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Chatterbot

ChatterBot is an open-source Python library that builds conversational chatbots from collections of known conversations. It trains a dialog engine on corpus data and user interactions, matches new statements with learned statements, and returns likely associated responses.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Chatterbot — OpenViST project profile in Chat & Assistants.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use ChatterBot when a Python-based chatbot should learn from known conversation pairs and operate in a self-hosted environment. It is especially relevant when the training corpus can be curated for a specific support, FAQ or internal knowledge workflow.

Who it is for

  • Python developers building conversational chatbots.
  • Teams creating self-hosted support, FAQ or internal help-desk prototypes.
  • Builders working with curated or multilingual conversation corpora.

Documented uses

  • Train a chatbot from built-in or custom conversation data.
  • Select likely responses to user statements based on learned conversations.
  • Add chatbot response handling to a Python application.
  • Create language-specific conversational prototypes.
Before you choose

Requirements & limits

The repository is public, open source and licensed under BSD 3-Clause. The supplied installation example uses PyPI. No account, pricing or hosted-service requirement is documented.

  • Python is the documented implementation language and integration surface.
  • Training can use built-in English data, other included language data and custom corpus calls.
  • The response approach depends on the breadth and quality of known conversations.
  • The supplied example demonstrates Python object construction and get_response, not a specific web or hosted deployment.
  • The project is suited to self-hosted use across Linux, Windows and macOS environments.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train a chatbot with a built-in English corpus

Set up a ChatterBot instance, train it from the English corpus and request a response.

Before you start

  • Python environment with ChatterBot installed from PyPI.
  • Access to the ChatterBot Python package and its corpus utilities.

Steps

  1. Install the ChatterBot package with pip.

  2. Import ChatBot and ChatterBotCorpusTrainer.

  3. Create a ChatBot instance and a corpus trainer for it.

  4. Train the instance with chatterbot.corpus.english.

  5. Call get_response with an input statement.

Expected outcome

A trained ChatterBot instance can return a response to an input statement using the trained conversation data.

Guide limitations

  • The example does not document a web framework, hosted API or deployment architecture.
  • Response quality depends on the coverage and quality of the training conversations.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
2 October 2026
Original website title
gunthercox/ChatterBot · Official homepage title · retained in its source language
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