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The most advanced RAG framework available. Open-source with Python and TypeScript support.
Get LlamaIndex FreeWhat is LlamaIndex?
LlamaIndex (formerly GPT Index) is an open-source data framework for building LLM applications that are grounded in your own data. While LangChain is a general-purpose LLM framework, LlamaIndex specializes in the hard problems of connecting LLMs to complex, heterogeneous data sources with maximum retrieval accuracy.
By 2026, LlamaIndex has evolved from a simple RAG library into a comprehensive platform for building sophisticated data-driven AI applications: multi-document agents, structured data extraction, streaming responses, evaluation frameworks, and managed cloud services (LlamaCloud).
Key Features: 8.5/10
1. Advanced RAG Pipelines
LlamaIndex offers the most comprehensive RAG toolkit available: 100+ data connectors, advanced chunking strategies (sentence windows, hierarchical), hybrid search (dense + sparse), re-ranking, and response synthesis techniques that maximize accuracy for complex queries.
2. Data Connectors (LlamaHub)
LlamaHub provides 100+ pre-built connectors for loading data from Notion, Slack, GitHub, Confluence, databases, PDFs, and dozens of other sources. Each connector handles the format-specific complexity of ingesting that data type correctly.
3. Agentic RAG
Beyond simple question-answering, LlamaIndex agents can perform multi-step reasoning over data: compare documents, synthesize across sources, generate reports, and execute tool calls based on retrieved context.
4. LlamaCloud (Managed Service)
LlamaCloud provides hosted data ingestion, parsing (including state-of-the-art PDF parsing with LlamaParse), and managed retrieval endpoints—eliminating the DevOps overhead of running your own RAG infrastructure.
Pros
- ✓ Best-in-class RAG capabilities
- ✓ 100+ data source connectors (LlamaHub)
- ✓ Python and TypeScript support
- ✓ LlamaParse: superior PDF/document parsing
- ✓ Active research team and frequent updates
- ✓ Comprehensive evaluation framework
Cons
- ✗ Steeper learning curve than LangChain
- ✗ LlamaCloud pricing adds up for large datasets
- ✗ Smaller ecosystem than LangChain
- ✗ Less general-purpose — RAG-focused
LlamaIndex vs LangChain
| Feature | LlamaIndex | LangChain |
|---|---|---|
| RAG Quality | Best-in-class | Very Good |
| Data Connectors | 100+ (LlamaHub) | 50+ |
| General Purpose | Limited | Excellent |
| JavaScript | ✓ (LlamaIndex.TS) | ✓ (better maintained) |
| Managed Cloud | LlamaCloud | LangSmith (observability only) |
Verdict: Should You Use LlamaIndex?
Yes — if RAG quality is your priority. LlamaIndex's advanced retrieval techniques (re-ranking, hybrid search, sentence window chunking) meaningfully improve accuracy compared to basic RAG implementations. For data-heavy applications where retrieval quality matters most, LlamaIndex is the better choice over LangChain.
Many production teams use both: LlamaIndex for data ingestion and retrieval, LangChain for orchestrating agents and tool use. They're complementary rather than mutually exclusive.
Build Better RAG Applications with LlamaIndex
Free, open-source, Python and TypeScript. The best retrieval accuracy for LLM applications.
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