LangChain vs LlamaIndex — Which Should You Use?
LangChain and LlamaIndex are the two most widely used open-source frameworks for building LLM applications. Both have evolved significantly since their initial releases and cover a lot of overlapping ground. Here’s a clear-headed comparison based on what each does best in 2026.
What are LangChain and LlamaIndex?
LangChain is a framework for building applications with LLMs. It started as a way to chain together LLM calls and tool use, and has grown into a broad platform covering agents, RAG, evaluation, and production deployment (LangSmith, LangGraph, LangServe).
LlamaIndex (formerly GPT Index) is a data framework for LLM applications. It focuses specifically on connecting LLMs to external data sources — documents, databases, APIs — and querying that data effectively. It’s the go-to choice when “retrieval” is the core challenge.
The core difference: LangChain is a general-purpose LLM application framework. LlamaIndex is a specialised data ingestion and retrieval framework.
Where LlamaIndex excels: RAG and data indexing
LlamaIndex’s core strength is its data layer. If your application needs to:
- Ingest documents in multiple formats (PDF, Word, HTML, Markdown, CSV, databases)
- Chunk and index documents intelligently
- Retrieve the most relevant content for a query
- Synthesise answers from multiple sources
…LlamaIndex has the more mature, more configurable tooling.
Specifically, LlamaIndex is ahead on:
Connectors (Readers). LlamaIndex has 100+ built-in readers for specific data sources (Notion, Confluence, Google Drive, GitHub, databases, S3, Slack). These handle authentication, pagination, and format normalisation. LangChain has document loaders too, but they’re less comprehensive.
Index types. LlamaIndex offers multiple index structures: vector store index (most common), summary index (for sequential summarisation), keyword table index, knowledge graph index. You can choose the right index type for your retrieval pattern, or combine them.
Query engines. LlamaIndex’s query engines handle complex queries: sub-question decomposition (breaking a complex question into sub-questions and combining the answers), recursive retrieval, step-back prompting, HyDE (hypothetical document embeddings). These are production-ready and well-tested.
Response synthesis. Fine-grained control over how retrieved chunks are synthesised into a final answer: create-and-refine, tree summarise, compact, etc.
Where LangChain excels: agents and orchestration
LangChain’s strength is as a general-purpose orchestration layer. LangGraph (LangChain’s graph-based agent framework) is one of the most capable open-source frameworks for building complex multi-agent systems.
Specifically, LangChain is ahead on:
LangGraph. A state-machine framework for building agents with complex control flow: branching, loops, parallel execution, human-in-the-loop interrupts. Production-grade for multi-agent systems.
Tool ecosystem. LangChain has the largest library of pre-built tool integrations (web search, code execution, APIs, databases). Useful for agents that need to call many external services.
LangSmith. LangChain’s observability and evaluation platform. Tracing, evaluation datasets, prompt management, and production monitoring. If you need production LLM observability, LangSmith is one of the best options regardless of which framework you use for building.
Community size. LangChain has the larger community, more Stack Overflow answers, more tutorials, and more blog posts. If you get stuck, you’re more likely to find an existing solution.
The honest weaknesses of each
LangChain’s weaknesses: