RasaHQ/rasa
Rasa
Rasa is a chatbot development platform and conversational AI framework used to design, deploy, and integrate multi-turn conversational agents. It functions as an LLM orchestration engine and NLU dialogue manager, combining large language model fluency with structured business logic to control agent behavior.
The framework enables the development of conversational assistants that automate text and voice interactions. It allows for the definition of conversational flows using flexible sequences and provides tools to inspect agent decisions to debug and validate the internal reasoning process.
The system covers natural language understanding for intent identification, dialogue management workflows to guide user journeys, and omnichannel integration to connect agents to various messaging platforms.
Features
- Conversational AI Agents - Enables the creation of conversational AI agents that combine LLM fluency with structured business logic.
- Intent Classification Pipelines - Ships an NLU pipeline that extracts meaning and identifies intents from user input via machine learning models.
- Multi-turn Interaction Managers - Manages the state and sequencing of multi-turn conversations to guide users through business journeys.
- Conversation Memory Stores - Provides a persistent memory system to store user information and maintain context across conversational turns.
- Conversation Flow Design - Provides tools for designing multi-turn conversational flows using flexible sequences for user journeys.
- Conversational AI Assistants - Offers a framework for building context-aware conversational assistants that handle complex multi-turn interactions.
- Conversational AI Frameworks - Provides a machine learning framework for building text and voice assistants with NLU and dialogue management.
- Custom AI Assistant Development - Supports the development of custom AI assistants by combining LLM fluency with defined business logic.
- LLM Orchestrators - Orchestrates large language models with structured business logic to control conversational flows.
- Natural Language Processing - Processes human language input to identify intents and extract information for conversational interfaces.
- Dialog Agents and Chatbots - Acts as a comprehensive platform for designing, deploying, and integrating multi-turn conversational agents.
- Dialogue Interaction Engines - Manages multi-turn conversational state and processes natural language intent through defined logical sequences.
- Agent Debugging Tools - Includes utilities for observing and refining the reasoning process of AI agents to fix dialogue logic.
- Chatbot Integrations - Integrates a single conversational agent across multiple messaging platforms like Slack and Facebook Messenger.
- Sequence Learning Models - Employs sequence learning models to predict the next response in multi-turn interactions based on conversation paths.
- Conversational Channel Integrations - Connects conversational interfaces to various third-party messaging platforms and custom communication channels.
- Custom Action Handlers - Provides a framework for executing external business logic and API calls through custom action handlers.
- Graph-Based Workflow Orchestrators - Uses directed graphs to track conversation state and determine the next logical action based on user intent.
- Message Bus Architectures - Implements a decoupled message bus architecture to route incoming messages and trigger specific handlers.
- Natural Language Processing - Framework for building automated text and voice assistants.
Star history
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