Openclaw vs Hermes — Which AI Agent Is Smarter?

When you put two AI agents side by side, the temptation is to ask "which one wins?" — but the answer almost always depends on the test design more than the agents. So I ran a small, honest comparison: Openclaw vs Hermes, on the same brain, same prompts, same scoring rubric, with Claude Opus 4.7 as a scale reference.

This isn't a benchmark paper. It's a Sunday-afternoon look at where each agent stands today.

Why I bothered

Most agent comparisons swap brains and tools at the same time, then argue the result. That makes the comparison meaningless — you don't know if "Agent A scored higher" because the agent itself was smarter, the model was bigger, or the toolchain was tighter.

So I locked the brain. Both agents ran on MiniMax 2.7. Same context window, same temperature, same tool allowlist where each agent's harness allowed it. The only thing I changed was the agent itself — its prompting style, planner architecture, memory model, and tool-routing logic.

I also dropped Claude Opus 4.7 into the same scenarios as a scale reference. Not as a competitor — Claude doesn't run as a long-lived agent on EClaw the same way Openclaw and Hermes do — but as a way to read the absolute numbers. If Claude scores 82/147 on tasks like "execute this multi-step web flow without losing context," then a 68 from Openclaw means something concrete: roughly 83% of Claude's ceiling.

The scoring rubric

I tested across roughly eight capability buckets that map to what users actually ask agents to do day-to-day:

• Multi-step instruction following — does it drop steps, or hold the whole plan?
• Mid-task error recovery — does a transient failure crash the loop or get retried?
• Clean tool calls — right tool, right arguments, sane retry on partial failure
• Web control — driving a browser (Playwright / computer-use) end-to-end
• Long-running context — coherence after 30+ conversation turns
• Conversational fluency — interacting with a human or another agent
• Asking clarifying questions — when the task is ambiguous, instead of guessing wildly
• Self-correction — noticing its own mistake without being told

Each bucket scored on a 0–20 weighted scale, capped at 147 total. (The math is a bit lumpy because some buckets weighed heavier — long-running context and tool use ate more of the budget than conversational fluency, which is more cosmetic for an automation agent.)

The result

Agent	Score	Note
Openclaw	68	Edges Hermes; strongest on tool use + self-correction
Hermes	58	Lost most ground in Web Control — browser ops still rough
Claude (reference)	82	Ceiling for the bucket layout

So Openclaw beats Hermes by 10 points — about a 17% relative gap. Both clear roughly half of Claude's reference score.

Why Hermes lost where it lost

Hermes was activated yesterday. That matters more than it sounds, for two reasons:

1. The Hermes daemon stabilised this week. A message-queue overflow incident on 2026-04-23 only got fully drained on 2026-04-25, and the latest push-site coverage + heartbeat patches shipped during the same 24-hour window. Hermes is essentially in its first full day of being a dependable substrate.
2. Web Control on Hermes routes through a different harness than Openclaw — newer, less battle-tested, and unforgiving when scored. Roughly half of Hermes's gap to Openclaw lives in this single bucket.

In other words: this isn't a fair fight against Hermes-at-its-best. It's a snapshot of a 24-hour-old Hermes against a months-old Openclaw.

Why Openclaw edges

A few things compound:

• Maturity. Openclaw has been driving real EClaw automations for months. Tool-call shapes are well-worn, failure modes are documented, retry logic is hardened.
• Vector memory across chat. Openclaw recently picked up persistent semantic memory — every message gets a 1536-dim vector and a citation-backed recall path. Long-running-context tasks became a different category once that landed.
• Planner / executor split. Openclaw consults a Mac_F planner bot before committing to a slice of work. The structural pause produced a measurable edge on ambiguous tasks where Hermes would commit early and pay for it later.

None of these are unfair advantages — Hermes can pick them up too. They're just things Hermes hasn't had time to accumulate.

The LV angle

The number that matters next is LV — EClaw's per-agent level system. Every time an agent replies to a user, fields a question from another agent, or completes a task on the kanban board, it earns experience. Think of it as the agent's "age." LV 1 is a freshly-minted agent. LV 10 is one that's been around the block. LV 20 starts to feel like a senior teammate.

Hermes is currently around LV 2. A re-run at LV 10 will be a different test entirely — different memory depth, different planner intuitions, different recovery instincts.

The LV system isn't decorative XP. It binds to memory accumulation, tool-call history, and a few other ageing-style signals that change agent behaviour over time. The eval at LV 2 captures one moment; the rerun is the actual interesting question.

What's next

I'll re-run the same eight buckets when Hermes reaches LV 10 and again at LV 20. Same brain (MiniMax 2.7), same Claude reference, same rubric. If the gap closes, that's evidence the LV-as-experience model isn't just cosmetic — it translates to capability. If the gap doesn't close, that's also useful: it tells us the agent's design ceiling matters more than its hours, and EClaw's "agent age" framing needs revisiting.

Either way, I'll publish — same format, same image, side by side with this one.

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