LangChain vs LlamaIndex 2026 Comparison
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
Open-source framework for building LLM-powered applications with chains, agents, and memory management.
Teams building complex multi-step agent workflows, needing production observability, and prioritizing flexibility across LLM providers.
LlamaIndex
Specialized framework for retrieval-augmented generation with advanced document indexing and structured data querying.
Data-centric applications, document Q&A systems, knowledge base builders, and teams prioritizing RAG-specific functionality with minimal configuration.
Quick Answer
AI SummaryLangChain 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.
Our Verdict
AI-assistedChoose 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.
Was this verdict helpful?
Choose LangChain if
Best pickTeams building complex multi-step agent workflows, needing production observability, and prioritizing flexibility across LLM providers.
Choose LlamaIndex if
Data-centric applications, document Q&A systems, knowledge base builders, and teams prioritizing RAG-specific functionality with minimal configuration.
Track this comparison
Get notified when prices change, new specs ship, or our verdict updates.
Triggers: price change new spec verdict update
No spam. Stop anytime.
Key Differences at a Glance
- Primary Use Case Focus:Multi-step agent orchestration, chains, memory management vs Retrieval-augmented generation, document indexing, data querying
- Supported LLM Integrations:✓ LangChain wins(50+ LLM providers (OpenAI, Anthropic, Ollama, Bedrock, Azure, HuggingFace, etc.) vs 40+ LLM providers (similar coverage, slightly fewer options))
- Vector Store Integrations:✓ LlamaIndex wins(35+ vector databases (broader vector DB ecosystem support) vs 30+ vector databases (Pinecone, Weaviate, Chroma, Faiss, Milvus, etc.))
Key Facts & Figures
75 numeric metrics compared
| Metric | LangChain | LlamaIndex | Ratio |
|---|---|---|---|
| Vector Store Support(count) | 30+ | 50+ | |
| Enterprise Market Share(%) | 65% of LLM framework users | 28% of RAG-focused projects | |
| Setup Time for Basic RAG(minutes) | 25-40 minutes | 5-10 minutes | |
| LLM Provider Integrations(providers) | 50+ | 40+ | |
| Vector Store Integrations(databases) | 30+ | 35+ | |
| Release Frequency(minor releases/year) | 24+ | — | — |
| Monthly NPM/PyPI Downloads(downloads) | 5.2 million | 180,000+ | |
| 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+ | 25+ | |
| Production Adoption Rate(percent) | 70% | — | — |
| Multi-Agent Orchestration Complexity(lines of code) | 150-300 | — | — |
| Documentation Maturity(pages) | 500+ | — | — |
| First Release Date(year) | October 2022 | — | — |
| Pre-built Integrations(count) | 150+ | — | — |
| Official Memory Types(types) | 7 specialized memory types | — | — |
| Documentation Pages (Estimated)(pages) | 500+ | — | — |
| Active Contributors(developers) | 200+ | — | — |
| Number of Integrated LLM Providers(providers) | 25+ providers | — | — |
| GitHub Stars (2026)(stars) | 95,000+ stars | 32,000 | |
| Programming Languages Supported(languages) | Python, JavaScript/TypeScript | — | — |
| Time to Build Basic RAG App(minutes) | 30-60 minutes (with documentation) | — | — |
| Fine-tuning Ease (1-10 scale)(score) | Requires manual setup (6/10) | — | — |
| Cost for Production Deployment (monthly estimate)(USD) | $200-1000+ (depends on LLM provider) | — | — |
| Available Models in Repository(models) | 0 (integrates externally) | — | — |
| Memory Management Features(types) | 6 (Buffer, Summary, Entity, Vector, Knowledge Graph, Multi-window) | — | — |
| Python Package Downloads (Monthly)(downloads) | 8,500,000+ | — | — |
| GitHub Stars(stars) | 85,000+ | 58,000+ | |
| LLM Integrations(providers) | 100+ | 45+ providers | |
| Time to First Agent (minutes)(minutes) | 30-45 minutes | — | — |
| Production Maturity (years since launch)(years) | 3+ years | — | — |
| Built-in Memory Types(types) | 5+ types | — | — |
| Memory Types Available(count) | 7+ | 3 | |
| RAG Retrieval Speed (vs baseline)(% faster) | Baseline (100%) | +25-30% faster | |
| Community Discord Members(members) | ~5,000+ | 18,000+ | |
| Monthly Active Commits(count) | 15,000+ | 3,500+ | |
| LLM Model Integrations(integrations) | 90+ | 70+ | |
| Latest Stable Release Cycle(weeks) | 2-3 weeks | — | — |
| Third-Party Integrations(integrations) | 200+ integrations | — | — |
| Token Efficiency (Tokens Per Task)(% less tokens vs LangChain) | Baseline (100%) | — | — |
| Production Adoption(companies (estimated)) | 2,000+ enterprises | — | — |
| Time to Build Multi-Agent System(hours (estimated)) | 40-60 hours with manual orchestration | — | — |
| Initial Release Date(year) | 2022 | — | — |
| API Stability(breaking changes per year (2024-2026)) | 2-3 breaking changes | — | — |
| Pre-trained Models Available(count) | 50+ LLM integrations | — | — |
| Setup Time (Hello World)(minutes) | 5-10 min | — | — |
| Inference API Latency(milliseconds) | 50-200ms (provider dependent) | — | — |
| Documentation Pages(pages) | 500+ | 500+ | |
| RAG Setup Time (Baseline Task)(minutes) | 25-35 minutes | 10-15 minutes | |
| Document Indexing Speed (1000 PDFs)(seconds) | 120-180 seconds | 80-120 seconds | |
| API Documentation Coverage(%) | 92% (broad but less RAG-focused) | 88% (deeper RAG coverage) | |
| 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 | 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 | |
| Data Connectors(count) | 100+ | 100+ | |
| Minimum Deployment Size(megabytes) | 200 | 200 | |
| Retrieval Strategy Types(strategies) | 6+ (hybrid, fusion, reranking, hierarchical, etc.) | 6+ (hybrid, fusion, reranking, hierarchical, etc.) | |
| Storage Backends(backend types) | 8+ (via supported vector DB integrations) | 8+ (via supported vector DB integrations) | |
| Setup Time (minutes)(minutes) | 120-240 | 120-240 | |
| Supported Data Sources(count) | 100+ data connectors | 100+ data connectors | |
| Query Latency (P95)(milliseconds) | 200-500 | 200-500 | |
| Learning Curve (Hours)(hours) | 8-20 | 8-20 | |
| Production Deployments Reported(count) | 2,000+ | 2,000+ | |
| GitHub Stars (Community Size)(stars) | 32,500+ | 32,500+ | |
| Vector Store Connectors(databases) | 45+ | 45+ | |
| Document Format Support(types) | 12 formats (PDF, DOCX, TXT, JSON, CSV) | 12 formats (PDF, DOCX, TXT, JSON, CSV) | |
| Setup Time (Minutes to First Query)(minutes) | 5-10 minutes | 5-10 minutes |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- Multi-step agent orchestration, chains, memory managementPrimary Use Case FocusRetrieval-augmented generation, document indexing, data querying
- 50+ LLM providers (OpenAI, Anthropic, Ollama, Bedrock, Azure, HuggingFace, etc.)(winner)Supported LLM Integrations40+ LLM providers (similar coverage, slightly fewer options)
- 30+ vector databases (Pinecone, Weaviate, Chroma, Faiss, Milvus, etc.)Vector Store Integrations35+ vector databases (broader vector DB ecosystem support)(winner)
- 85,000+ stars (as of 2026)(winner)Community Activity (GitHub Stars)58,000+ stars (as of 2026)
- Comprehensive but broad (covers many use cases, less focused)Documentation DepthNarrower but deeper RAG-specific documentation(winner)
- Steeper (must understand chains, agents, memory concepts)Learning Curve for RAG TasksGentler (RAG abstractions are more intuitive)(winner)
- Highly mature with LangSmith observability platform(winner)Production Deployment MaturityMature but fewer native observability tools
- Primary Use Case Focus
LangChain
Multi-step agent orchestration, chains, memory management
LlamaIndex
Retrieval-augmented generation, document indexing, data querying
- Supported LLM Integrations
LangChain
50+ LLM providers (OpenAI, Anthropic, Ollama, Bedrock, Azure, HuggingFace, etc.)(winner)
LlamaIndex
40+ LLM providers (similar coverage, slightly fewer options)
- Vector Store Integrations
LangChain
30+ vector databases (Pinecone, Weaviate, Chroma, Faiss, Milvus, etc.)
LlamaIndex
35+ vector databases (broader vector DB ecosystem support)(winner)
- Community Activity (GitHub Stars)
LangChain
85,000+ stars (as of 2026)(winner)
LlamaIndex
58,000+ stars (as of 2026)
- Documentation Depth
LangChain
Comprehensive but broad (covers many use cases, less focused)
LlamaIndex
Narrower but deeper RAG-specific documentation(winner)
- Learning Curve for RAG Tasks
LangChain
Steeper (must understand chains, agents, memory concepts)
LlamaIndex
Gentler (RAG abstractions are more intuitive)(winner)
- Production Deployment Maturity
LangChain
Highly mature with LangSmith observability platform(winner)
LlamaIndex
Mature but fewer native observability tools
Full Comparison
| Attribute | LangChain | LlamaIndex |
|---|---|---|
| Vector Store Support(count) | 30+ | 50+(winner) |
| 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) | — |
| Official Memory Types(types) | 7 specialized memory types | — |
Show 10 more attributesProgramming Languages Supported(languages) Python, JavaScript/TypeScript — LLM Integrations(providers) 100+ 45+ providers Built-in Memory Types(types) 5+ types — Agent Orchestration Complexity Manual agent coordination required — Memory Types Available(count) 7+ 3 Third-Party Integrations(integrations) 200+ integrations — Primary Use Case Optimization(null) RAG and retrieval-augmented systems — Retrieval Strategy Types(strategies) 6+ (hybrid, fusion, reranking, hierarchical, etc.) — Storage Backends(backend types) 8+ (via supported vector DB integrations) — LLM Integration Native (built-in agents) — | ||
| 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(winner) | 28% of RAG-focused projects |
| Setup Time for Basic RAG(minutes) | 25-40 minutes | 5-10 minutes(winner) |
| Multi-Agent Orchestration Complexity(lines of code) | 150-300 | — |
| Time to First Agent (minutes)(minutes) | 30-45 minutes | — |
| Learning Curve Complexity(1–10 scale) | 8/10 (Steep) | 5/10 (Moderate)(winner) |
| RAG Setup Time (Baseline Task)(minutes) | 25-35 minutes | 10-15 minutes(winner) |
Show 1 more attributeLearning Curve (Hours)(hours) 8-20 — | ||
| Production Monitoring Tools(tool availability) | LangSmith (dedicated platform) | Basic logging via LlamaDebug |
| Production Observability Features(null) | Built-in logging, caching, callback handlers | — |
| Production Monitoring/Debugging Tools(features) | Limited (logging integration available) | — |
| LLM Provider Integrations(providers) | 50+(winner) | 40+ |
| Vector Store Integrations(databases) | 30+ | 35+(winner) |
| Primary Language | Python | — |
| Setup Time (Hello World)(minutes) | 5-10 min | — |
| Primary Language Support(count) | Python & JavaScript equally | Python (primary), TypeScript/JavaScript |
| Average Setup Time(minutes) | 2-4 hours | — |
| Setup Time (Minutes to First Query)(minutes) | 5-10 minutes | — |
| Azure OpenAI Integration Quality(native support level) | Community-maintained, requires manual configuration | — |
| Azure OpenAI Integration Depth(level) | Standard (community-maintained) | — |
| Release Frequency(minor releases/year) | 24+ | — |
| Monthly Active Commits(count) | 15,000+(winner) | 3,500+ |
| Community Size(millions of users) | 35,000+ | — |
| Python Package Downloads (Monthly)(downloads) | 8,500,000+ | — |
| Monthly Active Users(billions) | 50,000+ | — |
| Transformers Library Monthly Downloads(downloads) | Not tracked separately | — |
| Production Deployments Reported(count) | 2,000+ | — |
| Microsoft Copilot Integration(native support) | Limited, requires plugins | — |
| Azure/Microsoft Ecosystem Integration(integration level) | Minimal (basic Azure OpenAI support) | — |
| Monthly NPM/PyPI Downloads(downloads) | 5.2 million(winner) | 180,000+ |
| GitHub Stars (2026)(stars) | 95,000+ stars(winner) | 32,000 |
| GitHub Stars(stars) | 85,000+(winner) | 58,000+ |
| Typical Memory Footprint (Loaded State)(MB) | 512-768 MB | — |
| Average Model Download Time(seconds) | N/A (framework only) | — |
| RAG Retrieval Speed (vs baseline)(% faster) | Baseline (100%) | +25-30% faster(winner) |
| Token Efficiency (Tokens Per Task)(% less tokens vs LangChain) | Baseline (100%) | — |
| Inference API Latency(milliseconds) | 50-200ms (provider dependent) | — |
Show 3 more attributesDocument Indexing Speed (1000 PDFs)(seconds) 120-180 seconds 80-120 seconds Minimum Deployment Size(megabytes) 200 — Query Latency (P95)(milliseconds) 200-500 — | ||
| Weekly NPM Downloads(downloads) | 25,000 | — |
| LLM Provider Support(providers) | 100+(winner) | 25+ |
| Pre-built Integrations(count) | 150+ | — |
| Data Connectors/Loaders(connectors) | 200+ | — |
| Data Connectors(count) | 100+ | — |
| Supported Data Sources(count) | 100+ data connectors | — |
| Production Adoption Rate(percent) | 70% | — |
| Documentation Maturity(pages) | 500+ | — |
| First Release Date(year) | October 2022 | — |
| Production Adoption(companies (estimated)) | 2,000+ enterprises | — |
| Initial Release Date(year) | 2022 | — |
| Multi-Agent Native Support(boolean) | No (requires custom code) | — |
| Minimum Python Version(version) | 3.8+ | — |
| Documentation Pages (Estimated)(pages) | 500+ | — |
| API Documentation Coverage(%) | 92% (broad but less RAG-focused)(winner) | 88% (deeper RAG coverage) |
| Active Contributors(developers) | 200+ | — |
| GitHub Stars (Community Size)(stars) | 32,500+ | — |
| Number of Integrated LLM Providers(providers) | 25+ providers | — |
| Available Pre-trained Models(count) | Integrates with external sources | — |
| Pre-trained Models(models) | 100+ integrations | — |
| Native Model Hosting | No (external integration required) | — |
| API Inference Service(null) | No native inference API | — |
| Time to Build Basic RAG App(minutes) | 30-60 minutes (with documentation) | — |
| Time to Build Multi-Agent System(hours (estimated)) | 40-60 hours with manual orchestration | — |
| Fine-tuning Ease (1-10 scale)(score) | Requires manual setup (6/10) | — |
| Cost for Production Deployment (monthly estimate)(USD) | $200-1000+ (depends on LLM provider) | — |
| Available Models in Repository(models) | 0 (integrates externally) | — |
| Memory Management Features(types) | 6 (Buffer, Summary, Entity, Vector, Knowledge Graph, Multi-window) | — |
| RAG Pipeline Support(capability) | Native with document loaders and retrievers | — |
| Enterprise Support Plans Available(options) | Yes (LangChain Plus paid tier) | — |
| Documentation Pages(pages) | 500+ | 500+ |
| Enterprise Support Available | Yes (LlamaIndex Cloud) | — |
| Production Maturity (years since launch)(years) | 3+ years | — |
| Community Discord Members(members) | ~5,000+ | 18,000+(winner) |
| LLM Model Integrations(integrations) | 90+(winner) | 70+ |
| Latest Stable Release Cycle(weeks) | 2-3 weeks | — |
| Enterprise Support Options(available) | Available (LangChain Plus, third-party vendors) | — |
| JavaScript/TypeScript Support Level(level) | Full support (LangChain.js) | — |
| API Stability(breaking changes per year (2024-2026)) | 2-3 breaking changes | — |
| Pre-trained Models Available(count) | 50+ LLM integrations | — |
| Free Hosting Included(boolean) | No (BYO infrastructure) | — |
| Production Observability | Native LangSmith platform with debugging, tracing, evaluation | Limited native tools, integrates with external logging |
| Agent Orchestration Maturity | Advanced (ReAct agents, tool-use, multi-step planning) | Basic (limited agent capabilities) |
| License Type | MIT (open source) | — |
| Vector Database Integrations(integrations) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | — |
| Python Version Support(versions) | 3.8+ | — |
| LLM Model Providers Supported(providers) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | — |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | — |
| Latest Release Activity | 150+ commits/month | — |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | — |
| Setup Time (minutes)(minutes) | 120-240 | — |
| Setup Time(hours) | 20 | — |
| Maximum Embeddings(millions) | Unlimited (via Pinecone/Weaviate) | — |
| Vector Store Connectors(databases) | 45+ | — |
| Document Format Support(types) | 12 formats (PDF, DOCX, TXT, JSON, CSV) | — |
| Hybrid Search Support (BM25 + Dense)(boolean) | Partial (requires custom implementation) | — |
Show 10 more attributes
Show 1 more attribute
Show 3 more attributes
Pros & Cons
10 pros·4 cons across both
LangChain
Pros
- 50+ integrated LLM providers enabling vendor lock-in avoidance
- Advanced agent orchestration with ReAct, tool-use, and multi-step reasoning
- LangSmith observability platform for production monitoring and debugging
- Mature ecosystem with 85,000+ GitHub stars and 10,000+ community projects
- Flexible memory management (conversation history, token counting, summarization)
Cons
- Steep learning curve with many concepts (chains, runnables, agents, memory types)
- RAG capabilities require more boilerplate code compared to LlamaIndex
LlamaIndex
Pros
- 35+ vector store integrations with superior query optimization for RAG
- Intuitive document indexing with automatic chunking and metadata extraction
- Better support for structured data extraction from documents (SQL, JSON schemas)
- Faster time-to-value for RAG pipelines with pre-built templates
- Modular architecture allowing easy swapping of indexing and retrieval strategies
Cons
- Less suitable for non-RAG use cases like pure agent orchestration
- Smaller community ecosystem (58,000 stars) with fewer third-party integrations
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
As an affiliate, we may earn a commission from qualifying purchases at no extra cost to you. Learn more about our affiliate disclosure
Wikipedia
- W
LangChain on Wikipedia (opens in new tab)
Open-source framework for building LLM-powered applications with chains, agents, and memory management.
- W
LlamaIndex on Wikipedia (opens in new tab)
Specialized framework for retrieval-augmented generation with advanced document indexing and structured data querying.
Related Comparisons
12 more to explore
LangChain vs LlamaIndex
softwareLangChain vs LlamaIndex
softwareLlamaIndex vs Semantic Kernel
softwareLlamaIndex vs Pinecone
softwareLlamaIndex vs Weaviate
softwareLlamaIndex vs Hugging Face
softwareLlamaIndex vs Haystack
softwareLangChain vs Semantic Kernel
softwareLangChain vs Haystack
softwareLangChain vs CrewAI
softwareLangChain vs AutoGen
softwareLangChain vs Hugging Face
software
Related Articles
5 articles
- technology
Best Streaming Services in 2026: Top Picks for Every Budget & Interest
Navigating the crowded streaming landscape in 2026 can be overwhelming. We've tested and ranked the best streaming services that offer the most value, from Netflix's massive library to budget-friendly options like Tubi, helping you cut cable and find your perfect entertainment solution.
Read article - technology
Best Live TV Streaming Services & Plans for Spring 2026: Complete Buyer's Guide
Tired of overpaying for cable? Discover the best live TV streaming services and plans for Spring 2026, including YouTube TV's new genre-based packages starting at $55/month. Our comprehensive guide breaks down pricing, channels, and features to help you cut the cord.
Read article - technology
Philo in 2026: Streaming TV Service Review, Pricing & Reddit Community Insights
Explore Philo's evolution heading into 2026, including pricing tiers, channel lineup, and how it compares to competitors like Sling TV. Discover what the r/PhiloTV Reddit community thinks about the service's current offerings and future prospects.
Read article - technology
Best US Fighter Jets 2026: Top American Combat Aircraft Ranked
Discover the most advanced US fighter jets dominating the skies in 2026. From the legendary F-22 Raptor to the versatile F-35 Lightning II, we rank America's best combat aircraft based on performance, stealth, and air superiority capabilities.
Read article - technology
Philo in 2026: Pricing, Lineup & How It Compares to Sling TV
As we head into 2026, Philo continues to position itself as an affordable streaming alternative for cable TV lovers. Discover what Philo offers, how its pricing stacks up against competitors like Sling TV, and what the Reddit community thinks about its future.
Read article
Explore More
Related comparisons and categories