw has MEMORY.md. Hermes has a whole cognitive architecture.
Security record. I'm going to be direct about this. OpenClaw had 9 CVEs disclosed in 4 days in March 2026. CVE-2026-25253 scored 8.8 on CVSS. One scored 9.9. Over 135,000 exposed instances were found across 82 countries. More than 800 malicious skills were flagged on ClawHub.
Hermes Agent? Zero major agent-specific CVEs as of April 2026. The only CVE with "HERMES" in the name (CVE-2026-22798) affects a completely unrelated software publication tool called HERMES by softwarepub, not Nous Research's agent. Hermes ships with command approval, DM pairing for messaging platforms, and container isolation through its terminal backends. Smaller attack surface, cleaner record.
Terminal-first design. Hermes has a full TUI (terminal user interface) with multiline editing, slash-command autocomplete, conversation history, and streaming tool output. It's built for developers who live in the terminal. OpenClaw works through messaging interfaces and web UIs. Different philosophy.
Terminal backends. OpenClaw runs locally or in Docker. Hermes runs on six backends: local, Docker, SSH, Daytona, Singularity, and Modal. The Daytona and Modal options are serverless. Your agent's environment hibernates when idle and wakes on demand. On a $5/month VPS, that hibernation means your actual compute costs between sessions approach zero.
Subagent delegation. Hermes spawns isolated subagents for parallel work. Write Python scripts that call tools via RPC, collapsing multi-step pipelines into zero-context-cost turns. OpenClaw has basic multi-agent support but nothing as structured for parallel execution.
Research features. Batch trajectory generation, Atropos RL environments, trajectory compression for training the next generation of tool-calling models. If you're fine-tuning models or doing reinforcement learning research, Hermes was designed for that workflow. OpenClaw wasn't.
Built-in migration from OpenClaw. hermes claw migrate imports your SOUL.md, memories, skills, API keys, messaging settings, and command allowlist. It detects ~/.openclaw automatically during first setup. If you're already running OpenClaw and want to try Hermes, the path is paved.
The security gap deserves its own section
I keep coming back to this because the numbers are hard to ignore.
OpenClaw's security problems aren't theoretical. Shodan scans in March 2026 found 135,000+ instances exposed to the internet without authentication. Exploits for the March CVEs appeared on GitHub within days. The malicious skills problem on ClawHub is ongoing.
Hermes benefits from being smaller and younger. Fewer installs means fewer targets. But the architectural choices matter too: command approval by default, container isolation through multiple backends, and a Python codebase that doesn't expose a web server by default the way OpenClaw's Node.js setup does.
If security is a hard requirement for your use case, Hermes has the edge right now. If you're running OpenClaw, use NVIDIA's NemoClaw stack or at minimum keep it behind authentication and stay current on patches.
Running OpenClaw and worried about CVEs? OpenClaw VPS patches security updates automatically and keeps your instance behind authentication. Plans start at $12/month.
Real costs, side by side
Both are free to self-host. The costs come from your VPS and your LLM API usage.
OpenClaw typically runs $20-32 per month total. VPS at $3-12/month, API costs at $5-20/month depending on how much you use it and which models you pick. Power users hit $60/month.
Hermes runs cheaper for most setups. A $5/month VPS handles it. Serverless backends (Modal, Daytona) hibernate when idle, so you're not paying for compute your agent isn't using. API costs are the same since both support the same providers (OpenRouter, OpenAI, Anthropic, Ollama). Typical Hermes monthly cost sits around $10-25.
The difference isn't dramatic, but Hermes's serverless options give it an edge for people who don't need their agent running 24/7. If your agent runs 8 hours a day and sleeps the rest, you're paying for 8 hours of compute instead of 24.
Want OpenClaw without managing infrastructure? OpenClaw VPS handles hosting, security, and updates. You focus on using it. Pre-configured instances from $12/month.
Can you run both?
Yes. And for some workflows, you should.
The MLearning.ai Substack piece describes running both on a Hetzner server and having them talk to each other over HTTP. That's not a weird edge case. It makes sense for setups where you want OpenClaw handling the broad messaging surface (WhatsApp, iMessage, IRC) while Hermes runs the tasks that benefit from its learning loop.
Hermes can even act as a skill inside OpenClaw's ecosystem, or vice versa. The evey-bridge-plugin on GitHub connects Claude Code and Hermes for task handoff. Similar bridges exist for OpenClaw.
The migration tool also supports a gradual approach. Run both, shift workflows one at a time, see which agent handles which tasks better.
The Nous Research factor
Hermes doesn't exist in a vacuum. Nous Research is the lab behind it, and their background matters for evaluating the project's future.
Nous raised $50M from Paradigm (one of crypto's biggest VC firms) at a $1 billion token valuation in April 2025. They'd previously raised ~$20M from Distributed Global, North Island Ventures, and Delphi Ventures. The company was founded in 2023 by a group of AI researchers who met on Discord and GitHub.
Their Hermes LLM series (fine-tuned models on Meta's Llama and Mistral) gained serious traction in the open-source community. Their research papers on extending model memory have been cited by Meta and DeepSeek. They collaborated with Diederik Kingma, a member of the OpenAI founding team.
The crypto angle: Nous is building a decentralized model training system on Solana where people contribute idle GPU compute. That's still in development. The agent is the shipped product.
What this means practically: Hermes has funding, has a research lab behind it, and has a model ecosystem. It's not a hobbyist project. But it's also not backed by OpenAI or NVIDIA the way OpenClaw is. If institutional backing matters to you, OpenClaw has the bigger names.
My take
If I needed an agent running on WhatsApp, Signal, and Discord at the same time, serving 10,000+ ClawHub skills to a team of 20 people, with NVIDIA enterprise security, I'd pick OpenClaw. That's not a contest.
If I needed a personal agent that gets better at my specific workflows over time, runs on a cheap VPS, has a clean security profile, and doesn't need to connect to 50 messaging apps, I'd pick Hermes. Also not a contest.
The real split is about what you value. Breadth or depth, ecosystem or intelligence, reach or memory. Most people will start with OpenClaw because the community is bigger and the platform coverage is wider. Some of them will add Hermes later when they want something that actually learns.
Both projects are MIT licensed and model-agnostic. Neither locks you in. That's the best part of this space right now. You can try both with zero risk and zero switching cost.
