What is Haystack?
As an open-source framework for building LLM applications, Haystack tries to do the important things correctly, rather than doing all the things. Haystack doesn’t have as many first-party integrations as, say, LangChain, but it owns and fully supports the 34 integrations it currently has. Haystack also offers 28 community-contributed integrations to tie the framework to less-popular data and document stores, models, tools, and APIs. I mention integrations before discussing the core framework architecture (see “Haystack concepts” below) because the integrations actually require more development effort than core orchestration capabilities.
In addition to valuing doing things right over doing all the things, Haystack tries to be explicit rather than implicit. That may mean writing more code the first time you create a pipeline, but in return for that extra initial effort you’ll find it much easier to debug, update, and maintain your pipeline. To counter the grind of writing a lot of explicit code, you can create your pipeline graphs visually with deepset Studio, discussed below.
The four major design goals of Haystack are to be technology agnostic, to be explicit (as we just discussed), to be flexible, and to be extensible. The Haystack repo README describes these as follows: