Chatbot-Grok provides a modular AI Agent framework written in Python, designed to simplify development of conversational bots. It supports multi-turn dialogue management, retains chat memory across sessions, and allows users to define custom prompt templates. The architecture is extensible, letting developers integrate various LLMs including Grok, and connect to platforms such as Telegram or Slack. With clear code organization and plugin-friendly structure, Chatbot-Grok accelerates prototyping and deployment of production-ready chat assistants.

LangChain Chatbot for Customer Support leverages the LangChain framework and large language models to provide an intelligent conversational agent tailored for support scenarios. It integrates a vector store for storing and retrieving company-specific documents, ensuring accurate context-driven responses. The chatbot maintains multi-turn memory to handle follow-up questions naturally, and supports customizable prompt templates to align with brand tone. With built-in routines for API integration, users can connect to external systems like CRMs or knowledge bases. This open-source solution simplifies deploying a self-hosted support bot, enabling teams to reduce response times, standardize answers, and scale support operations without extensive AI expertise.