LangChain is a Python-first framework with 90+ integrations and broader LLM support, while Semantic Kernel is a Microsoft-backed C# framework optimized for Azure OpenAI and enterprise environments. LangChain dominates in popularity and flexibility, but Semantic Kernel offers deeper Azure integration and better C# ecosystem support.
LangChain vs Semantic Kernel — Side-by-side comparison on A Versus B
Open-source Python framework for building AI applications with LLM chains, agents, and memory management.
Microsoft-backed C# framework for orchestrating AI services with plugins, planners, and Azure OpenAI optimization.
Primary Language Support. Number of LLM Integrations. GitHub Stars (as of 2026). Azure Integration Level. Enterprise Adoption. Learning Curve for Beginners. Community Size & Activity
Choose LangChain if you need maximum flexibility, broad LLM model support, Python priority, and access to the largest AI dev community. Choose Semantic Kernel if you're building in C#, heavily invested in Microsoft Azure, require enterprise-grade integrations, or need tighter OpenAI API integration with Semantic Kernel's plugin system.
LangChain: Composable chains (manual setup) | Semantic Kernel: N/A
LangChain: Advanced (ReAct, Tool-using, custom) | Semantic Kernel: N/A
LangChain: 65% of LLM framework users | Semantic Kernel: N/A
LangChain: 25-40 minutes | Semantic Kernel: N/A
LangChain: LangSmith (dedicated platform) | Semantic Kernel: N/A
LangChain: 12+ (Pinecone, Weaviate, FAISS, Supabase) | Semantic Kernel: 8
LangChain: Community-maintained, requires manual configuration | Semantic Kernel: Native, optimized with Entra ID + Key Vault built-in
LangChain: 35,000+ | Semantic Kernel: 8,000+
LangChain: Limited, requires plugins | Semantic Kernel: Native, first-class Copilot Stack integration
LangChain: 5.2 million | Semantic Kernel: N/A
LangChain: 8 (buffer, entity, KG, summary, etc.) | Semantic Kernel: N/A
LangChain: 5 (basic loaders) | Semantic Kernel: N/A
LangChain: 512-768 MB | Semantic Kernel: N/A
LangChain: 12+ (ReAct, MRKL, Plan-and-Execute, OpenAI tools) | Semantic Kernel: N/A
LangChain: 25,000 | Semantic Kernel: N/A
LangChain: 150-300 | Semantic Kernel: N/A
LangChain: October 2022 | Semantic Kernel: N/A
LangChain: 7 specialized memory types | Semantic Kernel: N/A
LangChain: No (requires custom code) | Semantic Kernel: N/A
LangChain: 25+ providers | Semantic Kernel: N/A
LangChain: Integrates with external sources | Semantic Kernel: N/A
LangChain: No (external integration required) | Semantic Kernel: N/A
LangChain: 95,000+ stars | Semantic Kernel: N/A
LangChain: Python, JavaScript/TypeScript | Semantic Kernel: N/A
LangChain: 30-60 minutes (with documentation) | Semantic Kernel: N/A
LangChain: Requires manual setup (6/10) | Semantic Kernel: N/A
Cost for Production Deployment (monthly estimate)
LangChain: $200-1000+ (depends on LLM provider) | Semantic Kernel: N/A
LangChain: 0 (integrates externally) | Semantic Kernel: N/A
LangChain: 6 (Buffer, Summary, Entity, Vector, Knowledge Graph, Multi-window) | Semantic Kernel: N/A
LangChain: Yes (LangChain Plus paid tier) | Semantic Kernel: N/A
LangChain: N/A (framework only) | Semantic Kernel: N/A
LangChain: Native with document loaders and retrievers | Semantic Kernel: N/A
LangChain: 8,500,000+ | Semantic Kernel: N/A
LangChain: ~95,000 | Semantic Kernel: ~24,000
LangChain: 30-45 minutes | Semantic Kernel: N/A
Production Maturity (years since launch)
LangChain: 3+ years | Semantic Kernel: N/A
LangChain: 5+ types | Semantic Kernel: N/A
LangChain: Manual agent coordination required | Semantic Kernel: N/A
LangChain: Baseline (100%) | Semantic Kernel: N/A
LangChain: ~5,000+ | Semantic Kernel: ~1,500
LangChain: 15,000+ | Semantic Kernel: N/A
LangChain: 8/10 (Steep) | Semantic Kernel: N/A
LangChain: Standard (community-maintained) | Semantic Kernel: Native (Microsoft-maintained)
LangChain: 2-3 weeks | Semantic Kernel: 2-4 weeks
LangChain: Available (LangChain Plus, third-party vendors) | Semantic Kernel: Available (Microsoft Support, Azure Services)
LangChain: Full support (LangChain.js) | Semantic Kernel: Emerging (official bindings)
LangChain: 200+ integrations | Semantic Kernel: N/A
LangChain: 2,000+ enterprises | Semantic Kernel: N/A
LangChain: 40-60 hours with manual orchestration | Semantic Kernel: N/A
LangChain: 2-3 breaking changes | Semantic Kernel: N/A
LangChain: 50+ LLM integrations | Semantic Kernel: N/A
LangChain: 50,000+ | Semantic Kernel: N/A
LangChain: 5-10 min | Semantic Kernel: N/A
LangChain: No (BYO infrastructure) | Semantic Kernel: N/A
LangChain: 50-200ms (provider dependent) | Semantic Kernel: N/A
LangChain: Python & JavaScript equally | Semantic Kernel: C# (primary), Python
LangChain: N/A | Semantic Kernel: 35+ (OpenAI, Claude, Cohere, Hugging Face, etc.)
LangChain: N/A | Semantic Kernel: 8 (Azure AI Search, Cosmos DB, etc.)
LangChain: N/A | Semantic Kernel: 60+ (Dynamics 365, SAP, Salesforce, ServiceNow, etc.)
LangChain: N/A | Semantic Kernel: 3-6 hours
LangChain: N/A | Semantic Kernel: Native (Azure AI, Cosmos DB, Office 365, Teams, Dynamics 365)
LangChain: N/A | Semantic Kernel: 120+ commits/month
How to choose between LangChain and Semantic Kernel
LangChain vs Semantic Kernel — Structured Comparison Data
Machine-readable comparison dataset for LangChain vs Semantic Kernel. Contains 79 attribute comparisons, 5 FAQ pairs.
LangChain vs Semantic Kernel — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for LangChain vs Semantic Kernel.
Which framework has better documentation and learning resources?
LangChain has more third-party tutorials, blog posts, and community examples due to its larger user base, but documentation quality can vary. Semantic Kernel has more focused, official Microsoft documentation and better C#/.NET ecosystem integration. For Python developers, LangChain wins; for C# developers, Semantic Kernel is superior.
Can I use LangChain or Semantic Kernel with models other than OpenAI?
LangChain supports 90+ model providers including Anthropic Claude, Google Gemini, Cohere, Hugging Face, Ollama, and local models, providing maximum flexibility. Semantic Kernel supports ~25 providers with emphasis on Azure OpenAI, OpenAI, and Hugging Face. For multi-model strategies, LangChain is significantly more flexible.
Which is better for Azure-native enterprises?
Semantic Kernel is purpose-built for Azure and Microsoft ecosystems with native Azure OpenAI integration, Azure Authentication, and Azure Service Bus support. LangChain works with Azure but requires additional configuration. Semantic Kernel is the recommended choice for Azure-first organizations.
What's the performance difference between the two frameworks?
Performance depends on use case: LangChain has slightly more overhead due to its abstraction breadth but is highly optimizable. Semantic Kernel is leaner for Azure OpenAI workloads. Both are production-ready with comparable latency for most applications. Direct benchmarks show <10% performance difference in typical chatbot scenarios.
Which framework is better for production AI applications?
Both are production-ready. LangChain dominates with more deployed systems and operational battle-testing, plus LangChain Plus for monitoring. Semantic Kernel excels in enterprise with Microsoft backing and integrated Azure security. Choice depends on your tech stack: Python/multi-cloud = LangChain; C#/.NET/Azure = Semantic Kernel.
How to Choose Between LangChain and Semantic Kernel
Primary Language Support: LangChain has Python (primary), JavaScript/TypeScript (secondary), while Semantic Kernel has C# (primary), Python/JavaScript (secondary).
Number of LLM Integrations: LangChain has 90+ (OpenAI, Anthropic, Google, Cohere, Hugging Face, etc.), while Semantic Kernel has 25+ (OpenAI, Azure OpenAI, Hugging Face, local models).
GitHub Stars (as of 2026): LangChain has ~95,000 stars, while Semantic Kernel has ~24,000 stars.
Azure Integration Level: LangChain has Standard (via SDK), while Semantic Kernel has Deep native integration (Microsoft product).
Enterprise Adoption: LangChain has High (tech companies, startups), while Semantic Kernel has High (Microsoft enterprises, Azure-first companies).
Learning Curve for Beginners: LangChain has Moderate (extensive but sometimes inconsistent documentation), while Semantic Kernel has Moderate (excellent C# documentation, smaller community).
Community Size & Activity: LangChain has Very large (5,000+ Discord members, daily updates), while Semantic Kernel has Growing (1,500+ Discord members, regular updates).