What is an open-source chatbot, and why is it beneficial for developers?

An open-source chatbot is a conversational agent whose source code is publicly available, allowing full control over chatbot logic and customization options. Developers can leverage natural language understanding (NLU), integrate with multiple messaging platforms, and adapt the bot's framework and programming languages for rapid development and enterprise scale.

How do open source frameworks like Rasa or Microsoft Bot Framework help build conversational user interfaces across multiple platforms?

Tools such as the Rasa platform or Microsoft Bot Framework provide dialogue management, NLP tasks, and out-of-the-box connections to messaging apps (e.g., Facebook Messenger, Google Assistant) and can be easily integrated into your website. These open-source frameworks offer step-by-step instructions and boilerplate code to create chatbots that can be deployed on multiple messaging platforms or hosted on-prem or in the cloud.

What advantages do visual conversation builders offer for creating AI chatbots?

A visual conversation builder lets developers design conversational user interfaces without heavy coding, accelerating chatbot development. Using drag-and-drop building blocks, you can easily structure chat flows and manage messaging channels, making it a user-friendly way to build chatbots for specific tasks or rapid development projects on various websites.

How do I choose the right open source platform for building bots with deep learning and conversational AI?

When selecting an open source platform, like Rasa, Claudia Bot Builder, or a framework-agnostic solution, consider AI provider integrations, deep learning capabilities for natural language processing, support for multiple languages, and resources for a framework for building AI assistants. Look for robust documentation, an active community, and tools that align with your project scope, data requirements, and the messaging services you plan to connect and deploy.

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Top 20 Open Source Chatbot Frameworks

Cem Dilmegani

updated on Jul 24, 2025

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An open-source chatbot framework provides the source code publicly, allowing anyone to use, modify, customize, and distribute it freely.

We compiled a list of the top 20 open-source chatbot platforms while highlighting their key and differentiating features.

Top 20 open source chatbot platforms

Each platform listed here is an open-source project, allowing developers to customize and improve the chatbot according to their needs.

Tool	Most important feature	Licensing	Stars on GitHub
Flowise	Low-code platform building	Apache-2.0	37.1k
spaCy	Advanced NLP use to enhance chat understanding	MIT	31.4k
Rasa	End-to-end chatbot development	Apache-2.0	19.9k
ChatterBot	Quick prototyping	BSD-3-Clause	14.3k
Botpress	Visual, modular chatbot building	AGPL-3.0	13.5k
Botkit	Creating javaScript-based framework	MIT	11.6k
Chainlit	Minimal setup	Apache-2.0	9.2k
typebot	No-code chatbot generation	Apache-2.0	8.4k
Microsoft Bot Framework	Multi-platform chatbot deployment	MIT	7,6k
DeepPavlov	Research grade conversational AI	Apache-2.0	6,8k

*GitHub star counts are approximate as of April 2025.

**The table is sorted according to the number of stars vendors gained on Github.

5 Distinguishing features of some chatbot frameworks

1. Visual workflow for LLMs

Flowise offers a visual interface to chain language model prompts and design conversation flows.

Figure 1. Flowise’s example for visual workflow.¹

2. Modular & visual interface

Botpress has a rich admin UI for designing and visualizing conversation flows, making it accessible for enterprises.

Video explaining Botpress Studio’s interface guide.

3. Middleware extensibility

Botkit’s middleware architecture allows you to incorporate custom processes, such as logging, authentication, or adding extra data, enabling the bot to manage events and messages flexibly across various chat platforms and in a highly customizable manner.

4. User interface for large language model (LLM) interactions

Chainlit helps visualize and manage the flow of conversations when integrating large language models.

Figure 2. Chainlit’s interface²

5. Serverless optimization

The Claudia bot builder is specifically designed to utilize AWS Lambda, minimizing infrastructure management while automatically scaling with demand, making it easier to create bots. ³

Common features of open‑source chatbots

While each chatbot framework has its own distinct capabilities, there are certain features we expect all chatbot frameworks to include. The most significant common features of open-source chatbots are listed below.

1. Deployment flexibility

Nearly every framework allows you to deploy locally and in the cloud. They often support containerization (via Docker or Kubernetes) or serverless architectures, allowing you to choose the infrastructure that best fits your organization’s needs depending on your scalability and security requirements.

2. Multichannel integration capabilities

Open-source chatbots typically offer built-in connectors and APIs that make it easy to integrate with the messaging platforms your enterprise already uses, such as Slack, Facebook Messenger, or Telegram. They also support custom channels through RESTful APIs and messaging APIs, enabling smooth integration with existing CRM, ERP, or other business systems.

3. Customizability

Their open-source nature gives you full access to the code, allowing you to modify it to meet your specific needs, adding custom business logic, adapting conversation flows, or extending functionalities. Many frameworks support the creation of custom modules, enabling you to build advanced features such as custom NLU models or integrated analytics dashboards.

4. Natural language understanding (NLU) capabilities

Many open-source chatbots offer built-in natural language processing (NLP) capabilities or can integrate with advanced third-party NLU engines. These tools support key functions such as intent recognition, entity extraction, and contextual conversation flow management.

Why do businesses choose open-source chatbots?

Open-source chatbots provide various strategic advantages. They primarily enable extensive customization optimized to specific business needs. Enterprises reap the benefits of strong community support, regular updates, and integration capabilities with existing software systems. With control over the source code, businesses can avoid vendor lock-in, ensuring long-term operational flexibility. With these advantages, companies can build bots suited to their specific needs, providing a high level of customization and control over the development process.

If you decide to use an open-source chatbot platform for your enterprise, refer to chatbot architecture to understand how chatbots work and how to create your chatbot effectively. Additionally, you can check out the chatbot training data guide to find a suitable dataset for your training. After implementing your chatbot, the next step is to test it. You can refer to our chatbot testing guide to understand the necessary steps and precautions.

Optimizing your open-source chatbot platform

Regular training and updates enhance chatbot effectiveness by consistently updating its training datasets, refining NLP models, and incorporating new conversational scenarios based on real-time user interactions and evolving business goals. Regular training and updates are essential, and using the right tool can streamline this process, ensuring that your chatbot remains effective and up-to-date.
User feedback integration focuses on feedback to drive chatbot improvements, ensuring enhancements meet user needs and preferences.
Comprehensive documentation covers chatbot functionalities, integration processes, and troubleshooting guidelines, supporting effective management and ongoing optimization efforts.

Open source LLM alternatives

If you’re seeking to enhance your chatbot with an open‑source language model, consider these popular choices, each backed by permissive licenses, active communities, and readily available checkpoints:

GPT-NeoX-20B (EleutherAI): This model features 20 billion parameters and is trained on the comprehensive Pile dataset. It delivers robust general‑purpose performance and can be fine‑tuned for conversational applications under the Apache 2.0 license.
BLOOM (BigScience): BLOOM comes in parameter sizes ranging from 560 million to 176 billion and supports 46 languages, released under the RAIL license. Its modular design facilitates easy deployment and extension.
MPT-7B (MosaicML): With 7 billion parameters, this decoder‑only model boasts efficient inference optimizations. The Business and Community licenses permit commercial and research usage, respectively.
RedPajama-INCITE (Together): This collection of trained checkpoints mirrors LLaMA’s data, offering up to 7 billion parameters and released under Apache 2.0. It is perfect for those prioritizing transparency in training data and methodologies.
Alpaca & Vicuna: These are refined versions of LLaMA‑style models that are specifically enhanced for chat applications. Unlike typical interpretations of “open source, ” LLaMA is only available under Meta’s Community License. While you can access the model weights and code, there are restrictions on usage (limited to non‑commercial research for v1 and specific commercial conditions in subsequent versions), as it does not operate under a standard OSI‑approved open‑source license. Although they adhere to the licensing conditions of LLaMA, all model weights and instruction‑tuning recipes can be found openly within Alpaca and Vicuna.
OpenAssistant (LAION): End-to-end trained on open instruction datasets, this model (up to 20 billion parameters) operates under a custom MIT‑style license, focusing on safe and aligned conversational AI.
DeepSeek LLM (DeepSeek AI): The inference code is available under the MIT license (fully permissive), while the model weights can be downloaded publicly under a custom open‑model license that permits commercial use. However, the training data and complete training code are not available, meaning it is not “fully open source” by strict definitions.

All these models can be self‑hosted or utilized through popular frameworks like Hugging Face Transformers and Ollama, allowing complete control over customization, data privacy, and cost.

FAQ

Reference Links

Flowise - Low code LLM Apps Builder

Overview - Chainlit

Claudia Bot Builder

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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