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RASA

Open-source framework for contextual AI assistants and chatbots

Apache-2.0

ABOUT

Building a production chatbot requires connecting intent classification, entity extraction, dialogue management, and channel integrations — each typically a separate service with different APIs and deployment models. Rasa provides an all-in-one framework where intent recognition is trained from example conversations, dialogue policies are configurable as stories or rules, and the output integrates with Slack, Facebook, Telegram, and custom channels. The entire system runs on-premise or in a private cloud, giving enterprises full control over conversational data.

INSTALL
pip install rasa

INTEGRATION GUIDE

1. Build a customer service chatbot that handles returns, refunds, and order tracking through natural conversation 2. Create a sales qualification bot that collects lead information and routes qualified prospects to the right team 3. Deploy a multilingual FAQ assistant trained on product documentation with fallback to human handoff 4. Develop a workflow automation bot that books appointments, reschedules meetings, and sends reminders via messaging

TAGS

conversational-ainluchatbotdialogue-managementnlppython