Home / DialogFlow / Bot Frameworks / Dialogflow vs Lex vs LUIS vs Watson vs Chatfuel
Bot Frameworks | DialogFlow

Dialogflow vs Lex vs LUIS vs Watson vs Chatfuel

First published: Oct 2018 | Last updated: Sep 2020

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.

September 2020 Update

Dialogflow has now released a completely revamped version called Dialogflow CX. The existing version has been renamed to Dialogflow ES (for Essentials). Note: Dialogflow CX is still in beta.

Not only does it have a much steeper learning curve for non-programmers, it is also more expensive. Plus it only makes sense for fairly complex chatbots. So why bother? 🙂

Here is why: it is also a complete departure from the way other chatbot frameworks operate (more on that in a later article), and fixes many annoying issues with Dialogflow ES. It also uses the latest Machine Learning algorithms from Google for its intent mapping. So how does that benefit you? If it is designed well, it will make your bot have much more intelligent multi-turn conversations, which have been pretty hard to achieve till date.

July 2020 Update

Considering that it has been a while since I first published this article, in my view, NLU-powered bot frameworks is turning into a two-horse race between Dialogflow and RASA NLU.

Consider the following:

  • Dialogflow is the easiest option if you are not a programmer, but want to participate fully in the bot building process. It is very easy to compose all kinds of conversational bots (see some examples) using Dialogflow’s visual dialog builder and existing flowchart tools
  • The other bot frameworks don’t have a comparable visual dialog builder. The lack of explicit contexts (described below) makes a big difference, in my opinion
  • RASA NLU, being open source, is a lot more customizable than Dialogflow. But it is also much harder for technical non-programmers to use. Still, it automatically becomes the best alternative if you need an open source bot framework for privacy and flexibility reasons

So the choice will likely boil down to this:

Are you looking for a low code option? Choose Dialogflow

Are you looking for maximum flexibility (and have a large budget to pay the associated costs of hiring developers who know NLU quite well)? Choose RASA NLU

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:

  • Intents
  • Entities
  • Explicit Contexts
  • Webhooks


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. [1] 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
  • This isn’t entirely true now, they have created a visual dialog builder (see comment below the article). There are still no explicit contexts in LUIS, which is not only useful for constructing conversation flows, but also important for narrowing down the candidate intent list. So I still don’t rate the LUIS very highly but if you are doing all your work in the MSFT stack, it is a much better option now.

Watson Assistant

  • 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.

What about RASA NLU?

RASA NLU is hard to compare to the list above, because

a) it is open source, and

b) theoretically, it can be modified to do whatever you want. This isn’t true of the other frameworks which are essentially running on other companies’ servers.

However, I mention in my article comparing RASA and Dialogflow that I think RASA NLU really missed a trick by not evolving their framework to allow for explicit contexts and wildcard entities.

Having said that, one of the huge benefits of RASA is that it is really flexible and you can sort of mold it to your requirements. But the lack of the features I have mentioned means that you need someone who has a solid background in Natural Language Processing (NLP) to actually utilize RASA to its fullest potential.

Dialogflow effectively reduces your risk by

a) providing a solid framework backed by one of the world’s largest companies (although I do wish their Dialogflow support was better)

b) allowing you to hire programmers who don’t have a background in NLP. It is fairly easy to prove to yourself that there are more programmers who don’t know NLP than those who do know NLP 🙂

I have written an eBook to explain these points as well as my views on Dialogflow vs RASA.


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.

[1] I found a similar article, also a very good one, which compares these frameworks here.

Related Posts

  • Microsoft have a visual bot builder and it’s very good. Power Virtual Agent which is part of Power Platform. Azure = for developers. Power Platform = Non Developers.

    Also they integrate into platforms like Dynamics for call centre capabilities natively.

    So yeah. You can’t write a blog on the best bot without understanding the landscape.

    • Microsoft Power Platform is basically a glorified scripted bot builder, basically an improved version of Chatfuel’s keyword based matching. At the end of the day, there is still no support for explicit contexts in Azure Bots, which means my view on Dialogflow being the superior bot building framework still stands.

      >>So yeah. You can’t write a blog on the best bot without understanding the landscape.

      This is a good example of a comment from someone who has barely any understanding of how Dialogflow works, what makes it unique, and how a simple Mindomo map is a far better visual bot builder which actually uses NLU under the hood to build the conversation flow. And the flowcharting technique itself is only possible because it makes explicit use of explicit contexts. 🙂

      In contrast, Power Platform uses hard coded values to route conversation branches, which is simply not as powerful as NLU based conversation branching.

      And lastly, you are also welcome to provide a link to a bot you built using MS Power Platform. This will allow my readers to decide for themselves whether I am correct about my assessment of competing bot frameworks.

  • Good article my friend.

    I just do not agree what you say about Bot Framework.

    This is true Bot Framework do not have a dialog designer, but the dialog infrastructure if the best.

    You can achieve advanced user interaction by using SDK features, and the channels connection will make the user interaction totally transparent.

    Another point is the channels integration. In bot Framework this is totally transparent. You can visually setup webhooks and keys, and this will create a channel with your app.

    Bot Framework is a very interesting option, using Node or C#, if you also need an advanced user interaction experience.

    • I don’t agree with your assessment at all. Certainly not after you consider that the MS Bot framework does not even have explicit contexts that you can set from the UI (you need to program them) as of this writing.

      If you would like to see how explicit contexts help you build different kinds of bots, you can take a look at the kind of bots I have created here. Not one of them required me to write any code. You don’t need to buy the course to understand how the bot works.

      Here is a very simple test of your assertion: If you create example bots such as the ones I have created and add links explaining how to create them on some GitHub repo, I am happy to link to your repo. Unless your definition of “dialog infrastructure” starts and ends with easy channel set up and doesn’t actually involve setting up easy to manage conversation flows (in which case we are not really talking about the same thing).

      At the same time, I think MSFT Bot Framework is an excellent choice if you cannot or don’t want to leave the MSFT ecosystem, that I can certainly agree.

  • Another platform to look at is Oracle Digital Assistant. It has a pretty decent user interface for development and a fairly simple SDK for custom backend logic/integration through Node.

    One of the main sticking points is that you can create a “Digital Assistant” that encompasses multiple bots. E.g. you could make a t-shirt sales bot along with a banking bot. From within the flow of purchasing a t-shirt you could say to check your balance, switch to the banking bot, then switch back.