Hermes VS OpenClaw: Why the Answer Isn't What You Think

Hermes VS OpenClaw — the question everyone keeps asking me, and the answer genuinely isn't what you think.

Most YouTube reviews tell you to pick one.

"Use Hermes, it's smoother."

"Use OpenClaw, it's more powerful."

Both takes miss the point.

After 100+ hours testing both in production, here's what I actually do:

I use both. Plus a backup. Every single day.

Let me explain why that's the answer most people should land on, and what makes it the smartest play in 2026.

Video notes + links to the tools 👉

The Real Problem: Open-Source AI Agents Break

Here's the truth nobody in the AI agent space wants to say loudly:

Both Hermes and OpenClaw break.

Regularly.

Not because the projects are bad — the opposite.

They ship updates constantly.

Hermes just launched version 0.9 with a full new dashboard.

OpenClaw is pushing features weekly.

When open-source projects ship rapidly, things sometimes break.

That's normal.

That's expected.

And it's exactly why the "pick one" answer is wrong.

If you rely on only Hermes and Hermes breaks tomorrow, your workflows are dead until it's fixed.

Same with OpenClaw.

Redundancy isn't paranoia — it's common sense when running AI agents for real business.

My Three-Agent Stack

Here's what I actually run:

Hermes — primary for most tasks, especially since 0.9 launched
OpenClaw — secondary for the features Hermes doesn't have yet
Manas — managed backup when both open-source options fail

When one breaks, I've got others running.

When I need a specific feature, I pick the tool that has it.

When I want redundancy for a critical workflow, I run two agents in parallel.

This three-agent setup has kept my business running smoothly even through major update cycles on both Hermes and OpenClaw.

My Ollama + Hermes guide covers getting Hermes up and running quickly — start there if you haven't yet.

The Nous Research Factor

If I had to pick just one, it would be Hermes.

And the reason comes down to who built it.

Hermes: Built by a Real AI Lab

Hermes comes from Nous Research.

Nous is a proper AI lab.

They build their own models.

They understand what's happening inside the models they use.

They designed Hermes as an agent harness built with proper engineering foundations.

OpenClaw: Built by a Weekend Hacker

OpenClaw started as a weekend project by Peter Steinberger.

Genuinely great work.

It grew into an amazing community.

But it wasn't engineered from the ground up to be a worldwide leading AI agent.

It was a clever hack that got popular.

Why This Matters

The foundational difference shows up in everyday use.

Fewer unexpected bugs with Hermes
Cleaner architecture in Hermes's codebase
Better integration with the underlying models
More cohesive product decisions from the Hermes team

OpenClaw is catching up fast.

But Hermes starts from better fundamentals.

That matters over the long run.

🔥 Want to master both Hermes AND OpenClaw properly?

Inside the AI Profit Boardroom, I've built a full 2-hour Hermes course alongside a 6-hour OpenClaw deep dive. Plus we update trainings daily as new releases drop — the Hermes 0.9 dashboard tutorial went up today, as did the Elephant Alpha + OpenClaw free setup. 2,800+ members running serious AI agent workflows with both tools.

→ Get both the Hermes and OpenClaw courses here

Dashboard Face-Off: Hermes 0.9 vs OpenClaw

I want to walk through this carefully because the UX difference is genuinely significant.

What Hermes 0.9 Gets Right

The new dashboard includes:

Status overview — everything at a glance
Sessions panel — manage conversations cleanly
Analytics — usage metrics
Logs — debugging data
Scheduled tasks — crystal clear cron management
Skill management — easy to edit and organise

Everything is where you'd expect it.

Where OpenClaw Still Wins

One area OpenClaw beats Hermes:

In-dashboard chat.

OpenClaw lets you chat with the agent directly from the dashboard.

Hermes requires terminal, Telegram, or another messaging platform.

For me, this isn't a dealbreaker — I use Telegram anyway — but some users will prefer OpenClaw's all-in-one approach until Hermes catches up.

Where OpenClaw Is Losing

The OpenClaw dashboard feels messy compared to Hermes 0.9.

Cron section — hard to parse, confusing layout
Skill management — functional but not intuitive
Overall navigation — you feel like you're browsing a debug tool

It's the kind of thing that tells you one was designed and one was assembled.

The Skills Compound Argument

Here's the part most comparison articles miss completely.

It doesn't matter which you learn first.

The skills transfer.

What You're Actually Learning

How to set up AI agents (both work similarly conceptually)
What use cases actually automate well (this is universal)
How to prompt for agentic behaviour (model-agnostic)
How to debug and recover from failures (same principles)
How to build reusable skills (both have skill systems)
How to chain multiple agents together (same concepts)

All of this compounds regardless of which tool you start with.

The Next AI Agent Is Coming

OpenClaw launched November 2025.

Hermes launched February 2026.

Both are less than 6 months old.

Whatever you build expertise in now will serve you when the next major AI agent launches.

And something new IS coming.

It always is.

So stop worrying about "which to learn first" and just start learning.

If you're new to AI agents entirely, my Ollama + Hermes setup guide is the gentlest entry point.

Practical Use Cases I Run on Each

Let me show you how I actually divide work between them.

Tasks Where I Use Hermes

Morning research routines (Hermes 0.9 scheduled tasks are cleaner)
Content drafting workflows (smoother session handling)
Quick one-off agent queries (faster, less friction)
Telegram-based chatting (seamless integration)

Tasks Where I Use OpenClaw

Long-running background jobs (better uptime for my setup)
Workflows using specific community plugins (larger library)
Dashboard-based chatting (when I want everything in one UI)
Complex multi-tool workflows (more mature integrations)

Tasks Where I Use Both

Multi-agent group chats where they work together
Redundant critical workflows where I run parallel instances
Cross-validation of outputs (one agent drafts, another reviews)

This division isn't rigid.

I'll often pick whichever is working best that day.

Learn how I make these videos 👉

When Both Break — The Manas Backup

This is the tactic that saves me during bad update days.

When Hermes AND OpenClaw both have issues at the same time (rare but happens), I have Manas as my managed backup.

Manas is a paid managed service.

Less flexible than open-source.

But much more reliable.

When my critical scheduled tasks need to run, Manas keeps them running while I diagnose issues with the open-source options.

Alternatives to Manas

If you don't want to use Manas specifically:

Minimax-hosted agents — managed alternative
Claude Code — for fixing broken setups
Any managed AI agent service — pick what fits your stack

The point is: have a backup.

Running a business on open-source AI agents without a safety net is asking for trouble.

The Multi-Agent Group Chat Strategy

Here's an advanced technique I want to highlight.

Both Hermes and OpenClaw can be added to Telegram group chats.

You can tag each agent separately.

You can even have them talk to each other.

Example Setup

Telegram group: "AI Research Team"
Members: You, Hermes agent, OpenClaw agent
Task: "Hermes, pull the latest research on X. OpenClaw, review and find gaps."

Two different agents, two different models, working on the same task from different angles.

You can also run:

Two Hermes agents in parallel for double throughput
Multiple OpenClaw agents for parallel subtask execution
A mix of agents for specialised roles

This is where AI agents genuinely start feeling like a distributed team.

My Claude Code AI SEO setup uses a similar parallel concept for content generation, which pairs well with Hermes for other automations.

🔥 Want the exact multi-agent Telegram workflows I'm running?

Inside the AI Profit Boardroom, I share my production multi-agent configurations — which agents combine best, how to prompt them for collaboration, what tasks benefit most. Plus daily training updates as new features drop (today's drop: Elephant Alpha + OpenClaw + Claude Code all working together for free). 2,800+ members building advanced multi-agent systems.

→ Get the multi-agent workflows here

Hermes VS OpenClaw: Frequently Asked Questions

What's the single biggest difference between Hermes and OpenClaw?

Foundation. Hermes comes from Nous Research, a professional AI lab that builds models. OpenClaw started as a weekend hack by Peter Steinberger that grew. This shows up as smoother, less buggy experience with Hermes, though OpenClaw has a larger community library.

Should I learn Hermes or OpenClaw first?

Hermes. Cleaner dashboard (especially 0.9), smoother first experience, fewer frustrations for beginners. Once you're comfortable, add OpenClaw to your toolkit.

Do Hermes and OpenClaw work with the same AI models?

Mostly yes. Both support major providers (Anthropic, OpenAI, Minimax, local Ollama models, etc.). You can often use the same API key across both. Just check each agent's current model support for specifics.

What's Manas and why do I need it as a backup?

Manas is a managed AI agent service. You need a backup because open-source agents like Hermes and OpenClaw break during update cycles. When both are having issues, Manas keeps critical workflows running.

Can I use Hermes and OpenClaw simultaneously?

Absolutely, and I recommend it. Run them in the same Telegram group, tag each separately, even have them work together on tasks. Different strengths, complementary workflows.

Are the skills I build in Hermes transferable to OpenClaw?

Conceptually, yes. The specific skill files aren't directly portable, but everything you learn about prompting, agent design, and multi-tool workflows transfers between the two systems and will apply to future AI agents that launch.