FREE Open Source Deep Research VS OpenAI Deep Research VS WebUI Deep Research!🤖 (Save $200/mo)

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⏳ Timestamps

00:00 - Introduction to Open Source Deep Research Alternatives

00:20 - Overview of OpenAI Deep Research and Its Capabilities

01:30 - Comparing OpenAI Deep Research vs. Open Source Alternatives

02:40 - Setting Up Open Source Deep Research (CLI & GUI Versions)

05:12 - WebUI Open Source Deep Research Alternative Setup

09:39 - Running Deep Research Tests on OpenAI, WebUI, and Another Wrapper

12:53 - OpenAI Deep Research vs. Open Source: Research Quality Comparison

18:22 - Open Source Deep Research Report Analysis

20:40 - Summary Table: OpenAI vs. WebUI vs. Open Source Deep Research

24:58 - Sponsored Segment: Stride AI Appointment Setters

26:52 - Final Verdict: Best Deep Research Tool for Your Needs

28:16 - Should You Pay $200 for OpenAI Deep Research?

29:43 - Join Stride AI Academy & Business AI Solutions

Transcript

as many of you guys know I am currently

on chat gbt Pro Plan which means I have

access to open AI operator 01 Pro and

the new kit on the Block that everyone's

been talking about deep research but all

this is hidden behind a $200 a month pay

wall and quite frankly not everyone can

afford that nor do some people want to

give this money up to a close Source

company but at the same time you've

probably seen the hype online and deep

search is definitely great and very

impressive to say the least it can spend

5 to 10 minutes and compile PhD level

reasoning for different topics and it is

just insane the results that some people

have been able to get with it now if

you've been following this channel the

last few days you'll know that we

actually uploaded three different videos

showing you three different ways that

you can get around that $200 a month and

use open-source alternatives so we've

covered so far open-source deep research

a very powerful CLI tool we've covered

web UI which has a deep seek

functionality as well as an operator

alternative to open a eyes operator and

then yesterday we covered another

wrapper which is a gooey interface for

the CLI open deep research project now

I've been getting a lot of different

comments because many people are

interested in this deep research and

people are wondering what is the best

tool to use for deep research should you

go with the open- source tools should

you pay the $200 a month well in today's

video I'm going to show you exactly how

to set up each and every one of these

different tools

and I'm also going to do a comparison

between each and every one so you can

decide which one is best for you and

your particular use case and if you

should go with the open source versions

or the closed Source versions I'm going

to compare all these tools so let's dive

in and start doing some deep research

now all right guys so all links I

covered in today's video will be in the

description down below I'm also going to

be using this document here that I made

and we're going to be putting all the

different links here the resources the

prompts as well as the actual research

output from today's video so you can

analyze the comparison on your own time

if you want and see which tool gets the

best output now if you want access to

this entire document it is going to be

100% for free I'm going to be posting it

in the stride AI Academy if you're not

familiar with that that is our free

community on this channel where you can

network with other like-minded AI

entrepreneurs or AI developers in this

space and I'm also posting all the

templates resources tools Frameworks

videos and behind the- scenes stuff on

this channel in this community so you're

definitely going to want to join just go

to the link that I leave down below for

the stride community and request to join

and then you'll be able to access the

document right away all right guys so

before we actually dive into the test I

want to quickly go over setting up these

tools now I've linked all the separate

videos that I've done to each one of

these tools in depth so if you want to

see a step by step walkthrough on

setting up each and every one check out

the full video for the specific tool

I've also linked the githubschool

see the firec API docs here or browser

use so let's talk about the actual open-

Source deep research CLI version right

here all right so here is the GitHub

repo this is an AI powered research

assistant that performs iterative deep

research on any topic by combining

search engines web scraping and large

language models this is how it

specifically works you have input you

have a depth parameter a breadth

parameter and a user query it starts

doing deep research gathers Ser queries

pulls different results uh has different

directions and or learning

uh analyzes the depth and then either

performs a markdown report or next

directions has prior goals new questions

and learnings all right so this tool is

great but it does just use the CLI and

here's the setup here you simply clone

the repository you install the

dependencies running mpm install you set

your environment variables in the env.

loal file make sure it's env. loal so

you're going to copy the example one and

then paste in your uh API keys and then

you're simply just going to run npm

start to actually run the assistant and

then it's actually going to run within

your command line I went through that

very quick you know I like I said if you

want to use a CLI version check out my

video on it and the instructions are

right here in the GitHub repo for you

but I know most of you would prefer a

nice gooey interface which is why we're

going to be covering the another wrapper

project where if you didn't see my video

yesterday it's essentially built with

this CLI in the back end to do all the

Deep research but it's using their nice

user interface gooey and this is what it

actually looks like so you can go to

their Cloud version right here another

rapper.com and you'll be able to

actually access um and do deep research

if you just configure your API keys

right here within their Cloud version

and I'm going to show you this in just a

second but I'm going to show you on the

actual self-hosted version well you can

uh change your breadth and depth

parameters right here so you have it's

cool because you can't do that

functionality with an open AI deep

research it just actually goes about and

does the specific deep research for you

know could be 5 minutes or could be 20

minutes depending on the specific

question you ask it but here you can

actually change these parameters which

will affect how indepth uh it's going to

go about doing your research which is

very nice so here's the GitHub repo for

that guey right here and I'm going to

quickly show you how you can actually

set it up and self-host it before we run

some tests on it so you're simply going

to do the exact same thing I just went

over we're going to get clone this repo

right here then we're going to change

directory into open deep research and

then we're going to run npm install so

you can literally just copy all of this

if you don't know too much about

commands paste it in right here and then

run it I'm not going to do that because

I've actually already ran it but that's

what you're going to do once you do that

you're going to want to configure your

environment variables so I always like

opening it either in cursor or in uh VSS

code so you're going to run code dot or

cursor dot okay and then you'll see a.v.

example you can make a copy of that and

rename it to EnV and then you're going

to paste in your opening AI API key as

well as your fir craw API key now if you

don't have a fir craw API key simply go

to fir craw right here sign up get a API

key and paste that bad boy in now the

one downside personally is fire craw is

great but there is some limitations and

there are credits so if you're using

this a lot you may actually run out of

credits or have to actually pay for

credits so I personally usually prefer

using something like crawl for AI and

now at the moment there's no native

integration but I actually may look into

playing around with some of this and

seeing if I can maybe tweak some of this

and for for you guys and then do a video

on that so if you want me to do that and

see if I can get something ready for you

guys maybe let me know in the comments

down below now once you do that you're

simply just going to run npm runev so

it's the exact same as the you know CLI

version and it's going to start on Local

Host 3000 okay and boom now we have our

Local Host version right here we're

going to come back to this in just a

second when we start running our tests

all right so the last open source

project right now is web UI open source

and guys if you know of any other open

source deep research projects or whatnot

let me know in the comments down below

and I may do a video on it so um let's

go ahead and check out this repo here

all right so this project builds upon

the foundation of browser use I've done

a video on browser use it's essentially

like an open AI operator and you can

build out custom code within python to

use browser use it's really awesome

really cool project and it's made by the

same people who made that but web UI is

a nice gooey for that and it also has

deep research capabilities which is

really really cool this is a very

powerful tool so we're going to start by

cloning the repository and then changing

directory into web UI so exact same

process that we always do all right next

you're going to want to have UV

installed so this is for managing your

python environment so simply just go

here make sure you have UV installed and

then you're going to run in the command

line UV and then VV for virtual

environment and then python 3.11 make

sure you have python installed too if

you don't and then depending on what

system you're on whether it's Linux or

Windows or Mac you're going to run one

of the fulling commands so on Windows

you're either going to run this for the

command prompt or this for Powershell

right here um to activate your virtual

environment and then for macro Linux

you're just going to run this Command

right here okay all this is literally

Linked In the description within the

repo and if you're having trouble with

any of these installations check out my

individual video on that specific

project so you can actually get some

help with that and then you're simply

going to install the dependencies so

install python requirements like so run

this command and then you're going to

install playright this is all within the

same terminal and then you're going to

configure your environment variables

just like we did so I like opening it in

cursor or vs code so once you have that

open you're going to see e.v. example

you're going to make a clone of that and

you can add your open AI API key your

anthropic API key or Google API key you

have a lot more options than some of the

other Alternatives which makes me really

like this because you can really use

this with any model almost well really

any model cuz you have an own llama

endpoint right here so you can use it

with open source models as well and and

you can also change your open AI

endpoint so you could Point any provider

you want and actually use an open AI

operator or open AI deep research

alternative for free which is very very

nice and Powerful then once you got

those API Keys set up you're simply just

going to run this right here so python

web ui. and then the IP right here and

then the port right here so you can

simply just copy this and run it once

you run that Command right here it's

going to start everything up on Port

7788 Okay and like I mention I'm leaving

the docs in the description for browser

use so if you need any help you can

check out the docs right here it

explains every single thing all right so

now we have all the different ones that

we're going to be testing setup we have

browser use web UI right here which is

really nice we'll go over this in just a

second we have another wrapper which is

built upon the open deep research CLI

project and then we have open ai's very

own deep research the $200 a month plan

right here which we're going to be

testing and seeing the comparisons of

all three because of course A lot of

these different ones have from bells and

whistles but when it really comes down

to what matters it is the output and the

quality of that all right so we're going

to start with opening eyes deep research

so you'll see here the prompt is I'm

doing a YouTube video comparing the Deep

research of three different tools the

first tool is opening eyes new deep

research tool $200 per month but you

also get access to on1 Pro higher sore

limits and probably more openi deep

research and then giving the blog post

link the second and third are open

source alternatives to opening eyes deep

research the second one is web UI by

brows browser use so web UI by browser

use right here so a link to the repo a

link to browser use website right here

and mentioning that you can use nlm with

this project the third one is open

source deep research right here which is

a CLI tool and then linking to the

GitHub repo right here it uses fir crawl

linking to the fir crawl API docs right

here but someone made it gooey for this

project so you don't have to use the CLI

and it's called another rapper right

here open source deep research and

linking to the repo and they even have a

cloud version which is here and then

linking to the cloud version please do

it in-depth comparison between the

closed Source open AI deep research and

the two open source Alternatives and

weigh the pros and cons as to which one

my viewers should choose now when you're

using deep research you have the option

to select your models so typically

you're probably going to want to use

either 01 Pro or 03 mini High For This

example we're going to be using 03 mini

high now the reason we're using this is

because one it is super super super

powerful it's a lot faster than 01 Pro

and this is also going to be the same

model that we're going to be using to

test the other deep research tools so

we're giving it an even playing field

between the comparison of the output so

we'll go ahead and run this right here

and you can see that we get a few

different followup questions so it's

saying that's a great idea to make sure

we can do a useful and end up comparison

could you please clarify a few different

things here okay so I'm going to use

these uh followup questions for pretty

much all them so we can give every

single project the exact context for

their search so to go over these

follow-up questions we have evaluation

criteria so are there any specific

factors you'd like to focus on accuracy

speed easy use integration options cost

flexibility data privacy I said all the

above target audience is your target

audience more technical devs researchers

or general users I said both so have

both options but more devs and

researchers probably and then use cases

do you want to focus specific use cases

such as academic research market

analysis content creation or something

else we're going to go all the above and

then depth of comparison would you like

a simple pros and cons breakdown or a

detailed structure analysis with

performance benchmarks screenshots and

potential user testimonials I said

detail is possible but also have a

simple version and then preferred format

would you like a summary table in

addition to the written comparison I

said yes and also give unbiased opinions

based on what each specific user may

want to use for their specific use case

to get the best output okay now we're

going to go ahead and click Send here

and as you can see it's finally started

its deep research right here so as this

is going let's go ahead and start doing

the same thing for the other ones so

right now I'm on the another wrapper one

which is using the open deep research

CLI on the back end and we're going to

paste in that same prompt right here and

we're using 03 mini you could also use

some other models right here

unfortunately this one is limited it's

you're not going to be able to use like

o Lama or anything like that out of the

box you could probably tweak it but

unfortunately you can't use it out of

the box now the one cool thing with this

is we can adjust the breadth and the

depth for this search so by default it's

on four bread and two depth I'm going to

go ahead just for the sake of this test

and do six bread and three depth but you

know you can make this even more in

depth or less in- depth depending on

these settings right here now we're

going to go ahead and send this okay so

it's asking some different followup

questions I'm actually just going to go

ahead and paste in the same followup

questions we Ed for the open AI one just

cuz I want to give it the exact same

context and now we're going to go ahead

and press send and now it's going to say

starting in-depth research based on your

inputs so the cool thing with all these

like if we're looking here in the open

AI one you can see all the different

activities going on right here on the

right hand side so you can see it's

going to W combinator YouTube GitHub of

course and you can actually go through

and track some of the activity and see

how it's doing its reasoning as well as

it's researching and you can see all the

different sources right here that it's

compiling which is really cool and you

can do the same thing pretty much with

the open source versions too so we can

see it's doing uh different search

queries right here and then doing

different research finding different

results processing them and then

generating new learnings and then it's

going to take those learnings and then

be able to incorporate that into its new

research that it's doing right here so

as this one is going let's go ahead to

our final one which is browser uses web

UI version now I'm not going to go over

all the different settings here as you

can see there's a lot of different

configurations you can use you can do

agent types here maximum run steps so

just like how you can change different

configurations with the one we just s

for open source you can actually do the

same thing with browser use which is

really nice and as you can see here with

llm configuration we can select any llm

we want we're going to use open AI right

here and we're going to use 03 mini just

like we use with all of our other ones

but like I said you could use AMA

different ones like that and I'm going

to show you actually in future videos

how to use this with other models and

showing you the different outputs that

you can get using deep research with

maybe some open source models or with

some other closed Source models and I

really like this because we can actually

tweak a lot of different things like the

temperature um and all that good stuff

over here in browser settings you have

some different options right here so we

can use your existing browser keep

browser tabs open run browser without

guey so in headless mode um you'll see

once we actually start running this it's

actually going to open a browser if

we're not running it in headless mode

and you're going to actually be able to

see the agent do deep research within

different Chrome browsers so that's kind

of cool um and you can actually kind of

see like it's like a real person doing

research and you'll see the queries and

everything so I really like this you can

see the recording path Trace path agent

history this has a lot of settings so if

you're going to want to use this one I'd

recommend checking my individual video I

made on this where I go over all these

different settings here you can run

different tasks right here if you're

just using like open ai's operator

alternative so this will actually do

tasks we're going to use the Deep

research to do research so it's going to

use that AI agent to open the browser

and then take that information it's

going to scrape the the page and then be

able to do different queries and then

generate the report that way here you

see all the results information the

recordings will show up here and then if

you want to import or export a

configuration you can do that here so

I'm going to go ahead and paste in this

same exact prompt right here all right

so we're going to have the max search

iteration to three and the max query per

iteration to two and we're going to go

ahead and run deep research right now

and as you can see this one's a little

bit different when we actually run this

it's opening up two browsers on our

desktop okay and this is actually where

it's going to do the queries so we can

see open AI deep research verse web UI

versus open source deep research

features comparison and we can see the

same thing right here with a different

query in another browser if we look in

our terminal right here this is where

you'll actually be able to see the live

logs of what's going on so new memories

task progressions future plans summary

different actions etc etc all right so

while we are setting up web UI open deep

research as well as open AI deep

research are actually complete so the

one thing with this another raer right

here um you'll be able to see the logs

right here it isn't as in-depth as open

AI logs you can also go to the CLI right

here and you'll see the logs that are

how the CLI tool works and you can see

it's doing different searches having

different research routes Etc um the one

thing is it doesn't show the time from

what I can see within the guey which is

kind of not the best I wish it showed

how much time it did for its research

but if you go ahead to the uh CLI you'll

be able to see that it did a request

right right here for this time right

here which is

15632 seconds which is equivalent to 2

minutes and

3632 seconds now if we go ahead and look

at opening eyes deep research this one

actually went for 11 minutes now like I

showed you before you can change the

breadth and depth to make it you know a

little bit more in depth or a little bit

less in depth whereas open AI you can't

control that now with another wrapper

right here if you scroll to the bottom

well one thing you'll be able to see the

whole entire deep research right here

which is really nice in the UI but you

can also just download the report right

here so it we'll download a.txt file if

we go ahead and open this up you'll see

the whole entire research report in

markdown file which is really nice you

could feed this to another llm if you

want or do whatever you want with this

actual data now I went ahead and just

paste it in the research report right

here so we'll go over it briefly I won't

be able to go over every single thing

but you can see here in-depth comparison

between deep research tools you can see

a table of contents right here so we we

have our introduction um you know giving

an intro to all three tools right here

an overview of the tools so open AI deep

research browser use web UI so it's

going over things like the model the

cost the features the strengths

limitations we have browser use webui so

the model the cost the features

strengths limitations open source deep

research CLI and GUI going over the same

things right here and then evaluation

criteria so accuracy Speed and

Performance ease of use use integration

options right here cost and cost benefit

analysis flexibility and customization

data privacy and security and then

deployment models and use cases so

deployment modalities uh use cases right

here and then a summary table so we can

see here a full breakdown on a bird's

eye view with cost accuracy speed ease

of use integration flexibility data

privacy and then recommendations so here

with the another wrapper open deep

research for General users it actually

recommends open AI deep research and

then for developers and researchers it

recommends the open source Alternatives

and I don't know if I had 100% agree

because um you know General users may

not want to pay $200 a month all right

that's a huge factor for someone that's

generally using it a researcher uh or a

developer may actually want to pay that

money because it's an investment into

their most likely their job or their

business all right so pretty in-depth

right here as you can see it uses a

bunch of different sources here so it

actually pulled from 19 different

sources here now if we take a look at

open ai's deep research it actually

pulled from 27 different sources here

and it's very very long and intensive so

let's go ahead and paste this into our

dock also one thing to note the open

source deep research another rapper was

2,346 words and

18,957 characters and the open AI deep

research report was

16,336 words and

100,313 64 characters so it is a huge

report so we can see here the

alternative comparison a summary

comparison table right here that's going

over accuracy aspect speed it's going in

depth like it's pulling out different

Benchmark tests right here and getting a

lot of different data you can see the

speed the ease of use so very easy

moderate moderate um giving the direct

links right here which is nice

integration we can see a cost and for

each specific one it's going very in

depth for each specific point

flexibility right here data privacy

accuracy right here going over different

links and sources and I like how it

gives the sources directly in the report

instead of just giving them only at the

end and then speed here so more

different sources very very indepth like

I couldn't even go through this all of

course ease of use okay so integration

options cost each section is super huge

too like all these are very long

flexibility data privacy and security

and then pros and cons breakdown so open

AI close Source $200 a month Pros no

setup required and easy to use

integrated with chat GPT high quality

models thorough and reliable research

process citations and sources provided

maintenance and support FS very high

costs limited usage slow report response

time no customization or flexibility

closed ecosystem data privacy concern

required internet and public data a one

siiz fits-all model and then browser use

right here open source free and open

source multi llm support runs locally

browser automation capabilities parallel

processing and efficiency userfriendly

interface highly extensible integration

flexibility no fixed limits and then

cons requires technical setup resource

requirements maintenance overhead not as

polished as commercial product learning

curve for full utilization security

consideration dependent on external apis

potential to misuse if not careful and

then the open deep research CLI with

gooey rapper Pros free and open source

replicates opening eyes agent logic

adjustable parameters modern guey

parallel and efficient search citations

and markdown output self-host or privacy

fast adaption and Community Support

flexibility to integrate or modify lower

resource overhead and then cons initial

setup needed requires API Keys limited

built-in search choices potential rate

limits and cost surprises maturity and

stability less General than browser use

monitoring and intervention dual

dependencies open Ai and Fir crawl and

then documentation support is community

based then going over use cases based on

recommendations and then a summary table

of key differences and that's it for

that one that's very very long just as

we finish that off the web UI report is

done so we can go ahead and see this now

this one is much much smaller than the

other one

so if you want you can actually download

the report right here but you'll also be

able to access this in the folders right

here so if we go to our deep research

folder right here in our temp folder

you'll see this is our last query and we

can see the different query results

these are all the different scrapes that

it got for different sources and then

you can see the final report here you

can also see the record info right here

so here we actually got 1 2 3 four five

six we only got six sources I will say

guys I probably should have put it to

higher different search iterations and

Max queries I actually did do that

initially but I was getting an error are

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stride with AI agents so see I was

getting these failed uh tries right here

constantly so I just put it back to a

little bit lower and then it was working

so I don't know if that's just something

with me or maybe it's some issue that it

has okay now unfortunately at least as

to what I can see I don't see a spot

where you can see the specific time that

it was actually run for I know when it

was running it did show but now that it

is run I cannot find it anywhere and I

don't see it in the logs either I did

check and if you did have time stamps on

you can maybe see it but I will say that

it did take quite a long time it took

maybe I would just guess maybe about 5

minutes at least and it only generated

661 words and

4,967 characters so we can see here

comparative analysis overview of tools

uh comparative features so cost and

flexibility usability and setup

customization and control pros and cons

and conclusion now also too I will say

that this is the only one that didn't

ask me a follow-up question once I gave

it that initial prompt so if I was to

redo this again I would probably give

that prompt with the followup questions

so to make sure that you give all the

contexts out front you know think of the

different scenarios um because it won't

ask you a followup question

unfortunately so all in all just to run

over some of what I seen with doing

these tests webui does give a bit of a

shorter answer I found I mean you I did

have the lower settings on so maybe

giving it more settings it's going to

give a longer answer of course but me

personally within this test I say that

it probably got the maybe the worst

output but maybe if I did this test

again and gave it more of that initial

context that I gave in the follow

questions maybe it'll give a better

output it did get the least sources but

keep in mind it does have a lot of good

flexibility with being able to use

different models and test it so I'm

going to keep using it I think it's

still really good now open deep research

did a really good job with its report

it's it's in-depth I could have made it

longer if I gave more breadth and depth

but it did a really good comparison to

say the least and it cited you know a

bunch of different sources 19 different

sources which is really good and then

honestly I would have to give the win in

terms of the output here to opening eyes

deep research search I mean it's very

very indepth and I think it probably

maybe even like too in depth which is

good but you could also you know tone

down some of the prompt if you want to

get less information and honestly too

guys if you want to really get in-depth

nitty-gritty comparison you can go to

this Dock and read some of these

comparisons that these AI models

generate especially the open AI one it

is pretty damn good I think like I said

guys if you want access to this complete

document just join our free stri a

Academy I'll leave a link down below

where you can access it other than that

guys that's pretty much it for this

video let me know what your thoughts are

in the comments down below are you going

to be buying open AI deep research for

200 bucks a month or are you going to

use some of these open source

Alternatives I'm personally going to be

using both I like open source I really

love open source um one there's no

limits on these you know open AI deep

research only gives you 100 queries a

month yes it is really really powerful

and if you do have the funds and you're

someone that's serious about AI uh doing

research and whatnot it may definitely

be worth the investment me personally I

do not regret investing the $200 a month

to use this tool as well as the other

tools right now it is pretty damn

impressive but I also really like these

open source Alternatives and I'm going

to be doing different tweaks with some

of them testing out different ways to

use them with different LMS and whatnot

in future videos so make sure to stay

tuned for that let me know what your

favorite one is in the description down

below and guys if you just need a basic

deep research agent or something that is

really good but maybe you just don't

need to spend $200 a month then these

open source alternatives are very very

powerful nonetheless other than that

guys that's pretty much it for this

video on this in-depth comparison let me

know what your thoughts are in the

comments down below if you're new to the

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