10 Best Chatbot Development Frameworks You Must be Aware in 2024

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85% of the large and medium businesses are expected to have chatbot automation by 2025. Interactive applications that engage customers through meaningful dialogue, chatbots are the way forward for companies which intend to beautify their customer service, marketing and lead generation operations. The choice of Chatbot builder can be a game-changer when it comes to creating chatbots for your business. Businesses profit from personalized marketing thanks to Chatbots that make one of the few communication channels to offer a genuine one-to-one experience between brands and users.

What is a chatbot framework?

A chatbot framework is a structured set of tools, libraries, and guidelines that developers use to build and deploy chatbots efficiently. It provides a foundation for creating interactive conversational agents that can understand and respond to user inputs in natural language. These frameworks often include pre-built components for tasks like natural language processing (NLP), dialogue management, and integration with various messaging platforms.

Chatbot frameworks streamline the development process by offering reusable code and standardized methods for handling common functionalities. They abstract away the complexities of natural language understanding and communication, allowing developers to focus on customizing the chatbot’s behavior and integrating it with specific applications or services.

Popular chatbot frameworks include Microsoft Bot Framework, Google’s Dialogflow, and Rasa. These frameworks support the creation of chatbots for diverse purposes, ranging from customer support and information retrieval to entertainment and task automation. By leveraging a chatbot framework, developers can accelerate the development cycle, enhance the scalability of their chatbot applications, and ensure a more consistent and robust user experience.

How chatbot frameworks are different from chatbot platforms?

Chatbot frameworks and chatbot platforms serve distinct purposes in the development and deployment of chatbots. Chatbot frameworks provide a foundation for developers to build customized chatbots with greater control over functionality, requiring coding expertise. They offer flexibility for tailored solutions. In contrast, chatbot platforms are end-to-end solutions, offering a user-friendly interface and pre-built components for easier development without extensive coding. Platforms are suitable for individuals with varying technical expertise and provide a more accessible approach to chatbot creation and deployment. Remember if you do not want to get your hands dirty building a chatbot a chatbot framework is not for you.

What constitutes a typical chatbot architecture?

A chatbot architecture is a structured framework that defines the key components and their interactions, orchestrating the functionality of a conversational agent. The user interface layer serves as the entry point for user interactions, while messaging platform integration facilitates communication. Natural Language Processing (NLP) is pivotal, enabling the chatbot to comprehend and respond to user inputs in a human-like manner. Dialogue management governs the conversation flow and context maintenance, guiding the chatbot’s responses.

The architecture incorporates a knowledge base or backend integration layer for data retrieval, business logic for task execution, and memory/context management to sustain coherent interactions. Security measures safeguard sensitive information, and analytics/logging components track performance and user behavior. The deployment environment determines where the chatbot runs, whether on a cloud platform or on-premises servers.

This holistic structure ensures that a chatbot can effectively understand user queries, retrieve relevant information, and execute actions while maintaining context and security. The diverse layers collaboratively contribute to the seamless functioning of the chatbot, providing a user-friendly and efficient conversational experience across various platforms and use cases. The architecture decides the capabilities of a chatbot framework and how flexible it would be.


Chatbot Architecture by IBM

Here are the Top Chatbot Development Frameworks You Must Consider

Microsoft Bot Framework

Designed exclusively to interact, talk, listen, and communicate with your customers, Microsoft Bot Framework builds phenomenal frameworks. This AI chatbot platform comes with the ability to integrate with the most popular applications offered by Microsoft suite like Cortana, office 365, and so on.

Businesses can use Microsoft Bot framework and train chatbots using the existing conversation and azure cognitive service. Chatbots can understand people’s communication through text, SMS, video, and speech. It deploys active learning and includes pre-existing, pre-build models that allow chatbots to interact with users on chat programs they’re already using, such as Skype, Slack, Facebook Messenger, Cortana, Microsoft Teams, Kik, and more. The open-source SDK allows businesses to test chatbot products even before it is deployed into a channel. Powered by A.I. and machine learning, Microsoft Bot based chatbots can also reply to the most complicated questions asked by the visitors. Build, connect, publish, and manage smarter chatbots with Microsoft Bots Framework.


Wit.ai is a free and open-source Natural Language Processing API that businesses use to create text-based and voice-based bots. These chatbots can be integrated on all kinds of the messaging platform. The framework supports almost any languages spoken all over the world.

It uses various Machine learning algorithms to extract meaningful information. Wit.ai learns from human language when any interaction takes place. Businesses are leveraging this API in mobile apps, home automation, wearable devices and hardware. Wit.ai makes great chatbots for applications, which run mainly on mobile screen or tiny devices like wearables, by activating voice interface. SDK is available for Node.js, Python and Ruby.

Wit.ai is available via GitHub. If you list down top 10 open source chatbot frameworks there is no way wit.ai will not make it to that list.

Dialog Flow

Google owned Dialog Flow is a chatbot framework with unique voice navigating features. Businesses can use DialogFlow to digitize business processes to save time and money that goes into hiring expert community managers.

This framework uses Speech-to-text and natural language conversations to facilitate automated human-computer interaction. Dailog Flow framework leverages Google cloud architecture and AI-powered sophisticated system to convert speech into text. Google also used big data to understand what users are saying and respond accordingly. The framework comes with an Inline code editor that makes it easy for everyone to integrate multi-functional intelligent chatbots into their systems. Users can interact with brands through the website, on Google Assistant, Alex, Facebook Messenger, and other platforms, when the chatbot is built using Dialog Flow.

New customers get who sign up with Dialog Flow get $300 in free credits. Isn’t that great?

I.B.M. Watson

I.B.M. Watson is an outcome of I.B.M.’s DeepQA project. The prolific chatbot framework uses the neural network to respond with naturally processed replies. The framework offers pre-trained and pre-integrated architecture which makes it easier to deploy. I.B.M. Watson can help businesses build chatbots that facilitate better service, both internally and externally.

The framework is extensively used to develop chatbots for healthcare units which can actively take patient data and identifies potential diseases using the power of natural language processing. Chatbots build on I.B.M. Watson framework can even help doctors prescribe proper treatments and medicines. It is primarily designed to work as a question-answering system with dynamic dialogue flow. Businesses also leverage their distinct capabilities to retrieve information and essential data.


It is a popular open-source chatbot development framework that offers businesses tools to develop, launch and iterate their chatbots with ease. It is an AIaaS platform that uses the Artificial intelligence markup language and also includes The Artificial Linguistic Internet Computer Entity (A.L.I.C.E.)

Businesses use Pandorabots to build intelligent chatbots for businesses and third-party applications. With this framework, companies can reach the maximum number of people by leveraging two-way communication in their preferred channels. Pandorabots’ extensible SDK covers all the tools a chatbot developer can need to create, launch, and emphasize a chatbot. Pandorabots framework uses Artificial Intelligence Modelling Language, which is an XML-based language. It also leverages Artificial Linguistic Internet Computer Entity to process human language naturally. Pandorabots supports static images and GIFs. The framework comes with SDKs for Java, Node.js, Python, Ruby, PHP and Go. Machine learning capabilities of the chatbots developed with the help of Pandorabots help drive leads to your business from various messaging platforms. Add to it Pandorabots’ artificial intelligent XML markup language for visualization and creating human interfaces that keep conversations simple & easily understandable.

Pandorabot chatbot builder has been used to develop chatbots for voice interfaces, eCommerce, customer service, marketing and more, in the past. While the basic version is offered free of cost, the company provides multi-dimensional pricing plans for businesses with advanced requirements. 


A dual-license open-source chatbot framework platform, Botpress is built using a modular blueprint.

With Botpress, businesses can cut-off pieces off and add new bits on an existing code frame for chatbots. It promises a developer-friendly environment through an intuitive dashboard and its flexible technology. Botpress framework runs on a three-stage installation process. First, the developers start building the bot, then they deploy it to their preferred platform and thirdly, they hand-off access so that it can be efficiently managed. Businesses can build chatbots locally and use their favourite cloud hosting.

BotPress framework runs on-premise, and the business enjoys full control over the data that comes in and out of the chatbots. Chatbot developers can fully customize the chatbot to add business logic or integrate the 3rd party APIs etc. with Botpress. Botpress comes with several pre-installed components like an N.L.U. Engine, an administration dashboard and a visual flow editor. Superior features of Botpress framework like the Flow Builder and Dialog Manager facilitate building and debugging of complex conversation flows. Thus, it offers vivid functionalities like chat emulator/debugger and support for multiple messaging channels in a chatbot.

The framework is available under both the A.G.P.L. license and the Botpress Proprietary License. Botpress also has ready integrations with popular industry tools which helps you integrate the chatbot framework quickly with your platform. 


Botkit is a platform for a community of more than 7,000 developers from around the world. Another open-source chatbot building tool, Botkit features integrated Natural Language Processing from LUIS.ai. It is one of the best known open source chatbot frameworks out there.

It opens up dozens of plugins and open source libraries for chatbot developers. Botkit also features a visual conversation builder. The framework also features built-in stats and metrics — Botkit with various tools including Slack, I.B.M. Watson, Glitch and Heroku. Botkit.ai allows developers to add plugins as per the needs of the business.

Botkit has a free version and paid versions starts from $5/month, the cost varies depending on how many bots and active users a business interacts with.

RASA Stack

A well know name among the open source chatbot frameworks, RASA Stack comprises of two major components Rasa N.L.U. and Rasa Core. It is a refined set of open-source machine learning tools, that can be used to create chatbots and assistants. With RASA NLU, chatbots can understand natural language, and with RASA core it gets conversational functionalities. Thanks to the interactive and supervised machine learning RASA core chatbots can indulge in a sophisticated dialogue with the end-user.

With RASA Stack, the chatbot can be deployed on the server of the business. Businesses which prefer to keep all the components in-house, opt for RASA Stack. Being an independent service, all the data fed to the framework or received by it, don’t need to run through a third-party API. Chatbots built with RASA Stack can perform contextual dialogues, recognize user intent and even exact entities.

The framework is production-ready. A paid and functionally advanced version of RASA Stack is also available in the name of RASA Platform.


Powered by machine learning, ChatterBot uses a python library to automate responses of frequently asked questions put forth by the customers. The framework eases the workload of developers and simplifies the process of building conversational chatbots.

The end products are language independent and can quickly learn any language. ChatterBot framework allows chatbots to slowly pick up learning after its deployment, which contributes to the accuracy and speed of the responses over time. Each interaction with an end customer allows Chatbots to gain knowledge and improve its performance of producing replies, thanks to machine learning algorithms. Leveraging the A.I. tech of the ChatterBot framework, chatbots can generate random but accurate answers even for the same type of question. Because the framework stores and manipulates data, it can search the closest statements that match the question pattern while stimulating responses to the customers. Open source chatbot frameworks are used widely within the industry and chatterbot is a name many big companies are associated with.

This python driven framework churns out sophisticated and dynamic chatbots for businesses.

Amazon Lex Framework

Amazon is known to build solid products so when they launched their chatbot framework everyone took notice. This is because Lex allows you to create conversational interfaces for applications using voice and text and more importantly it uses the same conversational engine that powers Amazon Alexa. What differentiates it from other chatbot frameworks is that it offers some pre-built bot templates that automates and standardize experiences. Some of these templates are for basic and most popular tasks including conversation flows and dialog prompts. Lex chatbot framework supports both voice and text-based interactions, making it versatile for applications such as chatbots, voice-controlled applications, and interactive voice response (IVR) systems.

Since this is backed and hosted on the reliable AWS the users do not have to worry on scaling or downtime. As an AWS service, Lex benefits from the scalability, reliability, and security features of the AWS cloud infrastructure. It can handle varying levels of demand and is designed to ensure data privacy and security. This is very it beats other chatbot frameworks hands down. Developers can build and deploy chatbots using the Amazon Lex console or programmatically using the Lex API. Amazon Lex is commonly used in applications such as customer support bots, virtual assistants, and information retrieval systems, offering a comprehensive solution for building conversational interfaces within the AWS ecosystem.

Chatbot framework or AI chatbot framework: Where are we heading?

We are heading towards an integration of both chatbot frameworks and AI-driven capabilities, converging to create more powerful and intelligent conversational agents. While conventional chatbot frameworks provide the foundational structure for building interactive agents, the future lies in augmenting these frameworks with advanced artificial intelligence (AI) technologies.

AI chatbot frameworks are evolving to encompass sophisticated natural language processing (NLP), machine learning, and contextual understanding, enabling chatbots to comprehend user inputs with greater accuracy and respond in a more nuanced, human-like manner. This integration allows for improved adaptability to user preferences, enhanced personalization, and a more dynamic dialogue management.

The trajectory is toward comprehensive solutions that leverage AI chatbot frameworks, offering not only structured conversation flows but also the ability to learn from user interactions, adapt to evolving contexts, and deliver a more intuitive and seamless user experience. As AI continues to advance, the future of chatbots involves a synergistic relationship where the framework provides structure, and AI imparts intelligence, collectively contributing to more sophisticated, context-aware, and versatile conversational interfaces across various applications and industries.


With time, Chatbots frameworks are becoming an integral part of each business and thus customer experience. Businesses which decide to leverage chatbot tech into their customer service, marketing and lead generation operations, stand a chance to ace the race. If you are startup or a small business it makes sense to use anyone of the open source chatbot frameworks.

Businesses often count on multi-functional chatbot frameworks that help build outperforming chatbots that help them gain a competitive advantage and digitize the processes. Choosing the right chatbot framework opens up thousands of possibilities to achieve a business goal. While there is no ‘the’ perfect chatbot development framework, the choice needs to be based on the particular requirements of the business. Hope this precise list of Top Chatbot development frameworks helps you in making the right choice.

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Rahul Singh

Sr. App Developer

Rahul has been associated with the apps industry for more than 9 years now. He has seen the apps economy grow from its nascent days to a full fledged industry with its complete ecosystem as of today. His interest lies in pursuing and getting to know the best app development technologies, processes and platforms. He is truly an app enthusiast. In his free time he loves playing console games and reading history.

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