LangChain vs LlamaIndex

Framework-level comparison for builders choosing an LLM app and RAG stack.

Comparison — 2 PROJECTS

Updated regularly

Quick verdict

LangChain is broader; LlamaIndex is more retrieval-centered.

Use LangChain when the product is an LLM workflow with tools, agents, and many integrations. Use LlamaIndex when the product is primarily about connecting private data to an LLM through retrieval and query pipelines.

Pick LangChain for orchestration

It is the better default when agents, tools, chains, and provider integrations are central.

Pick LlamaIndex for RAG

It is usually the cleaner starting point when documents, indexes, and retrieval quality dominate.

Decision notes

Which one should you use?

Use these notes as a starting point, then validate the choice against your own deployment, data, evaluation, and maintenance constraints.

If your app starts with workflow complexity
Start with LangChain and keep the abstraction surface intentionally small.
If your app starts with private data
Start with LlamaIndex and spend more time on ingestion, retrieval, and evaluation.
If you need both
Use one as the primary layer and integrate the other deliberately instead of mixing patterns everywhere.

At a glance

Side-by-side summary

Metric	LangChain	LlamaIndex
Primary focus	LangChain is a broad framework for building LLM applications with chains, agents, tools, model integrations, and orchestration patterns.	LlamaIndex focuses on connecting private data to LLMs through ingestion, indexing, retrieval, query engines, and knowledge workflows.
Best for	LLM apps with agents, tools, workflows, and many external integrations	RAG applications over internal docs, knowledge bases, and custom datasets
Main strength	Large ecosystem of integrations across models, tools, vector stores, and application services	Strong retrieval, indexing, document ingestion, and knowledge-base primitives
Main tradeoff	Broad API surface can feel heavy for narrow retrieval-only projects	Less natural as the only abstraction for broad agent/tool-heavy applications
Repository	langchain-ai/langchain	run-llama/llama_index
License	MIT	MIT

Project

LangChain

Application framework

LangChain is a broad framework for building LLM applications with chains, agents, tools, model integrations, and orchestration patterns.

Open project

Strengths

• Large ecosystem of integrations across models, tools, vector stores, and application services
• Strong fit for agentic workflows, tool calling, and multi-step orchestration
• Good choice when you need one framework around many LLM application patterns

Limitations

• Broad API surface can feel heavy for narrow retrieval-only projects
• Production apps need discipline around abstractions, tracing, and version changes
• Teams may need to choose a smaller subset of the framework to avoid complexity

Best for

• LLM apps with agents, tools, workflows, and many external integrations
• Teams standardizing on one orchestration layer across several app types
• Prototypes that may later expand beyond simple document retrieval

Repository: langchain-ai/langchain

Stars: 134.9k

Forks: 22.3k

Language: Python

License: MIT

Updated: Apr 25, 2026

Project

LlamaIndex

Data framework

LlamaIndex focuses on connecting private data to LLMs through ingestion, indexing, retrieval, query engines, and knowledge workflows.

Open project

Strengths

• Strong retrieval, indexing, document ingestion, and knowledge-base primitives
• Clear fit for RAG systems where data connection is the center of the product
• Useful abstractions for query engines, retrievers, and document pipelines

Limitations

• Less natural as the only abstraction for broad agent/tool-heavy applications
• Still requires architecture choices around storage, evaluation, and deployment
• Teams building many non-RAG workflows may need additional orchestration pieces

Best for

• RAG applications over internal docs, knowledge bases, and custom datasets
• Search, question answering, and document intelligence products
• Teams that want retrieval/data abstractions to be the primary framework layer

Repository: run-llama/llama_index

Stars: 48.9k

Forks: 7.3k

Language: Python

License: MIT

Updated: Apr 26, 2026

Decision guide

How to choose

Choose LangChain

Your roadmap includes agents, tools, many model providers, multi-step workflows, and broad integrations.

Choose LlamaIndex

Your roadmap is mostly document ingestion, indexing, retrieval, and query interfaces over custom knowledge.

Avoid both as magic

Neither replaces careful chunking, evaluation, observability, and deployment design for production RAG.

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FAQ

Frequently asked questions

Is LangChain better than LlamaIndex?

Not universally. LangChain is broader for LLM app orchestration, while LlamaIndex is more focused on data ingestion, indexing, and retrieval. The better choice depends on whether workflow orchestration or private-data retrieval is the core problem.

Can LangChain and LlamaIndex be used together?

Yes. Some teams use LlamaIndex for retrieval and LangChain for orchestration, but combining both increases complexity. It works best when each tool has a clear boundary.