Pipelines: a cleaner abstraction than LangChain chains
Haystack models RAG as a pipeline of components connected in a directed graph. A standard RAG pipeline: Retriever (fetch documents from vector store) → Ranker (re-rank by relevance) → PromptBuilder (format prompt with documents) → Generator (LLM generates answer). Each component is a Python class with `run()` and optional `run_batch()`. Pipelines are defined declaratively or via YAML. This is conceptually simpler than LangChain where chains, agents, and tools have different APIs.
10+ document stores, 5+ retrievers
Haystack supports 10+ document stores: Elasticsearch, OpenSearch, Weaviate, Qdrant, Pinecone, Milvus, Chroma, and in-memory. And 5+ retrieval methods: BM25 (keyword), dense (embeddings), sparse (SPLADE), hybrid (BM25 + dense), and multi-modal. Switching from Pinecone to Qdrant is changing one line: `document_store = QdrantDocumentStore()`. This provider abstraction is cleaner than LangChain's because Haystack enforces a consistent interface.
Production features: caching, batching, streaming
Haystack has production features that LangChain lacks: pipeline-level caching (skip LLM calls for identical queries), batch processing (run 1000 queries in parallel), streaming responses (token-by-token for chat), and OpenTelemetry integration (traces, metrics, logs). These are not afterthoughts: they are built into the pipeline execution model. For production RAG at scale, these features matter more than the number of integrations.
Haystack 2.0: the rewrite that fixed everything
Haystack 2.0 (2024) was a ground-up rewrite with a cleaner API. Components are now Pydantic models with typed inputs/outputs. Pipelines can be serialized to YAML and version-controlled. The rewrite broke backward compatibility but fixed the 3 years of API cruft. If you used Haystack 1.x, the migration is significant but worth it. If you are starting fresh, use 2.x.
Haystack vs LangChain vs LlamaIndex
Haystack: production RAG, stable API, pipeline abstraction. Best for production document QA. LangChain: most integrations, most community, messiest API. Best for prototyping and complex agent workflows. LlamaIndex: best for data ingestion and indexing, RAG over structured data. Best for building search over diverse data sources.
