Haystack vs LlamaIndex: Building Enterprise Search Pipelines in 2026

Pros

• Mature enterprise pedigree — battle-tested in large-scale NLP since 2020
• Pipeline-first: drag-and-drop YAML/Python pipeline definitions
• Native Elasticsearch and OpenSearch DocumentStore integration
• Strong extractive QA and summarisation components alongside generative
• Built-in evaluation (SAS, F1, NDCG) for search quality measurement

Cons

• More complex API — component-based pipeline wiring has more boilerplate
• Heavier dependency footprint vs LlamaIndex
• Fewer LLM integrations than LlamaIndex out of the box
• Community smaller than LangChain/LlamaIndex in LLM-native circles

Pros

• Best document ingestion: LlamaParse handles complex PDFs, tables, charts
• Property graph index: build knowledge graphs from documents for multi-hop queries
• Simpler Pythonic API — higher abstraction level than Haystack
• Wider LLM and vector store integrations (50+ supported)
• Active development with frequent feature releases

Cons

• Weaker Elasticsearch/OpenSearch integration vs Haystack
• Less mature extractive QA components
• API changes between versions can require pipeline rewrites
• Enterprise support requires LlamaCloud subscription