Nous Research launched Hermes Agent on February 25, 2026. Within 7 weeks it hit roughly 100,000 GitHub stars. OpenClaw has 347,000, so Hermes is far from the most popular open-source agent. But it grew faster in its first 7 weeks than any other.
People switched for a specific reason: Hermes is the first open-source agent that writes its own code after each task, then uses that code to run the same task faster next time. The more you use it, the better it gets at your particular workflows.
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What is Hermes Agent, in plain language?
An AI agent is a program that uses an AI model to complete tasks on your behalf: browsing the web, writing files, running code, sending messages. Most agents work the same way every time. You give them a task, they complete it, and the next session they start from scratch with zero memory of how they did it before.
Hermes works differently. After each complex task, it runs a self-evaluation: it looks at what it just did, finds a more efficient path, and saves that as a reusable skill file on your computer. The next time you give it a similar job, it pulls that skill first.
Independent benchmarks show 40% faster task completion after the agent accumulates 20 or more skills. One developer on Medium:
“The first time they asked Hermes to research AI news and format a briefing, it took about four minutes; the third time, it pulled from the skill it had created and refined — two minutes with better formatting and no intervention needed.”
Beyond skills, Hermes builds a persistent model of you across sessions. Your preferences, past decisions, context it would otherwise have to re-ask about. Four memory layers, managed automatically. You don’t set any of this up. It accumulates on its own.
It runs on 200+ AI models via OpenRouter (including cheap options like DeepSeek), supports local deployment via Ollama with no API costs, and works across six different environments: your laptop, Docker, SSH servers, Modal, Daytona, and Singularity. MIT licensed, no telemetry, all data stays on your machine.
How is Hermes different from OpenClaw?
OpenClaw launched in November 2025 and reached 347,000 GitHub stars in roughly 4 months. It’s built around one idea: the best possible messaging hub for AI tasks. It connects to 50+ platforms (Telegram, Slack, Discord, WhatsApp, iMessage, and dozens more) and routes your requests through whichever AI model you configure.
Hermes is built to get better at your specific workflows over time. OpenClaw is built to reach you on any channel and orchestrate across many tools at once.
On memory, both agents have it, but they work differently. OpenClaw stores memory in plain Markdown files (MEMORY.md, daily notes, DREAMS.md) you can read and edit yourself. It also has 5+ memory plugins including Mem0, Supermemory, and MemOS. Hermes uses an automated 4-layer system the agent manages itself, with less visibility into what it’s storing. OpenClaw gives you full control. Hermes just handles it.
On security, the numbers tell the story. Hermes has zero public CVEs (security vulnerabilities). OpenClaw had 9 in March 2026 alone, including one rated 8.8 out of 10 in severity that could let an attacker steal your API keys. OpenClaw patched all of them, but the track record gap is real.
On the self-improving loop, Hermes stands alone. OpenClaw skills are written by humans and distributed through a community hub. Hermes writes its own. If you want your agent to get measurably better at repeated tasks, Hermes is the only open-source option doing this right now.
| Hermes | OpenClaw | |
|---|---|---|
| GitHub stars | ~100K (7 weeks) | 347K (4 months) |
| Design | Agent-first (self-improving) | Gateway-first (multi-channel) |
| Skills | Agent writes from experience | Human-written |
| Memory | 4-layer automated system | Markdown files + 5+ plugins |
| Security | 0 CVEs | 9 CVEs (March 2026) |
| Learning | Gets 40% faster after 20+ skills | Static |
| Best for | Repeating workflows that compound | Multi-channel orchestration |
What real users are saying
The migration stories from Reddit and Medium tell the same story. One developer who spent 3 months on OpenClaw before switching:
“I ran OpenClaw for three months, wrote the setup guide and built cron jobs, but within a week of setting up Hermes Agent they’d migrated everything, and within two weeks they’d stopped thinking about OpenClaw entirely.”
The debate has taken over r/openclaw (103,000 members), with Hermes-vs-OpenClaw threads running at the top of the subreddit.
What users say genuinely works:
- The self-improvement loop is real. Measured results on real workflows.
- Memory persists across sessions without any configuration.
- Ollama support works. Fully local means zero API bills.
- 7 major versions shipped between late March and mid-April 2026.
Where Hermes falls short:
73% of each API call is fixed overhead: tool definitions eat 8,759 tokens, the system prompt another 5,176, leaving only 27% for actual conversation. For one-off tasks, you’re paying a lot for not much output.
The setup isn’t beginner-friendly. As one reviewer put it:
“It’s built for people who are comfortable with SSH, config files, and ~/.hermes/.env. If you’re looking for something that feels more like a teammate and less like a sysadmin task, there are better options.”
Self-learning is disabled by default. Many new users expected the improvement loop to start automatically. You have to enable it in config.
Two security issues shipped in March 2026, both fixed in v0.50.34. And Hermes got caught in the LiteLLM supply chain attack of March 24, 2026, a backdoored dependency that harvested API keys before detection. The team patched quickly, but the exposure window was real.
No native Windows support. WSL2 works, but it’s an extra step.
How to set up Hermes Agent (and what it costs)
Installation is one command across macOS, Linux, and WSL2:
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bashOr via package manager:
# macOS
brew install hermes-agent
# Linux / WSL2
pip install hermes-agentAfter install, run hermes setup, an interactive wizard that takes about 5 minutes. It walks you through model selection, tool configuration, messaging gateways, and terminal backend. If you’re coming from OpenClaw, it detects your ~/.openclaw directory and migrates your settings automatically.
For a fully local deployment with Ollama, point the API URL to http://127.0.0.1:11434/v1 and set the API key to ollama. Local models need a minimum 64K context window. Community recommendation is Qwen 3.5 27B.
Real cost tiers from community setups:
- Budget: $6-8/month. Hetzner VPS + DeepSeek V4.
- Mid-tier: $15-25/month. Hostinger + Claude Haiku.
- Premium: $40-80/month. DigitalOcean + Claude Sonnet 4.6.
Honest verdict: is Hermes better than OpenClaw?
Hermes wins if:
- You run the same types of tasks daily and want the agent to improve with each session. No other open-source agent does this right now.
- Security matters. Zero CVEs vs nine is a real gap.
- You want fully local deployment with Ollama and no API costs.
- You prefer memory that manages itself rather than files you maintain.
OpenClaw wins if:
- You need to reach the agent through Slack, Telegram, Discord, WhatsApp, and other channels at the same time. Hermes doesn’t do this.
- You want a dedicated coding agent. OpenClaw’s Pi handles PR review, refactoring, and code generation in ways Hermes doesn’t specialize in.
- You want a more established community. 347K stars vs ~100K means more community skills and more people to ask when things break.
- You prefer memory files you can read and edit yourself.
Roughly equal:
- Both have memory, different approaches and tradeoffs.
- Both support local models, both MIT licensed.
- Both have had security incidents in 2026.
The migration is happening because solo developers running daily automation workflows found that Hermes compounds over time in ways OpenClaw can’t match. If that’s you, try it. For multi-channel orchestration or a bigger ecosystem, OpenClaw is the stronger pick.
