The LangChain framework is a popular way to LLM apps but popularity alone doesn’t make it the right fit for every use case. When QA Wolf started building our multi-agent AI system, we found it didn’t have the control we needed to handle real-world test automation.

So we built a custom framework tuned for fast hand-offs, sequential decision-making, and true end-to-end QA.

Nishant Shukla, QA Wolf’s Senior Director of AI, joins host Caleb Masters to outline the five questions we asked and answered for our project before going in a different direction.

In this webinar, you’ll learn:

• Why do so many teams reach for LangChain and where it delivers value out of the box.
  
• The five questions that helped QA Wolf evaluate (and ultimately skip) LangChain for our multi-agent system.
  
• How to evaluate the framework needs of your project. .

Check out how QA Wolf balances proven tools and custom builds to keep 80%+ test coverage stable by watching the video or reading our recap.