Ragas vs LangSmith

Developers should learn and use Ragas when building or optimizing RAG systems, such as chatbots, question-answering tools, or document-based AI assistants, to ensure reliable and accurate outputs meets developers should use langsmith when building production-grade llm applications to streamline the development lifecycle, from prototyping to deployment. Here's our take.

Ragas

Nice Pick

Developers should learn and use Ragas when building or optimizing RAG systems, such as chatbots, question-answering tools, or document-based AI assistants, to ensure reliable and accurate outputs

Pros

• It is particularly useful during development, testing, and deployment phases to benchmark performance against industry standards and iterate on improvements based on quantitative feedback
• Related to: retrieval-augmented-generation, python

Cons

• Specific tradeoffs depend on your use case

LangSmith

Developers should use LangSmith when building production-grade LLM applications to streamline the development lifecycle, from prototyping to deployment

Pros

• It is essential for debugging complex chains of LLM calls, optimizing prompts, and ensuring consistent performance through automated testing and monitoring, making it particularly valuable for teams working on chatbots, agents, or any AI-driven software
• Related to: langchain, large-language-models

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Ragas is a tool while LangSmith is a platform. We picked Ragas based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Ragas wins

Based on overall popularity. Ragas is more widely used, but LangSmith excels in its own space.