💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Universal memory layer for AI Agents. It provides scalable, extensible, and interoperable memory storage and retrieval to streamline AI agent state management for next-generation autonomous systems.

Rasa Core is now part of the Rasa repo: An open source machine learning framework to automate text-and voice-based conversations

CakeChat: Emotional Generative Dialog System

This repo contains implementation of different architectures for emotion recognition in conversations.

τ-Bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Welcome to the Bot Framework Solutions repository which is the home for a set of templates and solutions to help build advanced conversational experiences using Azure Bot Service and Bot Framework. Microsoft Bot Framework is a comprehensive framework for building enterprise-grade conversational AI experiences.

Enterprise-grade open source GUI platform for Rasa teams

An open source Ruby framework for text and voice chatbots. 🤖

Dialogflow Web Integration. Supports rich components

Attention-based multimodal fusion for sentiment analysis

This repository contains a new generative model of chatbot based on seq2seq modeling.

RASA chatbot use case boilerplate

Agentic AI platform that harnesses Visual LLM Chaining to build proactive digital assistants

Kotlin framework for conversational voice assistants and chatbots development

IssuesにNLP（自然言語処理）に関連するの論文を読んだまとめを書いています．雑です．🚧 マークは編集中の論文です（事実上放置のものも多いです）．🍡 マークは概要のみ書いてます（早く見れる的な意味で団子）．

Explore LangChain and build powerful chatbots that interact with your own data. Gain insights into document loading, splitting, retrieval, question answering, and more.

DialogLab is an authoring tool for configuring and running Human-AI multi-party conversations.

Overview of venues, research themes and datasets relevant for conversational search.

Learn to build and customize multi-agent systems using the AutoGen. The course teaches you to implement complex AI applications through agent collaboration and advanced design patterns.