Features
- Pipeline-based architecture for NLP workflows
- Production-ready RAG with multiple retrievers
- Evaluation framework for pipeline quality
- Integration with major LLMs and vector stores
Pros
- Production-focused design with strong evaluation tools
- Clean pipeline API with composable components
- Well-documented with clear upgrade paths
Cons
- Python-only framework
- Smaller community than LangChain
- Haystack 2.0 rewrite means some legacy resources are outdated
Overview
Haystack is an end-to-end NLP framework by deepset, designed for building production-grade search and RAG (Retrieval-Augmented Generation) pipelines. It provides a clean, composable pipeline API where components (retrievers, generators, preprocessors) are connected to form complete NLP workflows.
Haystack 2.0 was a ground-up rewrite that introduced a more Pythonic, flexible API. Pipelines are built by connecting components, with each component having defined inputs and outputs. This makes it easy to understand data flow and swap components for experimentation.
The framework includes built-in evaluation tools for measuring pipeline quality (faithfulness, relevance, accuracy), which is critical for production RAG systems where retrieval quality directly impacts user experience.
When to Use
Choose Haystack for production RAG systems where pipeline clarity and evaluation are priorities. It is well-suited for teams that want a structured, component-based approach to building search and question-answering systems.
Getting Started
pip install haystack-aifrom haystack import Pipeline
from haystack.components.generators.chat import OpenAIChatGenerator
pipe = Pipeline()
pipe.add_component("llm", OpenAIChatGenerator(model="gpt-4o"))
pipe.connect("llm")
result = pipe.run({"llm": {"messages": [{"role": "user", "content": "Hello"}]}})