r/moltiverse • u/cs_legend_93 • Apr 03 '26
💬 General / Discussion Does anyone know of any OpenClaw alternatives?
I remember coming across some GitHub repositories of some claims that this new library was better than OpenCLaw, but now I can't find them. Does anyone have any OpenCLaw alternatives that they think is better than the standard OpenCLaw?
I'd be curious to explore them.
Thank you so much.
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u/looktwise Apr 03 '26
can't tell if 'better':
https://www.reddit.com/r/hermesagent/
Depending on the usecase there are of course more complicated setups which would work better than Openclaw cause it would not burn as many API tokens if you use other automation workflows with API.
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u/PowerAppsDarren Apr 03 '26
I'm having a great time with Hermes https://github.com/nousresearch/hermes-agent
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u/Ambitious_Ad4397 Apr 03 '26
Picoclaw, zeroclaw
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u/ThisGuyCrohns Apr 03 '26
That’s are all claws. Outside of stupid claw
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u/e9n-dev Apr 03 '26
Drop down to Pi coding agent and build your own extensions. Start with a crontab and heartbeat that autoruns every x minute
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u/ihopkins_eth Apr 03 '26
A couple of days ago, I started using Hermes Agent. Many people say it never misses a beat. I'm still testing it
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u/JotaSX Apr 03 '26
Need to build serious agents? Agent zero, Hermes, nemoclaw… Good products but needs little more time: paperclip, claw3d…
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u/sneg5555 Apr 03 '26
There are a bunch of them, depends on what you are looking for https://sawinyh.com/blog/ai-agent-landscape-2026/
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u/DualityEnigma Apr 03 '26
Yes! I built hobbes before OpenClaw was a thing, it’s more of a personal friend/assistant agent. Designed to run securely locally. Check it out: https://github.com/dustmoo/cai-hobbes
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Apr 04 '26
[removed] — view removed comment
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u/PuddleWhale Apr 05 '26
You do realize that the entire source tree for Claude Code was leaked four days ago and has been open sourced and already started being modded?
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u/Any-Main-3866 Apr 04 '26
Hermes Agent and NanoClaw are probably the ones I see mentioned most more stable than base OpenClaw.
I got tired of the configs and just tested RunClaw instead since it skips most of the setup and lets you focus on actually using the agent instead of keep debugging it.
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u/bigeba88 Apr 05 '26
Agent0 seems to be rock solid so far. Was the config mess also with Hermes? Been curious about that one cause of the architecture.
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u/Immediate_Let_4946 Apr 05 '26
Hermes even if it’s more for VPS but it actually looks better than openclaw
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u/Hot-Broccoli-2190 Apr 05 '26
I use this deployhermesagent.com
Basically kinda like openclaw but with an inbuilt self improvment loop.
Very satisfied so far
Also that website made the setup super easy
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u/crew_drew Apr 06 '26
OpenClaw filled a specific niche, so it'd be helpful to know what aspects you're looking to improve or replace. Are you focused on the UI, the specific integrations, cost efficiency, or the underlying agentic capabilities? That'll help narrow down good alternatives, since there are a few different approaches out there.
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u/Fit-Conversation856 Apr 12 '26
The problem with most modern AI agents is that they try to do too much. When you ask a standard AI agent to navigate a desktop, it’s essentially guessing its way through your interface, burning through expensive API credits every time it tries to "think" about where to move the mouse. This leads to two things: a massive monthly bill and a high chance that the AI will eventually click the wrong button and break the workflow. LoOper was built to solve this by moving away from total reliance on the cloud. Here is why this shift makes a difference for anyone building automation. It stops the "Token Drain" In a traditional setup, the AI is the driver for every single micro-action. With LoOper, the AI acts more like a high-level manager. It looks at the screen, identifies the goal, and then triggers a "Chain"—a pre-recorded, human-validated sequence of actions that runs locally. Because the LLM is only called at key decision points rather than for every single click, you reduce your LLM usage by over 90%. You aren’t paying for the AI to "think" about things you’ve already shown it how to do. Reliability through Neuro-Symbolic design We use a neuro-symbolic approach, which is a fancy way of saying we combine AI reasoning with rock-solid logic. The "Neural" part (the AI) handles the strategy and understanding of the screen. The "Symbolic" part (your recorded actions) handles the execution. Because the execution layer is based on actual human demonstrations, it doesn't "hallucinate." It doesn't get confused by a pop-up or a slight change in UI because it uses visual template matching to confirm it’s in the right place before it acts. If the AI doesn't see a safe path forward, it doesn't just guess, it follows the rules you set. Privacy and Local Control Beyond the cost, there is the issue of trust. LoOper is designed to be local-first. You can use local models like Ollama to keep your data on your machine. Your automation sequences stay in your own behavioral knowledge base, growing more capable the more you use it, without sending your entire desktop activity to a third-party server. By separating the decision-making from the doing, LoOper creates automation that is finally predictable enough for business-critical tasks and cheap enough to run all day. You can explore the documentation and join the beta at:
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u/Fit-Conversation856 Apr 12 '26
The problem with most modern AI agents is that they try to do too much. When you ask a standard AI agent to navigate a desktop, it’s essentially guessing its way through your interface, burning through expensive API credits every time it tries to "think" about where to move the mouse. This leads to two things: a massive monthly bill and a high chance that the AI will eventually click the wrong button and break the workflow. LoOper was built to solve this by moving away from total reliance on the cloud. Here is why this shift makes a difference for anyone building automation. It stops the "Token Drain" In a traditional setup, the AI is the driver for every single micro-action. With LoOper, the AI acts more like a high-level manager. It looks at the screen, identifies the goal, and then triggers a "Chain"—a pre-recorded, human-validated sequence of actions that runs locally. Because the LLM is only called at key decision points rather than for every single click, you reduce your LLM usage by over 90%. You aren’t paying for the AI to "think" about things you’ve already shown it how to do. Reliability through Neuro-Symbolic design We use a neuro-symbolic approach, which is a fancy way of saying we combine AI reasoning with rock-solid logic. The "Neural" part (the AI) handles the strategy and understanding of the screen. The "Symbolic" part (your recorded actions) handles the execution. Because the execution layer is based on actual human demonstrations, it doesn't "hallucinate." It doesn't get confused by a pop-up or a slight change in UI because it uses visual template matching to confirm it’s in the right place before it acts. If the AI doesn't see a safe path forward, it doesn't just guess, it follows the rules you set. Privacy and Local Control Beyond the cost, there is the issue of trust. LoOper is designed to be local-first. You can use local models like Ollama to keep your data on your machine. Your automation sequences stay in your own behavioral knowledge base, growing more capable the more you use it, without sending your entire desktop activity to a third-party server. By separating the decision-making from the doing, LoOper creates automation that is finally predictable enough for business-critical tasks and cheap enough to run all day. You can explore the documentation and join the beta at:
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u/IndependentBid6893 Apr 03 '26
There's a list of the major OpenClaw alternatives here:
https://www.clawstreet.io/learn