I get this question a lot.
"How does Watson compare to Dialogflow?"
"What do you think of LUIS?"
So I am going to discuss my opinions in this article.
The first disclaimer is that I am clearly biased. I actually work with clients to help them out in their Dialogflow projects. At the same time, I am hoping this post starts a discussion amongst people and helps people do some homework before selecting the appropriate framework.
The second disclaimer is that I haven't had time to actually work on bots using the other frameworks, and used my knowledge of Dialogflow to consider what I feel are the important features. Then I watched a bunch of online tutorials to check how to implement those same features in the other frameworks. It is definitely possible I missed a thing or two during this process.
Chatfuel and ManyChat
Let us get something out of the way right away.
Chatfuel and ManyChat are not really NLU (Natural Language Understanding) based tools. They have some kind of rudimentary keyword matching, but once you get into features like entities and contexts, they fall quite short.
I have added them to this list mainly to point out this difference to folks because there are a lot of people who say "Chatbots are toys" because they have only ever been exposed to these two platforms.
There are a lot of challenges in building complex multi-step conversations in chatbots. So I am not claiming chatbots are "extremely powerful" tools yet. But they certainly aren't toys. In fact, some people have become tired of the word chatbot because it covers such a wide spectrum of capabilities and doesn't even represent anything specific anymore.
For example, a service which allows you to periodically broadcast messages to your Facebook followers may be very useful for you as a marketer, but
- it isn't really a chatbot even if the user interface is a chat app
- there isn't really anything "intelligent" about it
- you are confusing everybody when you claim that you "used a chatbot to increase your conversion rate by a gazillion percent!" (instead, simply say you used Chatfuel's sequences to improve your conversion rate)
So, can a Chatfuel bot do what a Dialogflow powered bot can? No
Is Chatfuel vs Dialogflow a valid or logical comparison? No
Does it mean you should avoid Chatfuel? No
But is Chatfuel a good choice for AI (NLU) bots? No
Are you confused yet? 🙂
What I am saying is, learn both the pros and cons before making your choices.
You should of course use all the tools at your disposal appropriately, but it is also important to learn a bit about what is going on under the hood.
Now back to the main topic.
So here is how I will evaluate the frameworks: I will categorize them based on their ability to provide the building blocks I have already used in Dialogflow.
Here are the major building blocks:
- Explicit Contexts
- Flexibility in Conversation Design
This is the video I followed for understanding how Amazon Lex works.
This is the video I watched for learning about LUIS and the bot framework.
Here is the playlist I watched to understand how IBM Watson Assistant works.
I believe all the frameworks are more or less equally capable in this particular feature.
All of them provide (somewhat) easy ways to define intents, as well as use multiple training phrases to make the intent recognition accuracy better.
While different frameworks may differ in their power when it comes to extracting entities, all of them have a concept of a developer entity and a system entity.
Here again, they are quite similar to each other.
Explicit contexts which have a lifespan
Contexts are used to keep track of what has happened in the conversation till the current moment. I think this is where the difference between the frameworks becomes quite stark.
I was looking for a word to express how contexts in Dialogflow are different from those in the other frameworks, and I came across this article.  It is a very good article, quite well researched, and defines the notion of "explicit contexts". Explicit contexts are defined right inside the visual editor (so you can see their names) . Also, explicit contexts can be used to guide the conversation flow so you can follow the conversation logic - said another way, you can start from a flowchart and use the help of explicit contexts to quickly turn it into a working chatbot.
You are probably familiar with how contexts work in Dialogflow (if not, read this article)
How do they work in the other frameworks?
- Lex has no contexts, but it has session variables which are used to store the state of the conversation, including user inputs from previous messages. (This isn't from the video. In fact I couldn't find any mention of contexts in Lex tutorials, and then learnt that they use the concept of session variables to save conversation state).
- Lex considers slot filling as the fundamental operation of chatbots. Unfortunately IMO, the other bot frameworks (including Dialogflow) seem to have copied this bad idea. Thankfully, in Dialogflow, there are ways around it.
- LUIS doesn't have a visual editor (that I am aware of). Microsoft seems to have skipped no-code and low-code and have created a only-code tool
- Contexts are thus declared and used from code
- From my understanding, there is no notion of context lifespan
- Watson Assistant uses a sort of linear ordering of the intents to provide priority to the different intents. This is definitely unique and different from the other frameworks
- Watson does have the notion of contexts, but it doesn't seem to have any way to specify the lifetime (lifespan) of a context.
- It doesn't seem like there is some way to actually use contexts themselves to guide the conversation (see for example my Dialogflow based decision tree bot)
In other words, the combination of explicitness of contexts and the ability to specify a context lifespan provides the bot builder with a lot more options in Dialogflow.
All the frameworks support the notion of webhooks, so I am not sure if there is much to choose between them.
Flexibility in Conversation Design
In my view, this is where Dialogflow's versatility really shows.
For example, I am not sure if any of the other frameworks in this list can build all the seven bot types I list here. I have built a functional prototype of each one using Dialogflow.
Bot Frameworks and Website Builders
To lighten up this article a bit, let us consider some analogies with the world of website builders. 
Like all analogies, this one is guaranteed to be mostly useless when taken to the extreme. 🙂
Chatfuel and ManyChat = Wix or SquareSpace
This one is a reasonable comparison. They let you get started very quickly, and have something to show for your efforts, but you will also run into their limitations quite quickly.
Lex = Shopify
With the full power of Amazon's eCommerce focused business, Lex lets you create skills which are very appropriate in the context of shopping. Just like Shopify is an extremely powerful platform for building online stores.
LUIS = Sharepoint
You CAN use Sharepoint to build a website, but you probably shouldn't.
On the other hand, if you run entirely on the Windows stack, and C# is the only language your dev team uses, and you already have plenty of experience with Sharepoint (aka you are a proper enterprise business), then you might be able to do a good job of building an intranet site using Sharepoint. If all these things are true, LUIS might well be the best tool for your chatbot too. Also, of all the bot frameworks, LUIS works best for Skype bots, from what I hear.
Watson = WordPress.com
It looks like Watson is a pretty solid option, but they also seem to prescribe very set ways of achieving your goals (e.g creating multi-step conversations). Much like WordPress.com 🙂
Dialogflow = WordPress.org
A lot of people dislike WordPress.org (the self hosted version of WordPress), and there are a lot of people who just want PHP or WordPress (or both) to die. But it just keeps going on, gaining more and more marketshare. Why? Because of the plugin ecosystem - which allow you to extend it however you wish, sometimes beyond all reasonable limits.
Dialogflow (in my biased view of course) is the most flexible of these frameworks when you look at it in the context of bot ecosystems.
- It has single-click integrations with many other channels like FB Messenger
- It is probably the most powerful when it comes to multi-lingual capabilities
- As I have already discussed, it is quite versatile when it comes to conversation design
- I hear, but can't prove, that is has the most powerful NLU capabilities
So whatever type of bot you are trying to build, there is probably a way to get it to work in Dialogflow, and often with less work than the other frameworks.
So there's my prediction.
What do you think? Have you already taken out your pitch-fork in defense of your favorite framework? Let me know why I am wrong.
Also, it would be great if you can point me to examples of how to build the 7 types of bots I mentioned here in the different frameworks. I think all of us can get a much better understanding of these frameworks by seeing these tutorials. I found a similar article, also a very good one, which compares these frameworks here.  If nothing else, we can all revisit this post a year later and have a hearty laugh at how wrong it went.
- Reader Question: What if a specific system entity isn’t available in all languages in a multi-lingual bot?
- How much can Machine Learning ACTUALLY help with answering free-form questions?
- Dialogflow Toolkit vs MBD Membership
- Dialogflow Knowledge Connector : Pros and Cons
- How to integrate Telegram with Dialogflow
- Why I avoid using slot filling (required parameters) in Dialogflow
- Free Tool: Convert your WordPress website into a Dialogflow FAQ chatbot
- Dialogflow Mega Agent Tutorial
- Reader Question: Will followup events work when an intent has an input context?
- How to learn Dialogflow in a week