Haystack
Python · Apache-2.0
Haystack is an enterprise-grade framework for RAG and search applications, covering document processing, retrieval, generation, and evaluation end to end.
ragretrievalllmpython
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Overview
Haystack vs LlamaIndex: Which RAG framework fits your stack
Haystack (2018, deepset) leans pipeline-first and reads like a research codebase. LlamaIndex (2022) shipped in the LLM wave with friendlier abstractions and a much larger community. We compare index abstractions, retrievers, chunking, evaluation, and ecosystem so you can tell whether you want depth or speed.
Projects Compared
LlamaIndex
Python · MIT
LlamaIndex is a data framework for building LLM applications. It provides data connectors, indexing, query engines, and agent workflow orchestration — a core tool in the RAG ecosystem.
ragdata-frameworkindexingquery-enginepython
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Feature Comparison
| Best for | Haystack | LlamaIndex |
|---|---|---|
| Time to first chunk | Docs walk through components one by one. First tutorial shows InMemoryDocumentStore + BM25Retriever in ~50 lines, but you need to grasp the Pipeline / Component / Joiner model first. | Quickstart is literally "load a PDF and ask a question in five minutes." Abstractions hide the vector-store plumbing, so most days you never see it. |
| Retrieval toolbox | BM25, dense embeddings, cross-encoder rerankers, and hybrid retrieval (BM25 + dense, weighted) all ship as composable Pipeline nodes. Reads like a research codebase because it basically is one. | Vector indexes are the default. Keyword tables, fusion retrieval, and auto-merging retrieval all exist, but you usually write a few lines of glue to stitch them together. |
| Evaluation built in | deepset treats RAG evaluation as a first-class concern. Evaluator nodes plug into your Pipeline to score retrieval accuracy, answer faithfulness, and context relevance. Docs show how to dump everything to pandas. | Evaluation came later. llama-index ships a module for retrieval hit rate and answer correctness, but pairing with community tools (RAGAS, DeepEval) takes adaptation work. |
| Integration and deployment | deepset sells the commercial Cloud / Studio editions and ships a Docker image plus Helm chart for self-hosting. There are official integration docs for AWS and Azure. | Integrations are mostly community-maintained (LangChain, Pydantic, FastAPI work fine), but there is no first-party enterprise deployment path — you wire it up yourself or use a partner. |
| Who should pick this | Teams doing serious RAG research who need to inspect and tune retrieval separately, want hybrid or cross-encoder rerank, and have ML engineers willing to read source code. | Application developers who need "upload documents + ask questions" wired into a product fast, and care more about delivery speed than retrieval internals. |
GitHub Stats
| Metric | Haystack | LlamaIndex |
|---|---|---|
| Stars | 26.2k | 51.5k |
| Forks | 3.0k | 7.9k |
| Language | Python | Python |
| License | Apache-2.0 | MIT |
| Last commit | August 10, 2026 | August 9, 2026 |
Which one should you choose?
Choose based on your primary workflow, language ecosystem, and integration needs. Review each project's documentation and recent GitHub activity before adopting it in production.
Frequently asked questions
Which one should I pick, Haystack or LlamaIndex?
Depends on your team. Pick Haystack if you need hybrid retrieval, cross-encoder reranking, or reproducible evaluation, and your team reads ML code. Pick LlamaIndex if you need the shortest path from idea to demo, and your team values community answers over fine-grained control.
Can the two be used together?
Yes. Haystack provides the retrieval pipeline; LlamaIndex provides QueryEngine abstractions and Index persistence. A common pattern is Haystack for retrieval and LlamaIndex for query rewriting plus synthesis, or vice versa. The cost is double dependencies and two upgrade cadences.
Which is better for multimodal RAG (images, tables)?
Roughly even. Haystack converters (pypdf, unstructured, etc.) handle PDF, images, and tables. LlamaIndex ships MultiModalVectorStoreIndex and SimpleDirectoryReader for the same set. The real differentiator is how your retriever couples to document types.
Which is easier to deploy in production?
Haystack has the deepset Cloud / Studio managed option; self-host uses Docker or Helm. LlamaIndex has no first-party commercial product, so most teams wrap it in FastAPI / Next.js themselves, or hook it into LangChain Serve.