There are different methods a developer can build chatbots. You can use either bot builder platform such as Chatfuel or use advanced bot development frameworks. Chatbot development frameworks allow the developers to customize the chatbot in whatever the way to suit the business requirement.
Frameworks help you develop better and faster! Frameworks are structured, maintainable and upgradable. It allows developers to save time by re-using components or modules in order to focus on implementing business logic.
Here we analyzed 5 top chatbot development frameworks available for the developers to build production-ready chatbots. In fact, each of the frameworks has its own advantages and disadvantages, there are a few points you have to keep in mind while choosing a framework when building production-ready chatbots. Read them below.
Even though there are many frameworks, most of the chatbot development agencies and developers use the 5 frameworks mentioned above. These are the frameworks are matured, well structured, tested and used by 1000s of chatbot developers.
Microsoft offers the Azure Bot Service which provides an integrated environment that is built for chatbot development. It allows the developers to speeds up the development. Azure Bot Service has two components - Microsoft Bot Framework connectors and BotBuilder SDKs.
Microsoft Bot Framework connectors allow you to deploy chatbots on websites, apps, Cortana, Microsoft Teams, Skype, Slack, Facebook Messenger and more. Azure categorize the messaging channels into two - Standard channel (Skype, Cortana, Microsoft Teams, Facebook, Slack) and Premium channels (DirectLine and Web Chat clients).
Standard channel support unlimited messages while Premium channels give you free 10,000 messages/month. You can host the chatbot in either Azure or your own server.
From our experience, Microsoft Bot Framework connectors and Bot Builder SDK are the best solutions to consider when you are developing an omnichannel chatbot. It's very easy for the developers to connect the Bot Builder SDK with any Natural Language Understanding (NLU) services. Bot Builder SDK Github account has many code samples and templates which help the developers to get started the chatbot development quickly.
Botkit is one of the leading open source chatbot development frameworks. According to botkit.ai 10,000's of bots are built using Botkit framework.
Botkit supports major messaging channels such as Facebook, Slack, Microsoft Teams, SMS etc. One of the important features of Botkit is supporting middlewares (or plugins). Middlewares can add to the core bot running processes and make changes both incoming and outgoing messages. For example 'Natural Language Processing' plugin allows the developers to integrate the NLU services such as Microsoft Luis, Amazon Lex, Google Dialogflow, IBM Watson to chatbot by installing specific plugins.
Botkit is a Node.js module and works with Node and NPM.
Dialogflow is a conversational platform that lets you design and builds chatbots and voice apps (Google Actions and Amazon Alexa Skills). Dialogflow is backed by Google and is the most widely used tool to build Actions for more than 400M+ Google Assistant devices. Dialogflow is adding more and more new features and is becoming the leading chatbot development tool.
The major difference between Dialogflow and other frameworks we discussed (Botkit and Microsoft Bot Builder SDK) is, Dialogflow itself is a fully-featured bot builder platform which provides UI for the channels integration, NLU services and has an integrated editor powered by Cloud for coding.
Dialogflow allows us to deploy chatbot to 14 platforms. It supports all the major messaging channels such as Facebook Messenger, Slack, Skype, Kik, Line, Telegram, Twitter, Viber.
When comes to pricing, Dialogflow comes in 2 editions. Standard Edition is free and covers the need of most developers, while Enterprise Edition offers paid enterprise support in the form of Pay as you go. Bot editions provide most of the same features but interactions are limited by usage quotas. Standard Edition allows you 180 requests per minute while the Enterprise Edition supports 600 requests per minute.
So you can build a complete enterprise production-ready chatbot using the Dialogflow.
Botpress is another leading open-source platform for building and managing chatbots. The best part of Botpress is it provides UI where developers and non-technical people can manage the chatbots after the deployment.
Botpress has many nice features such as The Flow Builder and Dialog Manager. Flow Builder and Dialog Manager make it easier for developers to build and debug complex conversation flows. The developers can fully customize the chatbot - add business logic or integrate the 3rd party APIs etc. Botpress is a good choice if your client wants to manage the chatbot contents by non-technical people after the deployment.
With Botpress you can deploy chatbot on Facebook, Slack, Telegram, BotFramework, Twilio, Web.
Botpress is available under both the AGPL license and the Botpress Proprietary License
5. Rasa Stack
Rasa is an open source chatbot development frameworks. It has two major components Rasa NLU and Rasa Core. Rasa NLU is responsible for natural language understanding. Rasa core is a framework for building conversational chatbot. Rasa core allows more sophisticated dialogue, trained using interactive and supervised machine learning.
The major advantage of using Rasa Stack should be the chatbot can be deployed on your own server by keeping all the components in-house. It is possible to use Rasa Core or Rasa NLU separately. Rasa is production ready and used in large companies everywhere.
Rasa core support Facebook Messenger, Rocket.Chat, Slack, Telegram, Twilio.
Rasa is available under two license. Rasa NLU and Rasa Core are open sources. There is a paid and more advanced version of Rasa stack called Rasa platform. Rasa Platform extends the open source Rasa NLU and Rasa Core libraries with APIs, a graphical user interface, and customer success program which includes enterprise-grade support.
Rasa core is written in Python.