LangChain vs LlamaIndex Compared
Two of the most popular frameworks for building LLM-powered applications. Compare their strengths in retrieval, agents, orchestration, and production deployment.
LangChain and LlamaIndex are the two dominant open-source frameworks for building applications on top of large language models. LangChain positions itself as a general-purpose LLM orchestration layer with broad tool and agent support. LlamaIndex focuses on data indexing and retrieval, making it the specialist choice for RAG pipelines. Both have grown significantly in scope and now overlap in many areas, but their core philosophies remain distinct.
Head to Head
Feature comparison
| Feature | LangChain | LlamaIndex |
|---|---|---|
| Core strength | General-purpose LLM orchestration: chains, agents, tool use, and workflows | Data indexing and retrieval: loaders, chunking, embedding, and query engines |
| RAG capabilities | Flexible RAG via retriever abstractions; requires more manual assembly | Best-in-class RAG with built-in index types, re-ranking, and hybrid search |
| Agent support | LangGraph provides stateful, graph-based agent orchestration with checkpointing | Agent support via workflows and tool-calling; less mature than LangGraph |
| Data connectors | Community-maintained loaders via LangChain Hub; broad but variable quality | LlamaHub offers 160+ data loaders (PDFs, databases, APIs, Slack, Notion, etc.) |
| Production tooling | LangSmith for tracing, evaluation, and monitoring; LangServe for deployment | LlamaTrace for observability; integrates with Arize, Weights & Biases for monitoring |
| Learning curve | Steeper; many abstractions and frequent API changes can be confusing | Gentler for RAG use cases; more opinionated defaults reduce decision fatigue |
| Model support | Supports virtually every LLM provider via a unified interface | Supports all major providers; unified LLM and embedding model interfaces |
| Community size | Larger community; 90K+ GitHub stars and extensive third-party integrations | Growing community; 35K+ GitHub stars with strong focus on data-intensive use cases |
Analysis
Detailed breakdown
The choice between LangChain and LlamaIndex often comes down to your primary use case. If you are building a RAG pipeline—ingesting documents, creating embeddings, and serving grounded answers—LlamaIndex provides a more streamlined experience. Its index abstractions (vector, keyword, knowledge graph) and built-in query pipelines mean you can go from raw documents to a working retrieval system with less boilerplate. If your application is more agent-centric—orchestrating tool calls, managing multi-step workflows, or building autonomous systems—LangChain, particularly LangGraph, is the stronger choice. LangGraph's directed graph approach to agent orchestration, with built-in state management and checkpointing, is well-suited for complex, stateful workflows that need to handle interruptions and human-in-the-loop steps. Both frameworks suffer from rapid API evolution, which can make upgrading painful. LangChain has been criticised for over-abstraction, though recent versions have simplified the core API. LlamaIndex has stayed more focused but is expanding into agent territory, blurring the line. Many production teams use both: LlamaIndex for the retrieval layer and LangChain (or LangGraph) for the orchestration layer.
When to choose LangChain
- You are building complex agent workflows with tool use and multi-step reasoning
- You need stateful orchestration with checkpointing and human-in-the-loop support
- Your application integrates many different tools, APIs, and data sources
- You want LangSmith for end-to-end tracing, evaluation, and monitoring
- You prefer the largest community and widest third-party integration ecosystem
When to choose LlamaIndex
- Your primary use case is RAG and you want the best out-of-the-box retrieval experience
- You need to ingest data from many sources using pre-built connectors (LlamaHub)
- You want advanced retrieval features like hybrid search, re-ranking, and knowledge graphs
- You prefer a more focused, opinionated framework with less abstraction overhead
- You are building a data-intensive Q&A system over large document collections
Our Verdict
FAQ
Frequently asked questions
Yes. A common pattern is to use LlamaIndex for building and querying your document index, then wrap the query engine as a tool within a LangChain or LangGraph agent. This gives you the best of both worlds.
Both are used in production by thousands of companies. However, the rapid pace of API changes means you should pin your dependency versions carefully and budget time for upgrades.
For simple use cases (single prompt, single model), calling the API directly is often simpler and more maintainable. Frameworks add value when you need retrieval pipelines, agent orchestration, or multi-model routing.
Related Content
LangChain vs CrewAI
Compare LangChain with CrewAI for multi-agent orchestration.
RAG vs Fine-Tuning
Decide between retrieval and fine-tuning for your LLM customisation.
What is an AI Agent?
Understand the concept that both frameworks help you build.
Custom Agent Development
How we build production-grade agents using these frameworks.
Not sure which to choose?
Book a free strategy call and we'll help you pick the right solution for your specific needs.