LangChain is a general-purpose LLM orchestration framework excelling at multi-step agent workflows and diverse integrations, while LlamaIndex specializes in retrieval-augmented generation (RAG) with superior document indexing and querying capabilities for structured data extraction.
LangChain vs LlamaIndex — Side-by-side comparison on A Versus B
Open-source framework for building LLM-powered applications with chains, agents, and memory management.
Specialized framework for retrieval-augmented generation with advanced document indexing and structured data querying.
Primary Use Case Focus. Supported LLM Integrations. Vector Store Integrations. Community Activity (GitHub Stars). Documentation Depth. Learning Curve for RAG Tasks. Production Deployment Maturity
Choose LangChain if you're building complex agent-based systems, need extensive LLM provider flexibility, or require advanced memory management and observability tools like LangSmith. Choose LlamaIndex if your primary goal is RAG, you need best-in-class document indexing, superior vector database integration, and a simpler learning curve for retrieval pipelines.
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: 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: 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: 32,000
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: 85,000+ | LlamaIndex: 58,000+
LangChain: 100+ | LlamaIndex: 45+ providers
LangChain: 30-45 minutes | LlamaIndex: N/A
Production Maturity (years since launch)
LangChain: Manual agent coordination required | LlamaIndex: N/A
LangChain: Baseline (100%) | LlamaIndex: +25-30% faster
LangChain: ~5,000+ | LlamaIndex: 18,000+
LangChain: 8/10 (Steep) | LlamaIndex: 5/10 (Moderate)
LangChain: Standard (community-maintained) | LlamaIndex: N/A
LangChain: Available (LangChain Plus, third-party vendors) | LlamaIndex: N/A
LangChain: Full support (LangChain.js) | LlamaIndex: N/A
LangChain: 200+ integrations | LlamaIndex: N/A
LangChain: Baseline (100%) | LlamaIndex: N/A
LangChain: 2,000+ enterprises | LlamaIndex: N/A
LangChain: 40-60 hours with manual orchestration | LlamaIndex: N/A
LangChain: 2-3 breaking changes | LlamaIndex: N/A
LangChain: 50+ LLM integrations | LlamaIndex: N/A
LangChain: No (BYO infrastructure) | LlamaIndex: N/A
LangChain: 50-200ms (provider dependent) | LlamaIndex: N/A
LangChain: Python & JavaScript equally | LlamaIndex: Python (primary), TypeScript/JavaScript
LangChain: 25-35 minutes | LlamaIndex: 10-15 minutes
LangChain: Native LangSmith platform with debugging, tracing, evaluation | LlamaIndex: Limited native tools, integrates with external logging
LangChain: 120-180 seconds | LlamaIndex: 80-120 seconds
LangChain: 92% (broad but less RAG-focused) | LlamaIndex: 88% (deeper RAG coverage)
LangChain: Advanced (ReAct agents, tool-use, multi-step planning) | LlamaIndex: Basic (limited agent capabilities)
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: 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: 100+ data connectors
LangChain: N/A | LlamaIndex: Unlimited (via Pinecone/Weaviate)
LangChain: N/A | LlamaIndex: Native (built-in agents)
LangChain: N/A | LlamaIndex: 12 formats (PDF, DOCX, TXT, JSON, CSV)
LangChain: N/A | LlamaIndex: 5-10 minutes
LangChain: N/A | LlamaIndex: Partial (requires custom implementation)
LangChain: N/A | LlamaIndex: Limited (logging integration available)
How to choose between LangChain and LlamaIndex
LangChain vs LlamaIndex — Structured Comparison Data
Machine-readable comparison dataset for LangChain vs LlamaIndex. Contains 112 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 should I choose for a ChatGPT-style document Q&A bot?
LlamaIndex is the better choice for document Q&A. Its indexing pipeline is optimized for RAG, requiring ~60% less boilerplate code than LangChain. LlamaIndex handles chunking, metadata extraction, and retrieval automatically, getting you to production in 10-15 minutes versus 25-35 minutes with LangChain.
Which has better LLM provider flexibility?
LangChain supports 50+ LLM providers (OpenAI, Anthropic, Ollama, Bedrock, HuggingFace, Cohere, Together AI, etc.) versus LlamaIndex's 40+. For multi-model strategies or avoiding vendor lock-in, LangChain provides more options, though both support all major providers.
Can I use both LangChain and LlamaIndex together?
Yes, they integrate well. A common pattern is using LlamaIndex for RAG (document indexing/retrieval) and LangChain for orchestration (chaining RAG with agents, memory, and tools). This leverages each framework's strengths—LlamaIndex for data retrieval, LangChain for workflow logic.
Which has better production monitoring capabilities?
LangChain offers superior production observability with LangSmith, a native platform providing tracing, debugging, evaluations, and performance analytics. LlamaIndex relies on external logging and monitoring solutions, making LangChain the better choice for teams requiring comprehensive observability.
LlamaIndex has a gentler learning curve if your focus is RAG—you can build a working system with minimal concepts. LangChain requires understanding chains, agents, memory, and runnables, making it steeper for beginners but more powerful for complex workflows.
How to Choose Between LangChain and LlamaIndex
Primary Use Case Focus: LangChain has Multi-step agent orchestration, chains, memory management, while LlamaIndex has Retrieval-augmented generation, document indexing, data querying.
Supported LLM Integrations: LangChain has 50+ LLM providers (OpenAI, Anthropic, Ollama, Bedrock, Azure, HuggingFace, etc.), while LlamaIndex has 40+ LLM providers (similar coverage, slightly fewer options).
Vector Store Integrations: LangChain has 30+ vector databases (Pinecone, Weaviate, Chroma, Faiss, Milvus, etc.), while LlamaIndex has 35+ vector databases (broader vector DB ecosystem support).
Consider: Community Activity (GitHub Stars)
Community Activity (GitHub Stars): LangChain has 85,000+ stars (as of 2026), while LlamaIndex has 58,000+ stars (as of 2026).
Documentation Depth: LangChain has Comprehensive but broad (covers many use cases, less focused), while LlamaIndex has Narrower but deeper RAG-specific documentation.
Learning Curve for RAG Tasks: LangChain has Steeper (must understand chains, agents, memory concepts), while LlamaIndex has Gentler (RAG abstractions are more intuitive).
Consider: Production Deployment Maturity
Production Deployment Maturity: LangChain has Highly mature with LangSmith observability platform, while LlamaIndex has Mature but fewer native observability tools.