LangSmith, by LangChain is a framework-agnostic Agent Engineering Platform designed for the observation, evaluation, and deployment of Large Language Model (LLM) agents. The solution provides a unified environment to transform Trace Data into actionable insights for iterative agent improvement and enterprise-scale production management.
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Rasa
Score8.4 out of 10
Enterprise companies (1,001+ employees)
Rasa is a conversational AI platform from the company of the same name headquartered in San Francisco, enabling enterprises to build customer experiences. Rasa’s platform was built to create enterprise-grade virtual assistants, allowing personalized conversations with customers - at scale. Rasa’s conversational AI platform allows companies to build better customer experiences by lowering costs through automation, improving customer satisfaction, and providing a scalable way to gather customer…
Senior Data Scientist in Information Technology at Deutsche Telekom IT (Telecommunications, 5001-10,000 employees)
Chose Rasa
Glean - proprietary semantic search algorithms, no backend actions integration IBM Watsonx - complicated dialogue builder, poor separation of no-code and pro-code interfaces ELMOS (agent based) - all logic in code, no dialogue logic in no-code interface possible
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
I feel like LangSmith Agent Engineering Platform is great for complex AI agents built with LangChain that take many steps. It is very useful in projects which requires multiple subagents. We built an AI Answer engine for our product where we use many subagents in LangSmith Agent Engineering Platform for query generation, optimization, summarization, etc.
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
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
In my opinion the technologies and concepts which I use with LangSmith Agent Engineering Platform like API tracing, prompt management, Prompt Hub and Agent Builder were like pioneer in the market with LangSmith Agent Engineering Platform. There are some scopes of improvements in terms of compatibilities and open-source tooling gaps but overall, it has to be on the list if you are doing AI-engineering at Production level.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
With the help of dedicated team - documentation and video resources it is relatively easier to build. We prioritized pro-code usage to begin with launch.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Rasa support has been very responsive, trying to fix any reported issues ASAP. They've also listened to many requests for improvement. The Rasa features and changelog are well documented
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
