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Pricing sourced from Microsoft Semantic Kernel · Last verified March 2026
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View Full Features →No. While Azure OpenAI has the deepest integration, there are official connectors for OpenAI, Google Gemini, Hugging Face, Mistral, and Ollama. The IChatCompletionService interface lets you write custom connectors for any provider. The framework is provider-agnostic by design despite Microsoft's Azure emphasis.
If you're in a .NET-first organization or need tight Azure integration, Semantic Kernel is the clear choice. For pure Python projects, LangChain has a larger ecosystem, more integrations, and a bigger community. Semantic Kernel's Python SDK is capable but typically 2-3 months behind the C# SDK in features.
Semantic Kernel supports loading prompt templates from YAML files with metadata. Store these in version control alongside your code. Each template can specify model-specific settings for different LLM providers. The framework supports runtime template compilation with Handlebars syntax.
Yes, though it's not its primary strength. The Agent Framework (experimental) supports creating multiple agents with different personalities that can participate in group chats. For complex multi-agent orchestration, consider pairing Semantic Kernel's plugin system with a dedicated agent framework or using the Process Framework.
AI builders and operators use Microsoft Semantic Kernel to streamline their workflow.
Try Microsoft Semantic Kernel Now →Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.
Compare Pricing →Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.
Compare Pricing →Graph-based workflow orchestration framework for building reliable, production-ready AI agents with deterministic state machines, human-in-the-loop capabilities, and comprehensive observability through LangSmith integration.
Compare Pricing →Production-ready Python framework for building RAG pipelines, document search systems, and AI agent applications. Build composable, type-safe NLP solutions with enterprise-grade retrieval and generation capabilities.
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