I fixed OpenClaw so it actually works

What if OpenClaw finally worked the way it was meant to?
In this episode, we dive into the improvements that transformed OpenClaw into a more reliable, functional, and practical AI tool. Discover the key fixes, the challenges behind the development process, and how these changes improve performance, stability, and the overall user experience. Whether you're an AI enthusiast, developer, or automation builder, you'll gain valuable insights into what it takes to make open-source AI software truly usable.
🎧 Tune in to learn how OpenClaw was fixed—and what those improvements mean for the future of AI-powered workflows.

Transcript

SPEAKER_00 0:00

Jensen Wong said just the other day that every company needs an open claw strategy. I mean, he's calling it the new computer, but how do you actually wire this thing up so it holds up in the real world? So I sat down with my friend Moritz and we went through the exact setup that takes you from install to production. This is a super tactical, saucy episode. The clearest way to understand all these concepts. How to structure OpenClaw versus Cloud Cowork? How to set up personalization so it sounds like you, how to make memory actually persist and improve over time, how to configure models and fallback so it stays reliable, how to run heartbeat.md so nothing breaks in the background, how to lock down security so you can trust it with your business. And then what are different use cases like how do I use this thing to come up with ideas for me and create content that doesn't look like AI slot? This is the most comprehensive one-hour master class on how to go from I want to install OpenClaw to I've got this thing running and it's a digital employee that's working for me. Moritz Kram on the Startup Ideas pod. Moritz, by the end of this episode, what are people gonna get out of it?

SPEAKER_01 1:22

So if you're someone that has heard about open claw, maybe you even tried setting it up but didn't see the value and it didn't work very well for you, by the end of this episode, you will have a 10-step guide to 10x your open claw and make it actually useful. You learn how to set it up the right way, how to tweak it, and understand how it works under the hood so that it becomes basically like a superhuman employee. And at the end, I will also share some of the top use cases and systems I have built with my open claw.

SPEAKER_00 1:57

So basically, how people are using it, how you're using it in the wild. I know you're you've got you're out there, you've got these digital employees doing things. You're gonna show us how to use it, you're gonna explain these concepts clearly, you're gonna take people through all of it. Moritz, you're an absolute angel. Let's get right into it.

SPEAKER_01 2:15

All right. Awesome. So um I thought to start this out, let's just go over the basics and um talk about first what even is OpenClaw. Uh, for people that may have not heard about it. Um basically, OpenClaw um is an agent, a personal agent that can do things for you. It uh remembers things and gets better over time, it's proactive and it can actually automate things for you. Um, it also has access to built-in functionalities, tools, and skills. Um, and you can also bring it into any chat tool basically, so it's quite flexible in that sense. Um, and so it's kind of the first really personal agent that exists, and also currently I would say it's the closest to what we have of a truly autonomous agent. So um, yeah, now you might be asking, um, okay, but how is it actually different from ChatGPT and Cloud Code, right? Um, so in ChatGPT basically, if you um think of this as you communicating with with ChatGPT, um what's always first thing first thing you'll notice is like it's living in the cloud, right? So you're kind of always communicating with this thing in the cloud, uh, this intelligence in the cloud. Now, ChatGPT does have um like things built into it that they've built into it over time. In the beginning, it was kind of just this like this chat intelligence thing, um, and then they've added memory over time, they've added uh some tool use over time, like web search and so on. Um but yeah, you can like fundamentally think of chat GPT as uh just just a chat, right? Um okay, so what was kind of this like next um paradigm shift that was um that was when cloud code came out. And the fundamental difference between Cloud Code and ChatGPT is that Cloud Code is living locally on your machine. That's the main difference there, right? Um, and it also has memory in a sense, although it's actually more about uh context, managing your context. Um, it also has uh tools, although um I would say the tools are a bit more powerful because they're more flexible and you can kind of manage uh which tools it has access to better. And then the fundamental difference is that it can really like write and uh read files locally, and so the first big use case that um came out of this was that it's just it's really good useful for coding. Um, that's why it's called cloud code. Uh because you know, like when you're coding, you usually like have a huge folder of files locally on your machine. Uh, and if you want to do that in the cloud, it's like super, super cumbersome. Like you you basically need to like switch, uh switch around, upload those files all the time, switch around, copy and paste. And so this made it really useful for uh for coding. Um, and over time I think people like started realizing that there are all of these other cool use cases too, um, like marketing, um, and and that's kind of like starting to become more of a hot topic now, I'd say. Okay, so now what is then actually this next uh stage uh which is open claw? Like, how is open claw actually different from uh cloud code? I would say one of the main differences is that uh the communication layer is is different. Like you can communicate with your open claw um through these apps like Telegram, WhatsApp, Slack, and so on. So uh they're very open about that. You can bring it into any of your chat applications, whereas with Cloud Code and the other tools, you're locked into that uh ecosystem, right? Um they also like it's just like Cloud Code, it also has memory and context, it also has tools, although there are also more built-in tools than um than Cloud Code, I'd say. It also has this read-write files locally um capability. And then one more thing that is like really makes it stand out and makes it different from Cloud Code is this heartbeat and crons. Um and so heartbeat is essentially like a 30-minute timer that just continuously like makes your open claw kind of alive, like every 30 minutes it comes alive and it does something for you. So it it you know it it really makes it kind of like this living thing almost. Um, and the other thing is also it has cron jobs built in. So uh you can schedule uh tasks and um you know you can like say at 8 p.m. I want you to do this and this, and um it will run and and do that for you.

SPEAKER_00 7:43

So, where do you see Claude co-work in this spectrum of stuff? I saw today that there's a new research preview that came out from the Anthropic team. It's called Dispatch. Uh basically it's a persistent conversation with Cloud that runs on your computer, so you can message it from your phone and then come back to finish work. So it looks, it feels like Cloud is is sort of moving towards the direction of open class. I'm just curious where you see it in the spectrum.

SPEAKER_01 8:18

Totally, yeah. Um, so I think Cloud Cowork is was basically just they realized that Cloud Code is really awesome, and then they wanted to put a nicer interface on it so that regular people will also want to use it. So they kind of built Cloud Cowork and put that in the app with just a nicer UI. But under the hood, it's it's basically the same as as Cloud Code. Um, and then when OpenClaw came out, um, because it was so so hyped and and you know like so popular, um they realized that okay, like we kind of need to build something that's similar to that too. So we're gonna start building some of the features and adding that. And so throughout the last like two or three months since OpenClaw started taking off, they've been building these features, like the one that you mentioned, uh dispatch, which they released yesterday, um, which is uh you know essentially like you can you can talk to Cloud Cowork through your mobile phone, and that is kind of the feature that OpenClaw has, one of its standout features, which is that you can you know bring it anywhere. Um, and so what I do expect to happen is that it and like Cloud Code and Cloud Cowork and so on, they will all build like their own kind of versions of uh OpenClaw.

SPEAKER_00 9:41

Cool. So basically, you know, how do you decide between open claw and cowork? If someone's listening to this, like how why should they use open claw over cowork?

SPEAKER_01 9:58

Um right now open claw is definitely still more powerful. It has more of these like interesting features built in, like the ones we're we're going to go into in a bit. Um clawed code is still more limited. Um but over time I think they will be relatively similar or anthropic, and you know, all of the big players are going to have their own kind of versions of OpenClaw. But OpenClaw will be like the just the open source version. So it's it then kind of becomes a question, you know, it's like why would you use uh Linux over uh over Windows? So there are just like some um advantages over open source, it's more flexible and people just like it, people can contribute it uh to it and so on.

SPEAKER_00 10:45

Cool. Yeah, I mean the thesis is basically that it's more powerful ultimately because you have the backing of the open source community contributing to it, it's more customizable. So, you know, that that would be one reason, main reason why you'd go to open claw. But yeah, let's continue.

SPEAKER_01 11:02

Yeah, cool. So then let's get into this um yeah, optimized setup. So um you might have tried installing OpenClaw. It's it's like technically um not super hard to do the initial setup. Um, you basically need to go to the website here and and copy this command and paste it into terminal and then follow the onboarding. Um, but where most of the people then get stuck is when they then start using it, and then like all kinds of errors pop up and you know things break and they don't know how to fix it. And so um these 10 steps I'm going to go through now are to uh help you just make your open cloud setup a lot better. And the first thing I uh want you to do is to establish a so-called troubleshooting baseline before you do anything else. And um basically what I want you to do is go into your uh Cloud Um desktop app or or uh web as well, go into projects. Uh you can obviously use ChatGPT2 if you want to, go into the projects feature, they have that too, and then just create a new project, uh call it OpenClaw Support. And inside of this project, you'll want to upload the OpenClaw documentation. And um, the open claw documentation is basically like where all of the solutions are to your problems, um, because like if you run into an error, it's like a very high likelihood that uh somewhere in the docs there's a solution to how you're going to solve this. But obviously, you don't want to go in here and click through it and search for the um search for the answer yourself. So what you can do is go to this site called Context7, um, which is uh just a site that like has up-to-date documentations, search for open claw, and just click this link here with the docs. Um, so they have a like a compressed version of the documentation basically. You can copy that and go back into your project here and just add that as a file. So add um add it in here and then save it. So I've already saved it here. And what this does is it makes um the answers a lot better because normally Claude will, if you ask it something about open claw, it like it can give you the right answer, but it often just makes something up uh if it doesn't really know. It doesn't always go and check the docs by itself through its uh web search feature, and so just adding the docs here is a lot more robust. So, for example, I can say now um how do I pair my telegram? And um, you'll see here that it will, yeah, the first thing it does is let me check the project knowledge and context seven. Uh, so it will actually go and check the uh check the context there to give you the answer.

SPEAKER_00 14:18

This is really smart. I wish I knew this. Because I was just like, you know, prompting uh without the context, and then it would send me to like a random Reddit post that someone or you know, yeah, yeah, and then I try the thing and then it doesn't work, and I'm like, oh yeah, of course.

SPEAKER_01 14:36

Yeah, yeah, happened to me a lot too. And since I have this, um, it's solved like 99% of my problems. Cool. So uh then that's the first step. The second step then is about uh personalization. Um and I uh saw your the previous podcast with Remy, he kind of talked about that too, and it's it's a very similar process as with Claude Code when you set it up. You want to give it like all of the context and uh the context about yourself and also how it should behave. And so one um important thing to know here is that when you um install OpenClaw, it essentially installs this folder here called workspace. So I have this I have this here opened up in um cursor, you can open up open it up in in any other um text editing tool. Um, and inside of this workspace, you then have these important files. So one of them is agents.md, and this is basically the file that defines the agent behavior. So probably the most important file. Um you have the soul.md, which is like defining the agent's personality. Um, we can go into that too. So um basically, like how you want the agent to reply to you. Uh, you have an identity.md, which is similar. Then you have a user.md, which is like info about uh you as the user. Um and so what you should do in the beginning is to just give it a bunch of context so that it can so that it has this context and can work with you in an optimal way. And um the best way to do that is to either you can create these folders and just dump it in there, or you can kind of like talk to your bot and um just give it that information um over time. Um I think what's also very important to know is that every time you have your bot uh opened here and uh are are talking to it in a in a session, um these are the files that are loaded in by default. So um like whatever is in these files the the bot knows about. Um and and so it's important to like manage these files well. Does that make sense?

SPEAKER_00 17:17

Yeah, I think uh it's remarkable how big of a deal setting up these files properly affect output.

SPEAKER_01 17:32

Yeah, yeah, they're very important and um you want to really optimize them over time and um then also tell your open claw to like when when you notice something that you want to happen again or don't want to happen again, just tell your open claw to update these files. And um yeah, get get um familiar with these files also when you initially set it up. Just go into them, read what's what's inside of them already, and so on. Cool. So um you have that. Now uh memory. Let's talk about that because that's uh the third point here, and um that's something that a lot of people are struggling with when they're um setting it up for the first time and using it. Um they often complain about the open claw just like not remembering stuff and kind of being dumb about things. Um and the kind of way to solve that is first of all, I think understanding how the memory actually works in inside of OpenClaw. So as I mentioned earlier, like when you're in a session, these are the files that are just always loaded. Um and you then have actually a built-in functionality which um depending on like what you wrote, it will go and search for things in the memory. Um so so it's it's very important that um your memory is like logged so that something can be searched, right? That's that's kind of obvious. And so the first thing you should do is ensure that your memory is being saved. And uh for whatever reason, like when you when you initially set open claw up, um this file doesn't exist yet, this memory.md. So you kind of need to tell your open claw to create it. And this memory.md file is supposed to be its long-term memory. So it's like where all of the important things that's like learnings and insights over time, and some of your preferences also should be um flowing into and should be logged in there. So this is kind of like the uh more high-level memory. And then there's also a more granular memory, which is saved inside of a uh memory folder. And you can see that here if I go into my uh my workspace. So I have this memory folder, and these are created on a daily basis, so every day it's it should basically write things in there and and like log the things that you've been doing. Um, and these are just more detailed than this uh higher level uh memory. Um so okay, so that's kind of the the first part of of the memory problem. Um then like I found that this command here, um which is it's like I don't want to get too technical, but it's it says set compaction memory flush enable to true and set memory search.experimental dot session memory to true. And what this essentially does is um you sometimes have the problem that you're you're chatting with it and the session gets bigger and bigger, right? Um and it then starts to like when it gets close to the context window, it starts doing what's called a compaction. And when a compaction happens, it loses some of that information because it's like trying to summarize everything. Um, and what this uh setting here does is it says like before you do the compaction, just make sure you write everything into memory. And this way, like none of that stuff gets lost. So this one's um pretty useful. I think some of it may be um already implemented by default in the newer updates, but this was something that helped me a lot um when I s when I set it up in the beginning. Um another thing that I've implemented is as I mentioned, like the the main problem actually of the memory not working is because the memory was not saved in the first place. So I kind of implemented a sort of autosave feature. Um, and I just added into my heartbeat um this extra instruction of like uh essentially like just always every 30 minutes saving to uh to memory. So you can see here uh it says check if today's memory file exists and is up to date, um, and then create it if it's missing, and out of all current sessions, log a summary of what has been discussed and so on. So this just makes sure that every 30 minutes it's really logging the memory and that nothing uh gets lost.

SPEAKER_00 22:41

Makes sense.

SPEAKER_01 22:43

Okay, uh moving on. So uh the fourth step is this one's also really important because when I talk to people um that that want to set it up, like one of the first questions is always okay, uh like what model should I use? And then people start to overthink it a bit because they like see all of these YouTube YouTubers talking about local models and you know, like costs getting so expensive. Because actually, if you if you use a model through the through the API, it can really get very expensive. Like every every request can be like 20 cents and that can stack up really quickly. Um but there is actually a really easy solution to this, and that's what I recommend for most people getting started. It's just using the so called OAuth method. And what that means is you're you're basically using the model through your