LangChain vs LlamaIndex
LangChain
Open-source framework for building LLM applications with chains, memory, and agent tools.
Teams building complex AI agents, multi-tool workflows, chatbots with memory, and enterprise applications requiring diverse integrations and fine-grained control.
LlamaIndex
Python/TypeScript library specialized in retrieval-augmented generation with intelligent document indexing and query engines.
Data teams, knowledge management systems, document Q&A applications, and projects where retrieval quality and RAG optimization are the primary concern.
Short Answer
LangChain is a general-purpose LLM orchestration framework excelling at building diverse AI applications with 40+ integrations, while LlamaIndex (formerly GPT Index) specializes in retrieval-augmented generation (RAG) with advanced document indexing and querying capabilities. LangChain dominates market share with 85,000+ GitHub stars versus LlamaIndex's 33,000+.
Our Verdict
AI-assistedChoose LangChain if you're building complex, multi-step AI applications with agents, chains, and diverse tool integrations across different LLMs and services. Choose LlamaIndex if your primary goal is building high-quality retrieval-augmented generation systems with optimized document indexing, querying, and search relevance—it's the specialized tool that excels where it's designed to work. Most enterprise teams use both: LangChain as the orchestration backbone and LlamaIndex for the RAG module.
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Choose LangChain if
Teams building complex AI agents, multi-tool workflows, chatbots with memory, and enterprise applications requiring diverse integrations and fine-grained control.
Choose LlamaIndex if
Data teams, knowledge management systems, document Q&A applications, and projects where retrieval quality and RAG optimization are the primary concern.
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Key Differences at a Glance
Key Facts & Figures
| Metric | LangChain | LlamaIndex | Diff |
|---|---|---|---|
| LLM Integrations(integrations) | 50+ providers | 45+ providers | +11% |
| Vector Store Support(integrations) | 30+ stores | 35+ stores | -14% |
| Enterprise Market Share(%) | 65% of LLM framework users | 28% of RAG-focused projects | +132% |
| Setup Time for Basic RAG(minutes) | 25-40 minutes | 5-10 minutes | +357% |
| LLM Provider Integrations(count) | 50+ | — | — |
| Vector Store Integrations(count) | 12+ (Pinecone, Weaviate, FAISS, Supabase) | 35+ | -66% |
| Release Frequency(minor releases/year) | 24+ | — | — |
| GitHub Stars(stars) | 60,000+ | 33,000+ | +82% |
| Monthly NPM/PyPI Downloads(downloads) | 5.2 million | 180,000+ | +2789% |
| Memory Types Supported(count) | 8 (buffer, entity, KG, summary, etc.) | — | — |
| Document Processors Available(count) | 5 (basic loaders) | — | — |
| Typical Memory Footprint (Loaded State)(MB) | 512-768 MB | — | — |
| Agent Types(count) | 12+ (ReAct, MRKL, Plan-and-Execute, OpenAI tools) | — | — |
| Weekly NPM Downloads(downloads) | 25,000 | — | — |
| LLM Provider Support(providers) | 100+ | — | — |
| Third-Party Integrations(count) | 500+ | — | — |
| Production Adoption Rate(%) | 70% | — | — |
| Multi-Agent Orchestration Complexity(lines of code) | 150-300 | — | — |
| Documentation Maturity(pages) | 500+ | — | — |
| Documentation Pages(pages) | 500+ | 500+ | — |
| Vector Database Integrations(integrations) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | — |
| LLM Model Providers Supported(providers) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | — |
| Average Setup Time(minutes) | 2-4 hours | 2-4 hours | — |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | 20+ (Slack, Notion, Google Workspace, etc.) | — |
| Latest Release Activity(commits per month (avg)) | 150+ commits/month | 150+ commits/month | — |
| Pre-trained Models(models) | 100+ integrations | 100+ integrations | — |
| Data Connectors/Loaders(connectors) | 200+ | 200+ | — |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | 1-2 weeks | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
LangChain
General LLM orchestration & multi-tool workflows
LlamaIndex
Retrieval-augmented generation & document querying
LangChain
85,000+🏆
LlamaIndex
33,000+
LangChain
50+ including OpenAI, Anthropic, Cohere, Ollama
LlamaIndex
45+ including OpenAI, Anthropic, Bedrock, Vertex AI
LangChain
Basic retrieval chains, requires custom setup
LlamaIndex
Purpose-built RAG with auto-retrieval, re-ranking, fusion🏆
LangChain
30+ integrations (Pinecone, Weaviate, Milvus, etc.)
LlamaIndex
35+ integrations with native vector store abstraction🏆
LangChain
Moderate - broader abstraction layer required
LlamaIndex
Gentle - intuitive document-to-query flow🏆
LangChain
65% of surveyed companies using LLM frameworks🏆
LlamaIndex
28% of surveyed companies in RAG-heavy workflows
Full Comparison
| Attribute | LangChain | LlamaIndex |
|---|---|---|
| LLM Integrations(integrations) | 50+ providers | 45+ providers |
| Vector Store Support(integrations) | 30+ stores | 35+ stores |
| RAG Pipeline Maturity(maturity level) | Composable chains (manual setup) | Purpose-built with auto-optimization |
| Agent Framework Maturity(maturity level) | Advanced (ReAct, Tool-using, custom) | Emerging (basic tool support) |
| Enterprise Market Share(%) | 65% of LLM framework users | 28% of RAG-focused projects |
| Transformers Library Monthly Downloads(downloads) | Not tracked separately | — |
| Setup Time for Basic RAG(minutes) | 25-40 minutes | 5-10 minutes |
| Multi-Agent Orchestration Complexity(lines of code) | 150-300 | — |
| Production Monitoring Tools(tool availability) | LangSmith (dedicated platform) | Basic logging via LlamaDebug |
| Production Observability Features(null) | Built-in logging, caching, callback handlers | — |
| LLM Provider Integrations(count) | 50+ | — |
| Vector Store Integrations(count) | 12+ (Pinecone, Weaviate, FAISS, Supabase) | 35+ |
| Memory Types Supported(count) | 8 (buffer, entity, KG, summary, etc.) | — |
| Document Processors Available(count) | 5 (basic loaders) | — |
| Agent Types(count) | 12+ (ReAct, MRKL, Plan-and-Execute, OpenAI tools) | — |
Show 1 more attributePrimary Use Case Optimization(null) RAG and retrieval-augmented systems — | ||
| Primary Language | Python (primary) + JavaScript/TypeScript | — |
| Release Frequency(minor releases/year) | 24+ | — |
| Azure OpenAI Integration Quality(native support level) | Community-maintained, requires manual configuration | — |
| Community Size(Discord members (approximate)) | 35,000+ | — |
| Microsoft Copilot Integration(native support) | Limited, requires plugins | — |
| Azure/Microsoft Ecosystem Integration(integration level) | Minimal (basic Azure OpenAI support) | — |
| GitHub Stars(stars) | 60,000+ | 33,000+ |
| Monthly NPM/PyPI Downloads(downloads) | 5.2 million | 180,000+ |
| Typical Memory Footprint (Loaded State)(MB) | 512-768 MB | — |
| Weekly NPM Downloads(downloads) | 25,000 | — |
| LLM Provider Support(providers) | 100+ | — |
| Third-Party Integrations(count) | 500+ | — |
| Pre-trained Models(models) | 100+ integrations | — |
| Production Adoption Rate(%) | 70% | — |
| Documentation Maturity(pages) | 500+ | — |
| 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.) | — |
| Primary Language Support(languages) | Python (primary), TypeScript/JavaScript | — |
| LLM Model Providers Supported(providers) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | — |
| Average Setup Time(minutes) | 2-4 hours | — |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | — |
| Latest Release Activity(commits per month (avg)) | 150+ commits/month | — |
| Data Connectors/Loaders(connectors) | 200+ | — |
| API Inference Service(null) | No native inference API | — |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | — |
Show 1 more attribute
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
LangChain
Pros
- 50+ LLM provider integrations (OpenAI, Anthropic, Claude, Ollama, etc.)
- Robust agent framework with ReAct, Tool-using, and custom execution strategies
- Advanced memory systems (ConversationBuffer, Summary, EntityMemory) for stateful applications
- 30+ vector store integrations for flexible RAG implementation
- Largest ecosystem with 85,000+ GitHub stars and strongest community support
- LangSmith monitoring platform for production debugging and optimization
- Flexible expression language (LCEL) for complex orchestration workflows
Cons
- Steeper learning curve due to broader abstraction and more configuration options
- RAG implementation requires manual chain composition and lacks built-in optimization strategies
- Heavier resource footprint for simple document querying tasks
LlamaIndex
Pros
- Purpose-built RAG pipeline with auto-retrieval, re-ranking, and fusion search
- 35+ vector store integrations with native abstraction layer
- Intelligent document indexing with hierarchical, semantic, and keyword-aware structures
- Query optimization and multi-turn conversation support out of the box
- Lower barrier to entry for developers new to RAG concepts
- Sub-question query engines and document hierarchy support for complex documents
- Streaming support and async/await patterns for performance optimization
Cons
- Narrower focus limits flexibility for non-RAG use cases (agents, tool calling less mature)
- Smaller ecosystem (33,000 GitHub stars) with fewer third-party integrations
- Less mature agentic capabilities compared to LangChain's agent framework
Frequently Asked Questions
Use LlamaIndex as your primary tool—it's purpose-built for RAG with optimized document indexing and querying out of the box. If you need conversational memory, multi-turn context, or integration with external tools, wrap LlamaIndex within a LangChain agent. This two-tier approach (LangChain orchestration + LlamaIndex RAG) is the industry standard for production document Q&A systems.
Resources & Learn More
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