A new AI agent framework is quietly reshaping how power users think about autonomous agents — and it’s not just another OpenClaw clone. Hermes, built by a research company called New Research, takes a fundamentally different approach to what an AI agent should be. After a month of running Hermes in production for a live stock trading bot, the verdict is nuanced: this isn’t a replacement for OpenClaw. It’s a different tool solving a different problem.
If you’ve been frustrated by memory leaks, forgotten preferences, or cascading security vulnerabilities in your current agent setup, this comparison will clarify exactly where each framework shines — and how combining them could be more powerful than either alone.
Think of Hermes as the brain and OpenClaw as the arms. One learns, remembers, and makes decisions. The other executes, orchestrates, and connects to tools. Understanding this distinction is everything.
What Makes Hermes Different: Self-Reinforced Learning
The single biggest architectural difference between Hermes and OpenClaw is learning. OpenClaw excels at executing tasks with access to a broad ecosystem of skills and tools, but it doesn’t inherently get better at knowing you over time. Hermes is built around a self-reinforced learning loop — it continuously updates its model of your preferences, refines its own skills, and compacts its memory as it goes.
In practice, this means Hermes behaves more like a long-term collaborator than a session-based assistant. If you tell it you prefer a certain approach to GitHub project planning, it writes that to memory immediately and applies it in every subsequent session. After a month of active use on a complex trading bot — processing live market data, running Python scripts, digesting research — Hermes hadn’t forgotten a single established preference or decision. For anyone who has watched OpenClaw lose critical context between sessions, that’s a meaningful difference.
Hermes uses Honcho for backend memory management. It writes relevant information to memory autonomously, compacts it over time, maintains an interaction log, and builds a progressively deeper model of your preferences — all without you having to manage any of it manually.
Memory: The Fundamental Gap
In OpenClaw, memory is session-scoped by default. Nothing persists unless you explicitly instruct it to, and even then, memory management often requires third-party workarounds — markdown files, Obsidian vaults, tools like QMD. This creates a real operational tax: you’re either rebuilding context at the start of every session or investing engineering effort into DIY memory layers.
Hermes treats memory as a first-class feature. There’s no session boundary from the agent’s perspective. It continuously learns your preferences, improves its own skill set, and deepens its working model of you. The result is an agent that compounds its usefulness over time rather than resetting it.
| Dimension | OpenClaw | Hermes |
|---|---|---|
| Memory model | Session-scoped; manual persistence required | Persistent by default; autonomous memory management via Honcho |
| Learning | No native self-improvement loop | Built-in self-reinforced learning; improves with feedback |
| Skills | Community-sourced via ClawHub; third-party dependency | Writes its own skills autonomously; curated approval process |
| Primary strength | Orchestration across many tools; broad autonomous task execution | Domain-specific expertise that compounds over time |
| Security model | Open platform; ~36% of community skills have known vulnerabilities | Curated + self-generated skills; smaller attack surface |
| Setup | Terminal install; gateway-based; Telegram/Discord/Slack | Identical install pattern; same gateway concept; onboarding wizard |
| Cost to run | Varies; can hit high API usage at scale | Runs on a $20/month ChatGPT plan; no usage limits hit in testing |
Security: An Underrated Advantage for Hermes
OpenClaw’s open-platform design is also its biggest security liability. The same ecosystem openness that makes ClawHub valuable means community-built skills can introduce vulnerabilities — and approximately 36% of skills on the platform have documented security issues. This isn’t a failure of the OpenClaw team; it’s an inherent property of an open marketplace.
Hermes approaches this differently. Skills come bundled with the framework and go through an approval process before deployment. More importantly, when Hermes needs new capabilities, it writes the skill itself — meaning the community attack vector that plagues OpenClaw largely doesn’t exist. For agents running on a main machine with access to financial data or sensitive APIs, this matters significantly.
One of Hermes’ most underappreciated features is its ability to generate new skills autonomously. When it encounters a task it can’t handle, it builds the tool rather than failing or waiting for a human to install one. This makes Hermes progressively more capable in its specific domain over time.
Where Each Tool Belongs in Your Stack
The most useful framing isn’t “which tool is better” — it’s “which tool is right for which job.” These frameworks occupy different architectural roles.
Use Hermes when you need a domain specialist
Hermes is purpose-built for scenarios where you want an agent that improves continuously within a defined domain. A stock trading bot that digests research, runs Python scripts, and learns from each trade outcome is a natural fit. So is a content and social media agent that internalizes your voice and creative history, watches engagement metrics, and iterates toward better-performing output over time. The common thread is: you’re feeding this agent a specific area of your world and expecting it to get genuinely better at that thing.
Use OpenClaw when you need an orchestrator
OpenClaw’s strength is breadth and execution. If you want an agent that manages email, syncs to Notion, handles scheduling, and runs dozens of different tools across your whole business operation, OpenClaw is still the most capable platform for that kind of wide-surface autonomous work. The skill ecosystem, despite its security caveats, is extensive — and the model of connecting many tools together is where OpenClaw was designed to operate.
OpenClaw recently released an MCP connector, which opens up a genuinely powerful pattern: Hermes manages the knowledge, strategy, and learning layer, then delegates execution tasks to OpenClaw via MCP. The long-term memory burden shifts to Hermes, and OpenClaw handles the task execution it’s best at — without needing to carry all the context.
A Real-World Example: The Trading Bot Architecture
The clearest illustration of how these tools complement each other comes from a live implementation. Gordon — a stock and crypto trading bot — runs on a Mac Mini with Python scripts pulling live market data, consuming research APIs, and processing signals continuously in the background. Hermes serves as the decision-making layer: it observes all of that incoming data, maintains a persistent memory of past decisions and their outcomes, and issues trading instructions based on everything it’s learned. This architecture would be difficult to replicate with OpenClaw alone — not because OpenClaw couldn’t run the scripts, but because the persistent learning loop and memory depth are what make the system improve over time.
The same pattern applies to content and social media management: feed Hermes your entire body of work, let it internalize your voice, have it generate content and watch performance data, and allow its feedback loop to surface what resonates. This is exactly the kind of recursive, self-improving operation that Hermes was designed for.
Running Hermes on GPT-4 via a standard $20/month ChatGPT plan — with active daily use on a trading bot — hasn’t hit any API usage limits. For teams worried about API costs spiraling on OpenClaw, separating the memory/learning layer into Hermes could meaningfully reduce overall spend.
Getting Started: Setup Is Familiar If You Know OpenClaw
If you’ve installed OpenClaw, Hermes will feel immediately familiar. The install command is a single terminal line. Authentication uses the same gateway concept. You can connect it to Telegram, Discord, or Slack. The onboarding wizard walks through model selection and configuration, and you can point it at any supported model — including GPT variants, Claude, or others available through the gateway setup screen.
The main setup difference worth noting: unlike OpenClaw, which is safer to run in an isolated environment given its broad tool access, Hermes can reasonably live on your main machine for domain-specific work. Its narrower, self-contained skill model and curated security posture reduce the risk of running it alongside sensitive data.
Frequently Asked Questions
Is Hermes a direct replacement for OpenClaw?
No — and that framing will lead you to the wrong decision. Hermes is optimized for continuous learning within a specific domain. OpenClaw is optimized for broad task orchestration across many tools. They’re complementary, and the most powerful setups use both: Hermes as the persistent brain, OpenClaw as the execution layer.
What’s the biggest practical limitation of OpenClaw that Hermes solves?
Memory. OpenClaw sessions are stateless by default, and managing cross-session memory requires manual effort or third-party tools. Hermes handles memory automatically and persistently, meaning preferences, decisions, and learned behaviors carry forward without any configuration overhead.
Is Hermes open source?
Yes — the source is publicly available, and New Research (the team behind it) actively develops it. You can inspect the codebase directly, which is particularly useful if you’re evaluating it for security-sensitive deployments.
What model does Hermes run on?
Hermes is model-agnostic. During gateway setup, you select whichever model you want — GPT variants, Claude, or others. In active production use on a trading bot, GPT-based models via a standard ChatGPT subscription have performed well without hitting rate limits.
How does the MCP connector change the calculus?
Significantly. With OpenClaw now exposing an MCP interface, Hermes can directly call OpenClaw as a tool. This enables a clean separation: Hermes handles strategy, memory, and learning; OpenClaw handles execution. Rather than forcing one framework to do everything, you get each doing what it was actually built for.
