Transcription
Open claw and auto research claw. How to turn a simple chat message into a full research paper fully automated.
So what is this? Well, auto research claw is a free open source tool that writes research papers for you completely autonomously, right? So for example, if you type in one sentence like research AI agency marketing and walk away, it comes back with a full paper with real sources, charts, experiments, and a fully formatted document. Right?
This is super powerful because you can research anything you want. If you're doing market research, competitor research, if you're trying to understand something better, then this is one of the most powerful ways to do it and you can get a full research paper in just like one click using this fully autonomous AI agent. Right now, you can see it's already got 1,500 stars on GitHub. It is available for free. Literally to install it, it's really, really simple and easy.
So, let me show you an example of how I installed it earlier. So, you can just say, can you install this? And then give it the GitHub link. And then it installed it within this is with openclaw. So within 2 minutes it'd already installed it and you could run it straight away right and then for example we can say okay research Nemo claw it will just go off and start using the tools to implement that directly right works really well with open claw and the crazy thing about this is it has a 23 stage research pipeline right which is very very deep right it's kind of like running deep research directly inside your open core and you just type in what you want and it goes off and does it if you have an idea here auto research clock can can just write it up.
You drop a research topic inside there and then you can see all the different tools that use this. Now it can generate nice charts in there. It can for example verify sources as well which is great. So it has a four layer citation integrity option which basically just means like it checks if if the content is hallucinated or not. One of the biggest differences here as well is like it has multiple different stages inside the research process and one of them is called um proceed or pivot right and so it can loop through and basically figure out okay do we need to uh pivot or do we need to change on the angle and that sort of thing and also it can have multiple agents that debate between each other for hypothesis generation result analysis and peer reviews as well. You can also learn. So it extracts lessons from the content itself. And then for future runs, it's just going to keep improving. So it's got like that self-improvement loop. It's got knowledge base you can use and yeah, it's really easy to integrate with open claw.
So if you're looking for something like that goes really really deep when it comes to research and let's say for example, you want to give it a topic and then it goes off and does that in the background. Maybe you go to sleep and wake up and then it's ready to go. Then you can use something like this, right? There's 23 stages and eight phases inside the research process. So you can see all of them right here which is pretty crazy and you can see the key features as well of how it works. But yeah, it's pretty amazing. This is a full chart as you can see and you just tell it what to do. It goes off and it does it.
The one thing that I would say here, the one caveat I would say is like it does take time. So for example, normally if you're hiring someone to write a proper research paper, it's going to be expensive. It takes like weeks of back and forth. this tool can do it in about an hour for free um with no hallucinated sources. That's the main thing here is like you're avoiding hallucinations inside your content. Now, I would still check it just to make sure. Still check the research papers, but that's basically how it works, right? And it works in in 23 different stages.
So, how does this work in practice? Well, you want to think of it like this, right? It's like a research team that never sleeps, doesn't make up sources, and delivers in hours. So the simple flow is like you give it a topic like research how AI is changing SEO for small businesses then it finds a real paper right so actually search archives for real papers to find real sources and then it screens each paper for relevance and quality and only the good ones make it then it runs experiments so actually designs and runs experiments based on your topic and it can write Python run it in a sandbox and then collect actual data based on that topic after that multiple AI agents actually debate each other this is pretty cool so multiple AI agents will argue with each other about the best hypothesis, the best analysis, and whether the findings actually make sense. So, this multi-Agent debate makes the output way smarter than just like one AI working alone. And then it will write the full paper, usually about 5,6,500 words, format it, check every single source, and then you get a folder with the deliverables and the research paper. So, it's pretty insane.
And it's self-learning as well, so it gets smarter every time you use it. Every research run teaches the system something new. So when you're using this extracts lessons from each run, what worked, what failed, and what was faster. And then those lessons are stored in a 30-day time decay. It's older learnings fade out and recent ones are more useful. So the more you use it, the smarter it gets.
Now, if you want to use it straight away, I've actually got a bunch of use cases inside the video notes today. So you could use it for example for lead magnets or white papers, content authority, internal strategy reports, client deliverables, right? Uh product validation. These are all cool ways you could use this as well. And the other thing here is that it gets more advanced because you could actually schedule these tasks. So you could say, okay, each month automate a new research report on the market or automate research on like how AI agents have changed this month. And then you could schedule in new tasks because you got the power of open claw working with the power of auto research claw and that sort of thing.
Things to know before you start. So you need to open AI API key. Experiments obviously need more computing power. First run is going to be the slowest, but after that it runs up and then you can always pause it for human review at three key stages, right? Or you can skip them with auto approve and it's MIT license, means it's free to use, modify, and build on top of. It also speaks in multiple different languages and that's basically it.
All right, so just to recap, auto research is a tool that turns a single chat message into a full research paper. Works best through openclaw. So you can just say research topic. It handles everything. It pulls real sources. And to install it, you just paste in the GitHub link into the chat and then it's good to go inside OpenClaw. It runs real experiments, runs and uses multiple AI agents, has 23 stages of research and produces a full deliverables folder locally as well. So for business use, you know, white papers, content, client report, strategy, research, and product validation is pretty cool. It's completely free and it gets smarter every time you The only thing you would pay for is the LLM API course. So that's basically it.
Thanks so much for watching. If you want to get the video notes from today on auto research claw, you can get that right here. We also have a full six-hour course on how to use OpenClaw and another 3-hour course right here. Basically, we just update this community every single day with new cool trainings on exactly how to use um all the latest updates that just came out, right? So, you can see, for example, yesterday we covered OAM and OpenClaw, GLM5 Turbo, another video tutorial on the latest updates from Claude, all sorts of cool stuff which you can see right here. So if you want to learn, you can get that from the arprofitboard.com link in the comments description or go to the arprofit board directly.
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