The Verdict
LlamaIndex specializes in connecting LLMs to your data — documents, databases, APIs, and knowledge bases. While LangChain is a general-purpose AI framework, LlamaIndex focuses specifically on data ingestion, indexing, and retrieval. For building RAG applications, knowledge bases, and data-grounded AI assistants, LlamaIndex provides more specialized and often better tools than the alternatives.
What It Does
LlamaIndex provides data connectors (loading from 100+ sources), indexing strategies (vector, keyword, graph), query engines for retrieval, and response synthesis. LlamaCloud adds managed indexing, parsing, and retrieval as a service. The framework supports Python and TypeScript with integrations for all major LLM providers.
What We Liked
- Data focus: Purpose-built for connecting LLMs to data, with more indexing strategies and retrieval options than general frameworks.
- Data connectors: 100+ connectors for PDFs, databases, APIs, Slack, Notion, Google Drive, and more.
- LlamaCloud: Managed parsing and indexing eliminates the infrastructure work of maintaining vector databases and embedding pipelines.
What We Didn’t Like
- Narrower scope: Unlike LangChain, LlamaIndex does not handle agent workflows, tool use, or general LLM orchestration well.
- LlamaCloud pricing: The managed service at $50/month is expensive for small projects that could use open-source tools.
- API churn: Like LangChain, frequent API changes between versions require ongoing maintenance.
Pricing Breakdown
Open-source framework is free. LlamaCloud starts at $50/month for managed indexing and retrieval. Enterprise pricing includes dedicated infrastructure and support.
The Bottom Line
LlamaIndex is the best framework specifically for RAG and knowledge applications. If your primary need is connecting an LLM to your data, LlamaIndex provides better tools than LangChain for that specific task. For broader AI application development including agents and tool use, LangChain remains more versatile.
