AI Dev FrameworksUpdated June 25, 2026

LangChain vs LlamaIndex

Two leading frameworks for building LLM-powered applications.

LangChain

A general-purpose framework for chaining LLM calls and tools

LlamaIndex

A framework focused on data ingestion and retrieval for LLMs

LangChain score8.0/10

Strengths

Broadest set of integrations (models, vector stores, tools)
LangGraph adds solid support for complex agent workflows
Huge community, examples and third-party tutorials
Flexible enough for almost any LLM app architecture

Trade-offs

API surface is large and has churned across versions
Abstractions can feel heavier than necessary for simple RAG
Debugging deeply chained calls can be tricky without LangSmith

LlamaIndex score8.4/10

Strengths

Purpose-built for RAG — ingestion, indexing and retrieval shine
Clean abstractions for chunking, embeddings and query engines
Great connectors for structured and unstructured data sources
Lighter weight to reach for when the task is "search my data"

Trade-offs

Less mature for general agent/tool-orchestration use cases
Smaller integration catalog outside the retrieval space
Community and third-party examples are less abundant than LangChain’s

Feature-by-feature

Feature	LangChain	LlamaIndex
Core focus	General LLM app orchestration	Data ingestion & retrieval (RAG)
Agent support	LangGraph (mature)	Growing agent support
Integrations	Very broad	Broad, retrieval-focused
Learning curve	Moderate-high	Moderate
Observability	LangSmith	LlamaTrace / integrations
Best for	Complex multi-tool agent apps	RAG-first knowledge apps

The verdict

Choose LlamaIndex when your core problem is retrieval over your own data — it’s leaner and more focused for RAG. Choose LangChain when you’re building broader agentic systems that call many tools and need maximum flexibility.

Choose LangChain if…

Complex agents that orchestrate many tools and steps.

Choose LlamaIndex if…

RAG-first applications built around search over your own data.