As a Python dev, I'm torn between these two. LangChain has a massive ecosystem, but Semantic Kernel seems more "enterprise-ready" and structured. Which one is better for building production-level AI agents that need to integrate with existing legacy systems?
3 answers
The choice depends on your long-term roadmap. LangChain is fantastic for rapid prototyping and has a plugin for almost everything, but it can feel "brittle" in production due to frequent breaking changes. Semantic Kernel, being a Microsoft product, follows a more stable, interface-driven design. If your goal is to integrate with legacy systems (like a COBOL backend or an older SQL DB), the Semantic Kernel's "Native Function" approach is much cleaner. It treats your code as a first-class citizen, making it easier to maintain and test within a standard software development lifecycle.
Does the Semantic Kernel Python SDK have parity with the C# version yet? I noticed some features like certain planners were lagging behind in the Python repo earlier this year.
I switched to Semantic Kernel because the dependency injection and modularity made it much easier for our DevOps team to monitor the AI's behavior in our CI/CD pipeline.
Exactly, Pamela. The "Observability" hooks in Semantic Kernel are a major selling point for enterprise teams who need to audit exactly what the AI is doing at every step
Matthew, they have closed the gap significantly. Most of the core features like the Kernel, Plugins, and Function Choice Behavior are now consistent across both languages. While C# still gets some "enterprise" experimental features first, the Python version of Semantic Kernel is now more than capable for production use, especially with the 1.x release.