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No. Azure OpenAI and OpenAI are central integrations, and the ecosystem also documents connectors or examples for providers such as Google Gemini, Hugging Face, Mistral, and Ollama. Teams should verify runtime-specific connector maturity before standardizing on a provider, because support can differ across .NET, Python, and Java.
If you're in a .NET-first organization or need tight Azure integration, Semantic Kernel is the clearer fit. For pure Python projects, LangChain may offer broader community examples and integration coverage. Semantic Kernel's Python SDK is capable, but teams should compare the specific connectors and agent features they need before choosing.
Semantic Kernel supports prompt templates that can be stored with application code and reviewed through normal software delivery workflows. Teams commonly keep prompt files, model settings, and related metadata in version control so changes can be tested, reviewed, and rolled back like other application assets.
Yes, but it should be evaluated as an SDK for building application-integrated agent behavior rather than as a dedicated multi-agent workbench. For complex multi-agent orchestration, compare its agent and process patterns against specialist frameworks such as AutoGen, LangGraph, or CrewAI.
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