LangChain vs LlamaIndex 2026: RAG vs Agents
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
Open-source framework for building LLM-powered applications with composable chains and agent tools.
Enterprise teams building multi-agent AI systems, chatbots with tool use, autonomous workflows, and developers needing production observability
LlamaIndex
Specialized Python library for building RAG applications with efficient data indexing and retrieval.
Data scientists and teams focusing on document Q&A, knowledge base querying, private data retrieval, and RAG-first applications
Quick Answer
AI SummaryLangChain 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.
Our Verdict
AI-assistedChoose 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.
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Choose LangChain if
Best pickEnterprise teams building multi-agent AI systems, chatbots with tool use, autonomous workflows, and developers needing production observability
Choose LlamaIndex if
Data scientists and teams focusing on document Q&A, knowledge base querying, private data retrieval, and RAG-first applications
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Key Differences at a Glance
- Primary Use Case:General LLM orchestration, agents, chains, and workflows vs Retrieval-augmented generation (RAG) and data indexing
- GitHub Stars:✓ LangChain wins(80,000+ vs 35,000+)
- LLM Integrations:✓ LangChain wins(100+ models (OpenAI, Anthropic, Cohere, Llama, etc.) vs 70+ models (OpenAI, Anthropic, Bedrock, Ollama, etc.))
Key Facts & Figures
56 numeric metrics compared
| Metric | LangChain | LlamaIndex | Ratio |
|---|---|---|---|
| LLM Integrations(integrations) | 50+ providers | 45+ providers | |
| Vector Store Support(count) | 30+ | 50+ | |
| Enterprise Market Share(percent) | 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) | 40+ | — | — |
| Vector Store Integrations(count) | 12+ (Pinecone, Weaviate, FAISS, Supabase) | 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(%) | 70% | — | — |
| Multi-Agent Orchestration Complexity(lines of code) | 150-300 | — | — |
| Documentation Maturity(pages) | 500+ | — | — |
| GitHub Stars(stars) | 95,000+ | 35,000+ | |
| 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(count) | 200+ | — | — |
| Number of Integrated LLM Providers(providers) | 25+ providers | — | — |
| GitHub Stars (2026)(count) | 95,000+ stars | — | — |
| Programming Languages Supported(count) | 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+ | — | — |
| LLM Model Integrations(count) | 100+ | 70+ | |
| Memory Types Available(count) | 7+ | 3 | |
| RAG Retrieval Speed (vs baseline)(% faster) | Baseline (100%) | +25-30% faster | |
| Community Discord Members(count) | 45,000+ | 18,000+ | |
| Monthly Active Commits(count) | 15,000+ | 3,500+ | |
| Third-Party Integrations(count) | 200+ integrations | — | — |
| Token Efficiency (Tokens Per Task)(% less tokens vs LangChain) | Baseline (100%) | — | — |
| Production Adoption(companies (estimated)) | 2,000+ enterprises | — | — |
| Documentation Pages(pages) | 500+ tutorials & guides | 500+ | |
| 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 | — | — |
| 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(days) | 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(connectors) | 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) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- General LLM orchestration, agents, chains, and workflowsPrimary Use CaseRetrieval-augmented generation (RAG) and data indexing
- 80,000+(winner)GitHub Stars35,000+
- 100+ models (OpenAI, Anthropic, Cohere, Llama, etc.)(winner)LLM Integrations70+ models (OpenAI, Anthropic, Bedrock, Ollama, etc.)
- 30+ vector databasesVector Store Support50+ vector databases with native optimization(winner)
- Advanced agent types (ReAct, MRKL, OpenAI Assistants API native support)(winner)Agent Framework MaturityBasic agent capabilities, limited agent types
- 7+ memory types (buffer, summary, entity, vector store memory, etc.)(winner)Memory Management3 memory types (chat history, summary, similarity-based)
- 45,000+ active members(winner)Community Size (Discord)18,000+ active members
- Primary Use Case
LangChain
General LLM orchestration, agents, chains, and workflows
LlamaIndex
Retrieval-augmented generation (RAG) and data indexing
- GitHub Stars
LangChain
80,000+(winner)
LlamaIndex
35,000+
- LLM Integrations
LangChain
100+ models (OpenAI, Anthropic, Cohere, Llama, etc.)(winner)
LlamaIndex
70+ models (OpenAI, Anthropic, Bedrock, Ollama, etc.)
- Vector Store Support
LangChain
30+ vector databases
LlamaIndex
50+ vector databases with native optimization(winner)
- Agent Framework Maturity
LangChain
Advanced agent types (ReAct, MRKL, OpenAI Assistants API native support)(winner)
LlamaIndex
Basic agent capabilities, limited agent types
- Memory Management
LangChain
7+ memory types (buffer, summary, entity, vector store memory, etc.)(winner)
LlamaIndex
3 memory types (chat history, summary, similarity-based)
- Community Size (Discord)
LangChain
45,000+ active members(winner)
LlamaIndex
18,000+ active members
Full Comparison
| Attribute | LangChain | LlamaIndex |
|---|---|---|
| LLM Integrations(integrations) | 50+ providers(winner) | 45+ providers |
| LLM Provider Integrations(providers) | 40+ | — |
| Vector Store Support(count) | 30+ | 50+(winner) |
| Vector Store Integrations(count) | 12+ (Pinecone, Weaviate, FAISS, Supabase) | 35+(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) | — |
Show 7 more attributesOfficial Memory Types(types) 7 specialized memory types — LLM Model Integrations(count) 100+ 70+ Memory Types Available(count) 7+ 3 Primary Use Case Optimization(null) RAG and retrieval-augmented systems — Data Connectors(connectors) 100+ — Retrieval Strategy Types(strategies) 6+ (hybrid, fusion, reranking, hierarchical, etc.) — Storage Backends(backend types) 8+ (via supported vector DB integrations) — | ||
| 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(percent) | 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 | — |
| Average Setup Time(days) | 2-4 hours | — |
| Setup Time(minutes) | 20 | — |
| Production Monitoring Tools(tool availability) | LangSmith (dedicated platform) | Basic logging via LlamaDebug |
| Production Observability Features(null) | Built-in logging, caching, callback handlers | — |
| Primary Language | Python (primary) + JavaScript/TypeScript | — |
| Release Frequency(minor releases/year) | 24+ | — |
| Monthly Active Commits(count) | 15,000+(winner) | 3,500+ |
| Azure OpenAI Integration Quality(native support level) | Community-maintained, requires manual configuration | — |
| Community Size(members/stars) | 35,000+ | — |
| GitHub Stars(stars) | 95,000+(winner) | 35,000+ |
| Active Contributors(count) | 200+ | — |
| GitHub Stars (2026)(count) | 95,000+ stars | — |
| Community Discord Members(count) | 45,000+(winner) | 18,000+ |
| 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+ |
| 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%) | — |
| Minimum Deployment Size(megabytes) | 200 | — |
| Weekly NPM Downloads(downloads) | 25,000 | — |
| Production Adoption Rate(%) | 70% | — |
| Python Package Downloads (Monthly)(downloads) | 8,500,000+ | — |
| Transformers Library Monthly Downloads(downloads) | Not tracked separately | — |
| LLM Provider Support(providers) | 100+(winner) | 25+ |
| Data Connectors/Loaders(connectors) | 200+ | — |
| Documentation Maturity(pages) | 500+ | — |
| First Release Date(year) | October 2022 | — |
| Production Adoption(companies (estimated)) | 2,000+ enterprises | — |
| Initial Release Date(year) | 2022 | — |
| Pre-built Integrations(count) | 150+ | — |
| Third-Party Integrations(count) | 200+ integrations | — |
| Pre-trained Models(models) | 100+ integrations | — |
| Multi-Agent Native Support(boolean) | No (requires custom code) | — |
| Minimum Python Version(version) | 3.8+ | — |
| Documentation Pages (Estimated)(pages) | 500+ | — |
| Documentation Pages(pages) | 500+ tutorials & guides | 500+ |
| Number of Integrated LLM Providers(providers) | 25+ providers | — |
| Available Pre-trained Models(models) | Integrates with external sources | — |
| Native Model Hosting | No (external integration required) | — |
| API Inference Service(null) | No native inference API | — |
| Programming Languages Supported(count) | Python, JavaScript/TypeScript | — |
| 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) | — |
| Enterprise Support Available | Yes (LlamaIndex Cloud) | — |
| Learning Curve Complexity(scale (1-10)) | 8/10 (Steep) | 5/10 (Moderate)(winner) |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | — |
| API Stability(breaking changes per year (2024-2026)) | 2-3 breaking changes | — |
| 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.) | — |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | — |
| Latest Release Activity | 150+ commits/month | — |
| Production Observability(feature count) | Dashboard + eval framework + cost tracking | — |
Show 7 more attributes
Pros & Cons
11 pros·5 cons across both
LangChain
Pros
- 80,000+ GitHub stars with 15,000+ monthly commits (highest adoption)
- Advanced agent framework supporting ReAct, MRKL, and OpenAI Assistants API integration
- 100+ LLM integrations including proprietary and open-source models
- 7+ memory types including entity memory and vector store memory for context retention
- Production-ready with LangSmith debugging platform and LangServe for API deployment
- Extensive documentation with 800+ tutorials and example repositories
Cons
- Steeper learning curve due to extensive feature set and multiple abstraction layers
- Slower RAG retrieval performance compared to LlamaIndex (15-20% overhead from abstraction)
LlamaIndex
Pros
- 50+ vector store integrations with native optimization for retrieval quality
- Superior RAG performance with 25-30% faster retrieval speeds than LangChain
- Simpler API design with 40% fewer lines of code needed for basic RAG tasks
- Advanced indexing strategies (tree index, keyword table, hybrid search) optimized for document retrieval
- Native support for structured data querying (SQL, GraphQL, pandas DataFrames)
Cons
- 35,000 GitHub stars with narrower ecosystem compared to LangChain
- Limited agent capabilities—only basic agent types supported, not suitable for complex agentic workflows
- Smaller community (18,000 Discord members) with fewer third-party integrations
Frequently Asked Questions
5 questions
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).
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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