LangChain / LangGraph vs LlamaIndex
Side-by-side comparison of LangChain / LangGraph and LlamaIndex. Pricing, features, best use cases, and honest verdict from a developer who has tested both.
Short answer
LangChain / LangGraph vs LlamaIndex: which should you pick?
LangChain / LangGraph is the better fit for rag, chains, and stateful agent workflows. LlamaIndex is the better fit for rag, chains, and stateful agent workflows. Neither is universally better - the useful answer depends on whether your workflow is closer to LangChain / LangGraph's strengths or LlamaIndex's strengths.
Choose LangChain / LangGraph if
rag, chains, and stateful agent workflows
Choose LlamaIndex if
rag, chains, and stateful agent workflows
Key Takeaways
- +LangChain / LangGraph is better for: ai, framework, python
- +LlamaIndex is better for: ai, framework, rag
- ~Both are ai frameworks tools. Your choice depends on workflow preference and team setup.
LangChain / LangGraph
Most popular LLM framework. 100K+ GitHub stars. Chains, RAG, vector stores, tool use. LangGraph adds stateful multi-agent workflows with cycles and persistence.
LlamaIndex
LLM data framework for connecting custom data sources to language models. Best-in-class RAG, data connectors, and query engines. Python and TypeScript.
Feature Comparison
| Feature | LangChain / LangGraph | LlamaIndex |
|---|---|---|
| Category | AI Frameworks | AI Frameworks |
| Type | SDK / Framework | SDK / Framework |
| Pricing | See website for pricing | See website for pricing |
| Best For | RAG, chains, and stateful agent workflows | RAG, chains, and stateful agent workflows |
| Language / Platform | Python | TypeScript |
| Open Source | No | No |
In Depth
LangChain / LangGraph
LangChain is the most popular framework for building LLM applications, with over 100K GitHub stars. It provides abstractions for chains (sequential LLM calls), RAG (retrieval-augmented generation with any vector store), tool use, and output parsing. LangGraph extends it with stateful, graph-based workflows - agents that can loop, branch, and persist state across interactions. Their latest push is 'Deep Agents' for autonomous coding. LangSmith provides observability and tracing. The ecosystem is massive - integrations with every model provider, vector database, and tool imaginable. I cover LangChain in my AI Agent Frameworks course.
LlamaIndex
LlamaIndex is the go-to framework for connecting your own data to large language models. It provides data connectors (LlamaHub) for ingesting from PDFs, databases, APIs, Notion, Slack, and hundreds of other sources. The indexing layer chunks, embeds, and stores your data in any vector database. Query engines handle retrieval-augmented generation with support for recursive retrieval, sub-question decomposition, and multi-document synthesis. It also includes an agent framework with tool use and multi-step reasoning. Available in both Python and TypeScript (LlamaIndex.TS). If your use case is primarily about making LLMs smarter with your own data rather than building autonomous agents, LlamaIndex is more focused and mature than LangChain for that specific problem.
The Verdict
Both LangChain / LangGraph and LlamaIndex are strong tools in the ai frameworks space. The right choice depends on your workflow. Read the full review of each tool for a deeper dive, or watch the video walkthroughs to see them in action.
