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Haystack

What it is

Haystack is an end-to-end open-source framework for building applications powered by LLMs, Transformer models, and vector search. It is developed by deepset and designed to handle large-scale RAG and agentic workflows.

What problem it solves

It simplifies the construction of complex LLM pipelines by providing modular components for document loading, indexing, retrieval, and generation. Its "Pipeline" abstraction allows for flexible, DAG-based architectures that can handle non-linear logic.

Where it fits in the stack

Framework

Typical use cases

  • Enterprise RAG: Building search systems over millions of documents.
  • Conversational Agents: Creating chatbots that use tools and access external data.
  • Extracted Metadata: Using LLMs to structure unstructured data from various sources.

Strengths

  • Modular Architecture: Easy to swap out components (e.g., changing from Elasticsearch to Pinecone).
  • Production Ready: Designed with scaling and deployment in mind.
  • Haystack 2.0: Modern, simplified API with better support for complex routing.

Limitations

  • Ecosystem Size: While growing, it has fewer community integrations than LangChain.
  • Transitioning: Users of Haystack 1.x may find the shift to 2.0 requires significant code changes.

When to use it

  • When building production-grade RAG systems.
  • If you prefer a modular, component-based approach to pipeline design.

When not to use it

  • For very simple scripts where a basic API call suffices.
  • If you are already deeply committed to another framework's ecosystem (e.g., LlamaIndex).

Getting started

Installation

pip install haystack-ai

Minimal Python Example

from haystack import Pipeline
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator

prompt_template = "What is the capital of {{country}}?"
pipeline = Pipeline()
pipeline.add_component("prompt_builder", PromptBuilder(template=prompt_template))
pipeline.add_component("llm", OpenAIGenerator())
pipeline.connect("prompt_builder", "llm")

result = pipeline.run({"prompt_builder": {"country": "France"}})
print(result["llm"]["replies"][0])

Licensing and cost

  • Open Source: Yes (Apache 2.0)
  • Cost: Free
  • Self-hostable: Yes

Sources / References

Contribution Metadata

  • Last reviewed: 2026-03-02
  • Confidence: high