⚡ TL;DR — 30-Second Verdict
Choose LlamaIndex if your primary use case is RAG over documents — it has the most sophisticated data connectors, index types, and query engines for this specific problem. Choose Haystack if you need a more general-purpose NLP pipeline that covers retrieval, summarization, and generation in a single framework. LlamaIndex wins for document-centric RAG; Haystack for broader NLP pipelines.
Quick Comparison
| Feature | LlamaIndex | Haystack |
|---|---|---|
| Primary focus | Document indexing + RAG | NLP pipelines + search + RAG |
| Data connectors | 100+ loaders (PDF, Notion, DB, etc.) | 30+ integrations |
| Index types | Vector, Tree, List, Keyword, KG | Dense + sparse retrieval |
| Pipeline flexibility | Query pipelines | Declarative pipeline YAML/Python |
| Agent support | Full agent framework | Agentic pipelines (newer) |
| Observability | Phoenix, Arize integrations | Built-in tracing |
| Enterprise features | LlamaCloud (paid) | Haystack Enterprise (paid) |
What Is LlamaIndex?
LlamaIndex is purpose-built for RAG and data-over-LLM workflows — it does this job better than LangChain. If your primary use case is document Q&A, knowledge base search, or structured data querying with LLMs, LlamaIndex's data connectors, index types, and query engines are significantly more powerful. Use LangChain for general orchestration; use LlamaIndex when the data layer is complex.
— AI Nav Editorial Team on LlamaIndex
→ Read the full LlamaIndex review
What Is Haystack?
A well-regarded project with 17k+ stars, Haystack has proven itself in production deployments. Recommended when your primary need is grounding LLM responses in your own document corpus. The vector storage integrations are comprehensive, though you'll want to benchmark retrieval quality on your specific documents before committing.
— AI Nav Editorial Team on Haystack
→ Read the full Haystack review