LangChain vs LlamaIndex: Which RAG Framework to Choose in 2026
Use LlamaIndex for document-heavy RAG where ingestion / indexing / retrieval depth matters: our default for production RAG over diverse document types. Use LangChain for broader LLM application work, agent systems, and mixed RAG + non-RAG workloads. They're complementary, not competitive: many production engagements use both (LlamaIndex for retrieval, LangChain / LangGraph for orchestration). For pure RAG use cases, LlamaIndex's depth wins; for mixed use cases, LangChain's breadth wins.
Side-by-side comparison
| Dimension | LangChain | LlamaIndex |
|---|---|---|
| Primary use case | Broad LLM applications | Document-heavy RAG specifically |
| RAG primitives depth | Solid for basic | Best in class |
| Document parsing | Generic | Comprehensive (Unstructured.io integration, custom parsers) |
| Chunking strategies | Recursive, semantic | Recursive, semantic, hierarchical, structure-aware, custom |
| Index types | Vector primarily | Vector, summary, knowledge graph, document |
| Query routing | Limited | Strong (router patterns, query decomposition) |
| Agent support | AgentExecutor (legacy) + LangGraph | Limited (focused on RAG) |
| Integration ecosystem | Largest | Strong for RAG-relevant integrations |
| Observability | Native LangSmith | Bring your own (LangSmith, others work) |
| Best for | Mixed agent + RAG + integration | Document-heavy RAG specifically |
LangChain
Broad LLM application framework. Best for mixed agent + RAG + integration work.
LangChain is the broadest LLM framework: chains, agents, retrievers, memory, integrations with hundreds of providers and tools. The breadth is both its strength and weakness; LangChain has primitives for everything but goes less deep on any specific area. For agent systems specifically, LangGraph (same company) supersedes LangChain's original AgentExecutor. For RAG specifically, LangChain has solid primitives but LlamaIndex goes deeper. LangChain's sweet spot in 2026: broader LLM applications mixing agent + RAG + integration work, especially when LangSmith observability matters.
Pros
- Largest ecosystem of integrations
- Broad LLM application primitives (chains, agents, retrievers, memory)
- LangSmith observability is excellent
- Best for prototyping mixed LLM applications
- Strong RAG primitives for basic retrieval
- Active community and frequent releases
Cons
- Less deep on RAG specifically than LlamaIndex
- AgentExecutor superseded by LangGraph
- Abstractions can hide what's being sent to LLM
- Major version churn (v0.0/v0.1/v0.2/v0.3)
- TypeScript port lags Python
Best for
- → Mixed LLM applications (agent + RAG + integration)
- → Prototyping and rapid integration
- → Multi-provider abstraction
Worst for
- → Document-heavy RAG with complex parsing requirements (LlamaIndex deeper)
- → Production agent systems (use LangGraph instead)
- → Latency-critical paths (abstraction overhead)
Open source (MIT). LangSmith observability paid (free 5K traces/month, $39/seat/month).
Days for prototype; weeks for production-grade applications.
LlamaIndex
RAG-focused framework. Deepest document ingestion / indexing / retrieval primitives.
LlamaIndex is purpose-built for RAG and document-indexing systems. Comprehensive primitives for document ingestion (50+ data loaders), parsing (PDFs, Office, HTML, code, structured data), chunking (recursive, semantic, hierarchical, structure-aware), embedding, indexing (vector, summary, knowledge graph), and retrieval (hybrid, reranking, query routing). Where LangChain is broad, LlamaIndex is deep on RAG specifically. For production document-heavy RAG, LlamaIndex remains our default choice.
Pros
- Best document parsing and ingestion of any RAG framework
- Comprehensive chunking strategy support
- Strong vector store integration ecosystem
- Query routing primitives for complex retrieval
- Multiple index types (vector, summary, knowledge graph)
- Native support for advanced RAG patterns
Cons
- Less general than LangChain: focused on RAG specifically
- API has changed significantly between major versions
- Documentation uneven for advanced patterns
- Production observability requires bringing your own
- TypeScript port lags Python in feature parity
Best for
- → Document-heavy production RAG (PDFs, Office docs, web content)
- → Knowledge management systems requiring sophisticated retrieval
- → Production RAG requiring different chunking strategies per document type
Worst for
- → Pure agent systems without RAG (use LangGraph)
- → Simple chat applications without document retrieval
- → Use cases where document parsing isn't a major part
Open source (MIT). LlamaCloud (managed processing) paid.
Days for prototype; weeks for production document-heavy RAG.
Decision scenarios
Production RAG over 10K legal contracts with diverse document types
LlamaIndex. Document parsing depth and chunking flexibility are exactly what this use case needs.
Production agent system with multi-step state and tool use
Use LangGraph (LangChain ecosystem) for agent orchestration; LlamaIndex for retrieval if RAG is part of the agent. Common production combination.
Prototype LLM application with chat + RAG + integration with Slack
LangChain. Mixed application; LangChain's breadth fits. Move retrieval-heavy parts to LlamaIndex if RAG complexity grows.
Internal Q&A RAG over Notion + Confluence + GitHub
LlamaIndex. Multi-source document RAG benefits from LlamaIndex's depth.
Customer service AI with FAQ retrieval and tool use
LangChain (or LangGraph for agent layer). Mixed use case; LangChain primitives suffice for the simple FAQ retrieval.
Production GraphRAG over enterprise documents
LlamaIndex. Knowledge graph indexing is built into LlamaIndex; LangChain doesn't have native equivalent.
Common questions
Yes: common production pattern. LlamaIndex for retrieval primitives, LangChain or LangGraph for orchestration. The integrations work together.
For production agents, use LangGraph (LangChain ecosystem) rather than LangChain's original AgentExecutor. LlamaIndex isn't focused on agents; if your agent does RAG, integrate LlamaIndex into the LangGraph agent.
Both have comprehensive documentation; both have uneven coverage of advanced patterns. LangChain has more community content (more users, more tutorials). LlamaIndex has more depth on RAG-specific patterns.
Both are production-tested at scale. We've shipped 12+ production RAG systems on LlamaIndex and 11+ production projects on LangChain (mostly migrated to LangGraph for agents).
LlamaCloud is LlamaIndex's managed document processing service (newer). LangSmith is LangChain's observability product (mature). Different categories: LlamaCloud is processing infrastructure, LangSmith is observability. We use LangSmith for observability across both LangChain and LlamaIndex projects.
Per use case. For document-heavy RAG: LlamaIndex. For mixed applications or agent-heavy work: LangChain / LangGraph. For projects with both, we use both with clean integration patterns.
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