Haystack

HaystackSince 2026-08

Haystack is an open-source NLP and generative AI framework by Deepset, designed for building production-grade Retrieval-Augmented Generation (RAG), neural search, and multi-modal question-answering systems. Built with modular component architecture, Haystack allows engineering teams to assemble and customize search pipelines using diverse document stores, embedding models, and LLMs. With enterprise features like hybrid search (dense vector + sparse BM25 retrieval), advanced cross-encoder re-ranking, and structured pipeline validation, Haystack powers mission-critical search engines at global enterprises.

Haystack 2.0 provides a component-driven pipeline architecture where developers connect discrete nodes (DocumentStores, Embedders, Retrievers, Rankers, and Generators) into clean Directed Acyclic Graphs (DAGs). Pipelines can be serialized to YAML for continuous integration, tested locally, and deployed via REST API with Haystack service templates. Haystack integrates natively with all major vector databases, including Qdrant, Pinecone, Milvus, Weaviate, and OpenSearch, providing full vendor flexibility without vendor lock-in.

LlamaIndexSince 2023-01

LlamaIndex is the premier open-source data framework designed to bridge private, enterprise, and unstructured data with large language models. By providing sophisticated data connectors, automated parser modules, semantic chunking algorithms, and multi-document index structures, LlamaIndex enables developers to build context-augmented LLM applications and autonomous knowledge retrieval engines with minimal boilerplate. From parsing complex multi-page PDF documents and financial spreadsheets to orchestrating complex Agentic RAG workflows that query multiple disparate databases, LlamaIndex handles the complete data ingestion, indexing, and query evaluation lifecycle.

Architected for production scale, LlamaIndex provides seamless abstractions for 160+ vector stores, SQL databases, knowledge graphs, and data loaders through LlamaHub. Its query engine supports hybrid vector-lexical searches, sub-question query decomposition, recursive retrieval, and reranking pipelines. With native support for LlamaParse (a state-of-the-art vision-based document parser) and LlamaCloud (a managed parsing and retrieval service), enterprise engineering teams can deploy enterprise-grade RAG applications with strict accuracy evaluation benchmarks and low token consumption.