Capability · Comparison
Haystack vs LlamaIndex
Haystack and LlamaIndex both target retrieval-augmented generation but approach it from different ends. Haystack (deepset) is a pipeline-oriented framework — you compose nodes into a DAG and the library ships strong production components. LlamaIndex starts from data — it has the largest catalogue of ingestion connectors and index types, plus a growing agent surface. Pick by whether your hard problem is orchestration or data plumbing.
Side-by-side
| Criterion | Haystack | LlamaIndex |
|---|---|---|
| Primary abstraction | Pipeline (DAG of components) | Indexes, retrievers, query engines |
| Data connectors (as of 2026-04) | Good — Docstore, File, cloud adapters | Largest in class — LlamaHub has 300+ connectors |
| Index types | Document store + retriever | Vector, keyword, tree, graph, property graph |
| Agent / tool support | Agents module (tool calling, ReAct) | AgentWorkflow, LlamaAgents, native tool use |
| Production deployment | Hayhooks, deepset Cloud | LlamaCloud, any Python runtime |
| Evaluation | Built-in evaluators, ground-truth comparison | LlamaIndex Evals + integrations with TruLens, Ragas |
| Language | Python | Python + TypeScript |
| Commercial backer | deepset | LlamaIndex Inc. |
Verdict
For a team building a production RAG system from clean inputs, Haystack's pipeline discipline tends to scale better — components are composable and the production story via Hayhooks and deepset Cloud is mature. For a team still wrestling with messy data across many sources, LlamaIndex's connector catalogue is the faster path to a working prototype. Both now offer strong agent surfaces, so the choice is mostly about where your first week of pain will be: orchestration or ingestion.
When to choose each
Choose Haystack if…
- You value explicit pipelines and clear component boundaries.
- Your data sources are already under control — it's the orchestration that's hard.
- You want deepset's commercial support.
- You need multi-tenant, production-grade pipeline serving.
Choose LlamaIndex if…
- Your hard problem is ingesting data from many sources.
- You want to experiment with advanced index types (graph, property graph).
- You prefer Python + TypeScript support.
- You plan to use LlamaCloud for parsing and hosted indexes.
Frequently asked questions
Which has better RAG quality out of the box?
Both can reach the same quality ceiling. Haystack's defaults feel more opinionated; LlamaIndex gives you more levers to pull. The quality of your chunking and retrieval matters more than the framework.
Can I use LangChain tools inside either?
Yes, both have adapters. It's not always the cleanest path but it lets you reuse existing integrations.
Which is better for enterprise search?
Haystack has stronger enterprise-search heritage (deepset started there). LlamaIndex is catching up with LlamaCloud and document parsing products.
Sources
- Haystack (deepset) — accessed 2026-04-20
- LlamaIndex documentation — accessed 2026-04-20