LambdaTest
The AI-powered unified enterprise testing cloud for cross-browser and mobile automation.
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.
A completely redesigned orchestration engine where components are connected in a directed acyclic graph, allowing for branching, loops, and complex data flows.
The AI-powered unified enterprise testing cloud for cross-browser and mobile automation.
The orchestration engine for stateful, multi-agent AI conversations and autonomous workflows.
The premier operating system for building, benchmarking, and deploying AI solutions at scale.
Enterprise-scale workflow orchestration powered by generative AI and seamless ecosystem integration.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
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.
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Registry Updated:2/7/2026
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