The modular Python framework for building customizable LLM-powered applications and production-ready RAG pipelines.
Haystack 2.x by deepset is a premier open-source Python framework designed for the industrial-grade orchestration of Large Language Models (LLMs). Unlike more abstract frameworks, Haystack prioritizes a modular, component-based architecture using a Directed Acyclic Graph (DAG) system to connect Document Stores, Retrievers, and Generators. This architecture allows developers to build complex RAG (Retrieval-Augmented Generation) pipelines, semantic search engines, and autonomous agents with granular control over data flow. In 2026, Haystack remains a leader for enterprise-level AI solutions due to its emphasis on 'production-readiness'—offering robust evaluation tools, metadata filtering, and native support for diverse vector databases like Pinecone, Milvus, and Weaviate. While the core library is free and open-source, deepset provides an enterprise managed platform (deepset Cloud) that simplifies deployment, observability, and management of these pipelines. Its focus on performance optimization and transparency makes it the tool of choice for architects who require deterministic behavior in non-deterministic AI environments.
Haystack 2.
Explore all tools that specialize in retrieval-augmented generation (rag). This domain focus ensures Haystack by deepset delivers optimized results for this specific requirement.
Explore all tools that specialize in semantic document search. This domain focus ensures Haystack by deepset delivers optimized results for this specific requirement.
Explore all tools that specialize in multi-hop question answering. This domain focus ensures Haystack by deepset delivers optimized results for this specific requirement.
Explore all tools that specialize in agentic tool use. This domain focus ensures Haystack by deepset delivers optimized results for this specific requirement.
A completely redesigned orchestration engine where components are connected in a directed acyclic graph, allowing for branching, loops, and complex data flows.
Combines dense vector retrieval with classic keyword-based BM25 retrieval using reciprocal rank fusion.
Native components for processing and retrieving images and audio alongside text data.
Advanced query-time filtering based on document metadata (tags, dates, categories) within the vector database.
Built-in tools to measure Precision, Recall, and Faithfulness of LLM responses using 'LLM-as-a-judge' patterns.
Simple Python decorator pattern to turn any custom code into a Haystack-compatible pipeline component.
Native handling of API keys and sensitive environment variables through a standardized Secret class.
Install Haystack core via pip: 'pip install haystack-ai'
Define your Document Store (e.g., InMemoryDocumentStore or ElasticsearchDocumentStore)
Initialize Preprocessors to clean and split your raw data into manageable chunks
Configure an Embedder (e.g., SentenceTransformers or OpenAIEmbedder) to convert text to vectors
Write your documents to the Document Store using the indexing pipeline
Define the Retrieval component (e.g., BM25 or EmbeddingRetriever) to find relevant context
Create a PromptBuilder template to structure the context for the LLM
Initialize the Generator (LLM) such as OpenAIGenerator or HuggingFaceLocalGenerator
Construct the Pipeline by connecting components: retriever -> prompt_builder -> generator
Execute the pipeline and run evaluation metrics like RAGAS or Haystack's built-in evaluators
All Set
Ready to go
Verified feedback from other users.
"Users praise its modularity and production-readiness, often citing it as more stable and transparent than LangChain, though it has a slightly steeper learning curve for beginners."
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