Open source machine learning framework for building contextual text- and voice-based chatbots and assistants.
Rasa is an open-source machine learning framework for building contextual AI assistants and chatbots. It automates text- and voice-based conversations by combining natural language understanding (NLU) with dialogue management, enabling systems that handle complex, multi-turn interactions. The framework connects to various messaging platforms and allows for custom channel integrations.
Developers and engineers building production-grade conversational AI agents for customer support, virtual assistants, or interactive applications across platforms like Slack, Facebook Messenger, or custom interfaces.
Developers choose Rasa for its open-source flexibility, strong contextual dialogue management, and ability to handle layered conversations without relying on rigid decision trees. It provides a machine learning-based approach that can be customized and self-hosted, unlike many proprietary chatbot platforms.
💬 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
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Uses machine learning models to handle layered, multi-turn conversations, enabling natural back-and-forth interactions beyond rigid decision trees, as highlighted in its focus on complex dialogues.
Supports various messaging channels like Slack, Facebook Messenger, Telegram, and custom channels, allowing wide deployment without vendor lock-in.
Fully open-source with a modular architecture, permitting deep customization and self-hosting, which is ideal for enterprises needing control over their chatbot infrastructure.
Designed for enterprise use in sectors like banking and telecom, indicating reliability for complex applications with proven track records.
The project is in maintenance mode with no new features, as development has shifted to Hello Rasa, making it less future-proof for new projects.
Requires setup with Poetry and significant data for NLU model training, which can be resource-intensive and challenging for teams without ML expertise.
With the focus on Hello Rasa, documentation for the legacy version might become outdated, and community support could dwindle, as implied by the README's emphasis on the new platform.