Haystack - AI SDKs and Libraries Tool

Overview

Haystack is an open-source LLM orchestration framework from deepset-ai for building production-ready retrieval-augmented generation (RAG), question answering, semantic search, and conversational agents. It provides a modular pipeline abstraction that lets developers chain retrievers, readers/generative models, document stores, vector databases, and file converters into custom end-to-end applications. Common use cases include extractive QA over enterprise documents, RAG with LLMs for grounded generation, and conversational retrieval where a dialog layer augments responses with retrieved context. The project is designed for both research prototyping and production deployments: it supports multiple retriever types (sparse and dense), a variety of vector stores and document stores, connectors to hosted LLMs and local transformer models, document ingestion and conversion for PDFs/Office files, evaluation utilities, and REST/streaming APIs for serving pipelines. According to the GitHub repository, Haystack is actively maintained (last commit 2026-01-09) and released under the Apache-2.0 license, making it suitable for commercial and academic use.

GitHub Statistics

• Stars: 23,826
• Forks: 2,546
• Contributors: 307
• License: Apache-2.0
• Primary Language: MDX
• Last Updated: 2026-01-09T15:51:22Z
• Latest Release: v2.22.0

According to the GitHub repository, Haystack has 23,826 stars, 2,546 forks, and 307 contributors, and is licensed under Apache-2.0. The project shows active maintenance (last commit 2026-01-09) and a large contributor base, indicating strong community engagement and ongoing development. Frequent commits, a high star count, and many forks suggest the project is widely adopted and battle-tested.

Installation

Install via pip:

pip install farm-haystack

pip install "farm-haystack[all]"  # installs commonly used extras (vectorstores, search backends)

git clone https://github.com/deepset-ai/haystack.git && cd haystack

docker compose up -d  # start example services using the repository's docker-compose

Key Features

• Modular pipeline abstraction for chaining retrievers, readers/generative models, and preprocessors.
• Supports sparse (BM25) and dense retrievers with embedding-based retrieval workflows.
• Connectors for multiple vector DBs: FAISS, Milvus, Qdrant, Pinecone, Weaviate, Redis (and more).
• Document stores and search backends including Elasticsearch and OpenSearch.
• File conversion and preprocessing for PDFs, DOCX, PPTX, CSV, HTML ingestion.
• Integrations with hosted and local model backends (Hugging Face/transformers, OpenAI, inference APIs).
• Tools for evaluation, benchmarking, and tuning retrieval + reader performance.
• Production deployment support: REST/streaming server, Docker, and Kubernetes deployment patterns.

Community

Haystack has a large, active community—23.8k stars and 307 contributors on GitHub—providing issues, examples, and PRs. Users praise its flexibility for RAG and QA; feedback often cites a learning curve for complex deployments and occasional breaking changes across major releases. Community channels include GitHub issues, the deepset community forums, and example repos and templates in the project for deployment and integrations.

Key Information

• Category: SDKs and Libraries
• Type: AI SDKs and Libraries Tool