LlamaIndex vs Semantic Kernel
LlamaIndex
Python/TypeScript library specialized in retrieval-augmented generation with intelligent document indexing and query engines.
Data engineers, ML engineers, and startups building RAG systems, semantic search engines, and document-based AI applications
Semantic Kernel
Microsoft's framework for building AI-powered applications with native C#/.NET support and deep Azure/Copilot integration.
Enterprise organizations using Microsoft stack, businesses needing deep CRM/ERP integration, teams building orchestrated multi-model AI workflows with stateful management
Short Answer
LlamaIndex is a specialized data indexing and retrieval framework optimized for RAG applications with 50,000+ GitHub stars, while Semantic Kernel is Microsoft's orchestration platform with broader enterprise integration capabilities and 21,000+ GitHub stars. LlamaIndex excels at document parsing and vector search, whereas Semantic Kernel focuses on multi-model orchestration and enterprise connectors.
Our Verdict
AI-assistedChoose LlamaIndex if you're building RAG applications, need extensive vector database flexibility, or want rapid prototyping with minimal setup overhead—it dominates the data indexing and retrieval space. Choose Semantic Kernel if you're in a Microsoft enterprise environment, need orchestration across multiple AI services and business applications, or require deep Azure and Office 365 integration.
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Choose LlamaIndex if
Data engineers, ML engineers, and startups building RAG systems, semantic search engines, and document-based AI applications
Choose Semantic Kernel if
Enterprise organizations using Microsoft stack, businesses needing deep CRM/ERP integration, teams building orchestrated multi-model AI workflows with stateful management
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Key Differences at a Glance
Key Facts & Figures
| Metric | LlamaIndex | Semantic Kernel | Diff |
|---|---|---|---|
| Vector Store Integrations(count) | 35+ | 8 | +338% |
| Monthly NPM/PyPI Downloads(downloads) | 180,000+ | — | — |
| Documentation Pages(pages) | 500+ | — | — |
| Vector Database Integrations(integrations) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | 8 (Azure AI Search, Cosmos DB, etc.) | +150% |
| LLM Model Providers Supported(providers) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | 35+ (OpenAI, Claude, Cohere, Hugging Face, etc.) | +14% |
| Average Setup Time(minutes) | 2-4 hours | 3-6 hours | -33% |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | 60+ (Dynamics 365, SAP, Salesforce, ServiceNow, etc.) | -67% |
| Latest Release Activity(commits per month (avg)) | 150+ commits/month | 120+ commits/month | +25% |
| Pre-trained Models(models) | 100+ integrations | — | — |
| Data Connectors/Loaders(connectors) | 200+ | — | — |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | — | — |
| GitHub Stars(stars) | 33,000+ | 6,800+ | +385% |
| LLM Integrations(integrations) | 45+ providers | — | — |
| Vector Store Support(integrations) | 35+ stores | — | — |
| Enterprise Market Share(%) | 28% of RAG-focused projects | — | — |
| Setup Time for Basic RAG(minutes) | 5-10 minutes | — | — |
| LLM Provider Integrations(count) | 12+ | 12+ | — |
| Release Frequency(minor releases/year) | 3 | 3 | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
LlamaIndex
RAG (Retrieval-Augmented Generation) and document indexing
Semantic Kernel
AI orchestration and multi-model coordination
LlamaIndex
50,200+🏆
Semantic Kernel
21,400+
LlamaIndex
Limited (20+ connectors)
Semantic Kernel
Extensive (60+ enterprise connectors)🏆
LlamaIndex
20+ integrations (Pinecone, Weaviate, Milvus, etc.)🏆
Semantic Kernel
8+ integrations
LlamaIndex
40+ model providers🏆
Semantic Kernel
35+ model providers
LlamaIndex
2-4 hours🏆
Semantic Kernel
3-6 hours
LlamaIndex
Minimal
Semantic Kernel
Native (Azure, Office 365, Teams, etc.)🏆
Full Comparison
| Attribute | LlamaIndex | Semantic Kernel |
|---|---|---|
| Vector Store Integrations(count) | 35+ | 8 |
| Primary Use Case Optimization(null) | RAG and retrieval-augmented systems | — |
| LLM Provider Integrations(count) | 12+ | — |
| Monthly NPM/PyPI Downloads(downloads) | 180,000+ | — |
| Documentation Pages(pages) | 500+ | — |
| Enterprise Support Available | Yes (LlamaIndex Cloud) | — |
| License Type | MIT (open source) | — |
| Vector Database Integrations(integrations) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | 8 (Azure AI Search, Cosmos DB, etc.) |
| Primary Language Support(languages) | Python (primary), TypeScript/JavaScript | C# (primary), Python |
| LLM Model Providers Supported(providers) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | 35+ (OpenAI, Claude, Cohere, Hugging Face, etc.) |
| Average Setup Time(minutes) | 2-4 hours | 3-6 hours |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | 60+ (Dynamics 365, SAP, Salesforce, ServiceNow, etc.) |
| Azure/Microsoft Ecosystem Integration(integration level) | Minimal (basic Azure OpenAI support) | Native (Azure AI, Cosmos DB, Office 365, Teams, Dynamics 365) |
| Microsoft Copilot Integration(native support) | Native, first-class Copilot Stack integration | — |
| Latest Release Activity(commits per month (avg)) | 150+ commits/month | 120+ commits/month |
| Pre-trained Models(models) | 100+ integrations | — |
| Data Connectors/Loaders(connectors) | 200+ | — |
| Transformers Library Monthly Downloads(downloads) | Not tracked separately | — |
| Enterprise Market Share(%) | 28% of RAG-focused projects | — |
| Production Observability Features(null) | Built-in logging, caching, callback handlers | — |
| Production Monitoring Tools(tool availability) | Basic logging via LlamaDebug | — |
| API Inference Service(null) | No native inference API | — |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | — |
| GitHub Stars(stars) | 33,000+ | 6,800+ |
| LLM Integrations(integrations) | 45+ providers | — |
| Vector Store Support(integrations) | 35+ stores | — |
| RAG Pipeline Maturity(maturity level) | Purpose-built with auto-optimization | — |
| Agent Framework Maturity(maturity level) | Emerging (basic tool support) | — |
| Setup Time for Basic RAG(minutes) | 5-10 minutes | — |
| Primary Language | C# .NET (primary) + Python (secondary) | — |
| Release Frequency(minor releases/year) | 3 | — |
| Azure OpenAI Integration Quality(native support level) | Native, optimized with Entra ID + Key Vault built-in | — |
| Community Size(Discord members (approximate)) | 8,000+ | — |
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
LlamaIndex
Pros
- 50+ data connectors (PDFs, databases, APIs, web pages, Notion, Google Docs, etc.)
- 20+ vector database integrations with zero-shot compatibility
- Fastest integration path with 2-4 hour typical setup time
- Best-in-class document parsing and chunking strategies
- Active open-source community with 50,000+ GitHub stars
Cons
- Limited enterprise system connectors compared to Semantic Kernel
- Steeper learning curve for complex multi-agent orchestration scenarios
- Smaller commercial backing compared to Microsoft-backed alternatives
Semantic Kernel
Pros
- 60+ enterprise connectors (Dynamics 365, Power BI, SharePoint, Teams, Slack, ServiceNow, etc.)
- Native Azure integration with Cosmos DB, Azure OpenAI, and Azure AI Search
- Built-in support for plugins and function composition with C# and Python SDKs
- Strong backing from Microsoft with ongoing enterprise feature development
- Superior memory management and stateful conversation handling
Cons
- Slower initial setup (3-6 hours) with steeper configuration requirements
- Only 8 vector database integrations vs LlamaIndex's 20+
- Heavier memory footprint for lightweight applications
Frequently Asked Questions
LlamaIndex is purpose-built for RAG and has a significant advantage with 20+ vector database integrations, specialized document parsing, and chunking strategies. Setup takes 2-4 hours vs Semantic Kernel's 3-6 hours. If you're purely focused on RAG, LlamaIndex is the faster path to production.
Resources & Learn More
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Wikipedia
LlamaIndex on Wikipedia
Python/TypeScript library specialized in retrieval-augmented generation with intelligent document indexing and query engines.
Semantic Kernel on Wikipedia
Microsoft's framework for building AI-powered applications with native C#/.NET support and deep Azure/Copilot integration.
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