LangChain is a general-purpose LLM orchestration framework excelling at agent building and multi-step workflows, while LlamaIndex (formerly GPT Index) specializes in retrieval-augmented generation (RAG) for querying and indexing private data. LangChain has broader adoption with 80K+ GitHub stars versus LlamaIndex's 35K+, making it the more versatile choice for diverse AI applications.
LangChain vs LlamaIndex — Side-by-side comparison on A Versus B
Open-source framework for building LLM-powered applications with composable chains and agent tools.
Specialized Python library for building RAG applications with efficient data indexing and retrieval.
Primary Use Case. GitHub Stars. LLM Integrations. Vector Store Support. Agent Framework Maturity. Memory Management. Community Size (Discord)
Choose LangChain if you're building complex multi-agent systems, need broad LLM compatibility, or require advanced orchestration like tool use, memory management, and workflow chains. Choose LlamaIndex if your primary goal is building RAG applications, indexing proprietary documents, or querying structured data with superior vector store optimization and retrieval quality.
LangChain: 50+ providers | LlamaIndex: 45+ providers
LangChain: Composable chains (manual setup) | LlamaIndex: Purpose-built with auto-optimization
LangChain: Advanced (ReAct, Tool-using, custom) | LlamaIndex: Emerging (basic tool support)
LangChain: 65% of LLM framework users | LlamaIndex: 28% of RAG-focused projects
LangChain: 25-40 minutes | LlamaIndex: 5-10 minutes
LangChain: LangSmith (dedicated platform) | LlamaIndex: Basic logging via LlamaDebug
LangChain: 12+ (Pinecone, Weaviate, FAISS, Supabase) | LlamaIndex: 35+
LangChain: Python (primary) + JavaScript/TypeScript | LlamaIndex: N/A
LangChain: Community-maintained, requires manual configuration | LlamaIndex: N/A
LangChain: Limited, requires plugins | LlamaIndex: N/A
LangChain: 5.2 million | LlamaIndex: 180,000+
LangChain: 8 (buffer, entity, KG, summary, etc.) | LlamaIndex: N/A
LangChain: 5 (basic loaders) | LlamaIndex: N/A
LangChain: 12+ (ReAct, MRKL, Plan-and-Execute, OpenAI tools) | LlamaIndex: N/A
LangChain: 95,000+ | LlamaIndex: 35,000+
LangChain: October 2022 | LlamaIndex: N/A
LangChain: 7 specialized memory types | LlamaIndex: N/A
LangChain: No (requires custom code) | LlamaIndex: N/A
LangChain: 25+ providers | LlamaIndex: N/A
LangChain: Integrates with external sources | LlamaIndex: N/A
LangChain: No (external integration required) | LlamaIndex: N/A
LangChain: 95,000+ stars | LlamaIndex: N/A
LangChain: Python, JavaScript/TypeScript | LlamaIndex: N/A
LangChain: 30-60 minutes (with documentation) | LlamaIndex: N/A
LangChain: Requires manual setup (6/10) | LlamaIndex: N/A
Cost for Production Deployment (monthly estimate)
LangChain: $200-1000+ (depends on LLM provider) | LlamaIndex: N/A
LangChain: 0 (integrates externally) | LlamaIndex: N/A
LangChain: 6 (Buffer, Summary, Entity, Vector, Knowledge Graph, Multi-window) | LlamaIndex: N/A
LangChain: Yes (LangChain Plus paid tier) | LlamaIndex: N/A
LangChain: N/A (framework only) | LlamaIndex: N/A
LangChain: Native with document loaders and retrievers | LlamaIndex: N/A
LangChain: Baseline (100%) | LlamaIndex: +25-30% faster
LangChain: 45,000+ | LlamaIndex: 18,000+
LangChain: 8/10 (Steep) | LlamaIndex: 5/10 (Moderate)
LangChain: 200+ integrations | LlamaIndex: N/A
LangChain: Baseline (100%) | LlamaIndex: N/A
LangChain: 2,000+ enterprises | LlamaIndex: N/A
LangChain: 500+ tutorials & guides | LlamaIndex: 500+
LangChain: 40-60 hours with manual orchestration | LlamaIndex: N/A
LangChain: 2-3 breaking changes | LlamaIndex: N/A
LangChain: N/A | LlamaIndex: Yes (LlamaIndex Cloud)
LangChain: N/A | LlamaIndex: MIT (open source)
LangChain: N/A | LlamaIndex: 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.)
LangChain: N/A | LlamaIndex: 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.)
LangChain: N/A | LlamaIndex: 20+ (Slack, Notion, Google Workspace, etc.)
LangChain: N/A | LlamaIndex: Python (primary), TypeScript/JavaScript
LangChain: N/A | LlamaIndex: Minimal (basic Azure OpenAI support)
LangChain: N/A | LlamaIndex: 150+ commits/month
LangChain: N/A | LlamaIndex: 100+ integrations
LangChain: N/A | LlamaIndex: Not tracked separately
LangChain: N/A | LlamaIndex: RAG and retrieval-augmented systems
LangChain: N/A | LlamaIndex: Built-in logging, caching, callback handlers
LangChain: N/A | LlamaIndex: No native inference API
LangChain: N/A | LlamaIndex: 6+ (hybrid, fusion, reranking, hierarchical, etc.)
LangChain: N/A | LlamaIndex: 8+ (via supported vector DB integrations)
LangChain: N/A | LlamaIndex: Dashboard + eval framework + cost tracking
How to choose between LangChain and LlamaIndex
LangChain vs LlamaIndex — Structured Comparison Data
Machine-readable comparison dataset for LangChain vs LlamaIndex. Contains 81 attribute comparisons, 5 FAQ pairs.
LangChain vs LlamaIndex — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for LangChain vs LlamaIndex.
Which framework is better for building a chatbot with RAG?
LlamaIndex is better for RAG-focused applications due to 25-30% faster retrieval speeds and superior vector store optimization. However, if your chatbot needs multi-step reasoning, tool use, or agent capabilities, use LangChain with its advanced agent framework combined with LlamaIndex for the RAG component (they integrate seamlessly).
Can LangChain and LlamaIndex be used together?
Yes, they're complementary. Many production applications use LlamaIndex for data indexing and retrieval, then pass results to LangChain agents for complex reasoning and multi-step workflows. LangChain has native integration with LlamaIndex through its `LlamaIndexToolkit`.
Which has better documentation and learning resources?
LangChain has significantly more documentation with 800+ tutorials, official courses, and a larger community creating guides. LlamaIndex has solid documentation but fewer third-party resources. For beginners, LlamaIndex's simpler API is easier to learn despite fewer resources.
What's the production readiness comparison?
LangChain is more production-ready with LangSmith for monitoring, LangServe for deployment, and broader enterprise adoption (used by major companies like Coca-Cola, Shopify, and IBM). LlamaIndex is production-ready for RAG systems but less mature for complex agent orchestration in enterprise settings.
Which framework requires fewer lines of code for basic RAG?
LlamaIndex requires approximately 40% fewer lines of code for basic RAG tasks due to its simpler, more focused API. LangChain requires more boilerplate but offers greater flexibility for complex workflows. For a basic document Q&A system, LlamaIndex gets you to production faster.
How to Choose Between LangChain and LlamaIndex
Primary Use Case: LangChain has General LLM orchestration, agents, chains, and workflows, while LlamaIndex has Retrieval-augmented generation (RAG) and data indexing.
GitHub Stars: LangChain has 80,000+, while LlamaIndex has 35,000+.
LLM Integrations: LangChain has 100+ models (OpenAI, Anthropic, Cohere, Llama, etc.), while LlamaIndex has 70+ models (OpenAI, Anthropic, Bedrock, Ollama, etc.).
Vector Store Support: LangChain has 30+ vector databases, while LlamaIndex has 50+ vector databases with native optimization.
Agent Framework Maturity: LangChain has Advanced agent types (ReAct, MRKL, OpenAI Assistants API native support), while LlamaIndex has Basic agent capabilities, limited agent types.
Memory Management: LangChain has 7+ memory types (buffer, summary, entity, vector store memory, etc.), while LlamaIndex has 3 memory types (chat history, summary, similarity-based).
Community Size (Discord): LangChain has 45,000+ active members, while LlamaIndex has 18,000+ active members.