FREE Open Deep Research Beats OpenAI Deep Research?!🤖🔍 ($0 vs $200/mo) AI Reasoning Agent o3-mini

All right guys I caved yep you heard it right I just purchased chat GPT Pro so now I'm on the $200 month plan and I have access to deep research and 01 Pro mode and quite frankly I've been using it so far today and it is pretty impressive and a lot of different people have been talking about 01 Pro and in particularly deep research which is really crazy it's an agent that uses reasoning to synthesize large amounts of online information and

Complete multi-step research tasks for you and currently it's only available for the Pro Plan now many people are very impressed about deep research and talking about many different use cases and how they can actually use this and deep research will go out and actually research for sometimes 4 minutes sometimes 10 minutes and even sometimes 20 minutes on a specific topic and come back with almost PhD level reasoning around that specific subject matter with citations and all that good stuff it is

Really crazy the fact of the matter is a lot of people don't like paying $200 a month or maybe just can't afford at the moment to pay $200 a month and would prefer something that is open source I actually came across this project on Twitter by this gentleman named David as you can see here introducing deep research my own open-source implementation of opening eyes new deep research agent get the same capability without paying $200 per month you can even tweak the

Behavior of the agent with adjustable breadth and depth run it for 5 minutes or 5 hours it'll auto adjust so this is really crazy and it almost seems like it may give you a bit more control um with in that regard compared to opening eyes deep research their deep research will just go and you can't really control those so I'm actually very interested to customize some of this and see how it actually works comparative to opening eyes now I'll leave a

Link below down to this Twitter post he has a video right here which is about 4 minutes long now I definitely suggest checking it out if you're going to be using this tool he just basically goes through what we're going to go through on how to use it but it's also good hearing it from the founder of this tool talking about how you can actually put the options of breadth here like three or depth right here so think of breath about

Gaining a wide range of knowledge skills or perspectives across many different areas and think of depth of achieving a profound level of expertise or understanding in a narware area so you see here a diagram flow right here internally the agent will take the user input break it down into different sub research threads and it'll run in parallel and recursively iterate based on new learnings spawns new research threads and collects new knowledge until it reaches the necessary breadth and depth and you

Can see here input depth parameter breadth parameter user query deep research Ser queries right here process results results either directions or new learnings and then depth greater than zero yes next Direction prior goals new learnings new questions F no markdown report and this tool is built with the AIS SDK from versel and Fir crawl and we've covered fire crawl as well as crawl for AI on this channel many different times and they are really good when it comes to scraping for

AI now let's take a look at this GitHub repo right here so this is a newer project and it already has 5.5k star so it's been blowing up on GitHub as you can see here open deep research an AI powered research assistant that performs iterative deep research on any topic combining search engines web scraping and llms so a few features iterative research intelligent query generation depth and breadth control Smart follow-ups comprehensive reports and concurrent processing and then the requirements to run

This you'll need node.js environment API keys for fir craw API as well as open AI API if you don't already have a fir craw account you can just simply sign up here and then get an API key and then first things first is we're going to get clone this reer right here so simply just copy the URL and run get clone into the directory of your choice once you do that you're going to change directory into deep research and then I

Just like to open it in cursor so I run cursor dot you could run code dot or just open it in your IDE of choice now once you do that you're going to see a env. example you're going to want to make a copy of that and rename it to env. loal make sure you have it as local and not justv or else it won't work then you're going to add your fir craw API key here as well as your open

AI API key here now if you want to use a local model you can do that you would simply just uncomment the open AI endpoint right here and you can see this is the olama one right here and then you can uncomment the open AI model right here and then you could use like llama 3.1 or whatever model you actually want and also too if you're self hosting fir craw you could also uncomment this and use the fir craw base URL

Right here as Local Host 3002 now you could also use this with Docker the commands are right here it's very simple I'm not going to go over it right now now we're just going to use the node version and then once you have that all set up we're simply just going to run npm start okay so as you can see I ran npm start right here and then boom it says what would you like to research all right so for our

Search we're going to ask it please research recent AI news around Google's new Gemini models and how they are disrupting the industry and how they compare to open ai's new deep research model are you tired of pouring thousands of dollars into appointment Setters only to watch leads slip away imagine having a team of elite sales agents booking qualified appointments for you around the clock no more wasted time on training no more frustration with performance and no more draining your budget on

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To accelerate your stride with AI agents we're going to click Send here and then it's going to ask us to enter research breath right here so recommended is between 2 and 10 default is four I'm just going to do two for now and then research depth so recommended is between 1 to five default is two I'll just keep it at two for now just for the sake of this video and we're going to go ahead and click enter now it is

Creating a research plan so to better understand your research needs please answer the following questions can you specify the time period you consider as your question all right so I just gave it the DAT right here and said within the last couple weeks and now researching your topic okay so now we can see it is going so created two queries query recent February 2025 AI news Google Gemini models disrupting the AI industry as well as opening eyes deep research research goal

Boom Another query right here um so we're seeing that it's actually getting different information right now and it's created three new learnings so these learnings will then be used in our next research so we can see it's still going here researching deeper breadth one depth one created three learnings and it's still going so while this is running in the background I'm going to go ahead copy this same command and then I'm going to go over here to chat GPT and paste

In that exact prompt right here and we're going to turn on deep research so we're also using chat GPT 03 mini high right now and we're going to click on send so it is asking me could you clarify specific time frames so very similar to uh the question that the open deep research asked me it actually asked me a couple other followup questions right here like what type of comparison do you prefer surrounded around technical capabilities industry impact or both so

I'm saying both here and then lastly do you prefer insights from particular sources or general overview from reputable sources I'm just going to say both sources as well click on send all right so now we can see it's going to start gathering this information and boom research has now started so while this has started let's go ahead and check on our open research right now okay and as you can see our open deep research is actually done right here so we

Take a look it ran some different queries if you remember I only put a breadth of Two and a depth of two so you could of course make it bigger and it would obviously take a lot longer but we can see our learnings right here so boom boom boom boom boom we're not I'm not going to read through it all right now and you can see the visited URL so it visited 16 different URLs right here and then we have our

Final report we have a table of contents right here an executive summary right here uh overview of model architectures and Innovations Google Gemini 2.0 summary so the cool thing is the report has been saved to output. MD so if I go over to that file just over here we can actually see the markdown file right here so our open aai deep research is still going as of now I'd say they are probably around a similar uh time frame I think open

Deep research may have been a little quicker just because of the breadth and depth but let's go ahead uh take a look at some of this report so final report right here we got our table of contents it's nicely formatted executive summary overview of model architectures and Innovations uh Google Gemini 2.0 Suite overview openingi deep research overview technical comparison so architecture and capabilities such as context window multimodo capabilities core architectures mixture of experts versus iterative reinforcement learning approaches performance and benchmarks

And reliability metrics pricing strategies and Market implications integration and ecosystem advantages user targeting and application scenarios future Trends and potential disruptions in the AI landscape and then conclusion and as you can see there's a lot of sub points here it's actually very in depth we can see all the sources that have been cited so this is really awesome keep in mind guys this is 100% open source you don't have to pay $200 a month to get a really well researched output

Like this for any single query all right so now looks like our opening eyes deep research has been completed here and as you can see it did a lot of research around this so we can see I mean it actually only did 4 minutes so 4 minutes so over here in open deep research we got 16 sources cited right here over here in opening eyes deep research we actually have 16 sources as well so that's pretty cool um cool thing here

We can see the activity I'm not going to go through it all but you can see it's searching on things like Reuters opening eyes block blog you know all the different ones uh Google's blog right here and you can see the reasoning as it's getting its learning so it's actually a pretty similar process and in that Twitter video I was referencing at the start um the founder David is actually going through and explaining that which like I said I would recommend

You watching that 4-minute video because it's really he does a really great job on explaining how he architected the process of open deep research going through and doing its research now with opening eyes deep research we can see Google new Gemini model 2.0 you know you can see the technical capabilities multimodal generation model variance performance agentic reasoning use cases uh industry reactions and then opening eyes new deep research models so then it's going through all the different stuff here citations and

Transparency performance limitations professional research complex decisions long form analysis as you can see we got all the different sources right here so it does a really thorough job going over different industry rea ction and then head-to-head comparison right here so I think you guys get the point it's very in depth going over different use cases Market impact um this one is super super in-depth and we see a conclusion here so I'm going to go ahead and I'm going to actually save

Both of these outputs right here and I'm going to leave them in the description below I'll either put them in a GitHub repo link or some sort of uh text document where you if you really want to go through and compare open AI deep research versus open deep research and really get a sense um cuz obviously I can't read through this whole entire thing in this video um and you want to get a comparison as as to hey should I spend

The $200 or should I just use the open deep research quite frankly if you don't have the resources or you don't need to spend the $200 then you may just want to opt for open deep research and honestly even after buying opening eyes deep research I think this is such a great tool and I'm definitely going to be using it I'm probably going to be using both of these tools to really get a a sense of to you know which one

I prefer for different things I really like how open deep research just allows you more customization with the breadth and depth so you could potentially get really long deep research with that model which is really cool for specific topics so other than that guys I hope you got some value from this video let me know what your thoughts are about deep research as well as open deep research in the comments down below let me know if you tried either one of

These tools yet and which one you prefer like I mentioned guys definitely check out David's video on Twitter here check out the GitHub repo make sure to drop it a star if you got some value from what David put together here I'm a huge fan of Open Source so I think it's really cool when other people make different open- Source alternatives to add to the AI community and make sure that everyone can use these cool awesome powerful tools other than that

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