TrustRadius Insights for Rasa are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Steady Improvement: Several users have noted that Rasa has steadily improved its underlying technology over time, positioning it as a strong competitor in the market.
Customization and Flexibility: Reviewers appreciate that Rasa is an open-source framework, allowing for greater customization to meet different use cases and business problems. This flexibility makes it a preferred choice among developers.
User-Friendly Interface: Users find Rasa's user interface intuitive and easy to navigate, enabling them to complete important tasks efficiently. The streamlined design enhances the overall user experience when developing chatbots.
Greate customer support, by listening towards the clients needs.
And building future proof solutions around client Business Requirements within dazzling timeframes
Cons
UX/UI optimalisation (enhance the ease of use of their products)
Plug-and-play solutions, with more modular system integrations
I don't know if Rasa is a SaaS low-code platform as Zapier, Make, N8N (focus on private solutions, smal businesses). To steal the competition towards Mid/large companies with there modules?
Mature (conversational) Analysing tool that measure all KPI's within slick dashboards with filtering system or endpoint towards Tableau/BPI/... : intent, feedback, fallback, response, Anomaly detection, Sentiment analysis, email push notifications on thresholds, Voice calls sentiment and measurement, Security & Compliance, ...
I have been using the platform for over 3 years and I have noticed a very good evolution, in an attempt to reinvent themselves. The support team is amazing, always available to work out with us in achieving the best results. About the technology, the algorithms available in the platform suits most of the cases. Being language agnostic is a very positive point for us, because some big tech platforms have little support for PT-PT language.
VU
Verified User
Team Lead in Customer Service (5001-10,000 employees)
Our use case involves an internal IT support helpdesk, which is served by the chatbot. We use Rasa Pro, Rasa SDK (action server) and Rasa Studio products. Our chatbot is supporting users with all hardware, software and access issues at their workplace. The hardware includes e.g. company mobile phones, laptops, printers and accessories. The software includes different applications from the internal software catalogue.
Pros
Provides transparent interface for dialogue management
Pipeline-based approach to processing messages allows easy extension and customization of message processing components.
Seamless integration of Rasa SDK for custom actions provides a powerful interface for integrating the chatbot with other systems for data retrieval and manipulation.
Rasa CALM does a very good job at restricting LLM hallucination.
Cons
Rasa CALM flows and Rasa domain could be made fully independent of the Rasa training process and dynamically retrievable from e.g. a graph DB. This would make the chatbot more flexible.
Prompt templates, or at least paths could be referenced in Rasa config. Different policies in the Rasa config could then be configured without code change to use different prompt templates
LLM configuration should rather be part of the endpoints, than model configuration.
Rasa Studio could support all the functionality of Rasa Pro.
Likelihood to Recommend
Rasa Pro is well suited for corporate use and for chatbots which require backend connections. Smaller chatbots with a few flows might be better served with a simple dialogue engine and custom AI agents, or Rasa Open Source. Rasa does not come with its own complex vector database, just in-memory FAISS and connectors to external vector DB's such as Milvus and Qdrant. It provides only a basic document parser and embedder for FAISS. If you need to build a RAG focused chatbot around a large knowledge base with complex documents, e.g. lots of MS Word or PDF files, you'll have to build a separate document parser and embedder, as well as your own semantic search engine.
