Haystack has been building RAG pipelines since before retrieval-augmented generation was a buzzword, with deepset originally focused on open-domain question answering well before ChatGPT made the pattern mainstream. Its pipeline architecture is explicitly modular: components for retrieval, ranking, generation, and evaluation connect together in a defined graph, which gives it more structure and predictability than some newer, more improvisational agent frameworks.
It's model-agnostic, working with OpenAI, Anthropic, Hugging Face models, and self-hosted options, and includes built-in evaluation tooling for measuring retrieval and generation quality, a layer that LangChain and LlamaIndex users often have to bolt on separately. Haystack 2.0 added native agent support, extending it from a pure RAG framework into the same general-agent-framework space as its newer competitors.
Being free, open source, and backed by a company, deepset, that also sells a managed cloud platform built on it, Haystack has a sustainable path similar to Qdrant's or Weaviate's: an open core with a commercial managed offering layered on top for teams that don't want to run the infrastructure themselves.