Haystack is an open-source Python framework for building production-ready LLM applications. It provides a modular, component-based architecture where data processing steps (converters, embedders, retrievers, generators, evaluators, etc.) are connected into pipelines or agents. The framework includes over 100 built-in components, support for 50+ integrations (OpenAI, Anthropic, Azure, HuggingFace, etc.), async execution, streaming, serialization, evaluation, and observability. It is designed for RAG, semantic search, question answering, autonomous agents, and multimodal applications. The code is well-structured with consistent patterns (component decorator, lifecycle methods, serialization protocols), extensive test coverage, and security hardening (fuzzing, sandboxed Jinja2, deserialization allowlists).

This is a monorepo of 80+ Haystack integrations maintained by deepset, extending the Haystack framework with production-ready components for RAG, search, and agent pipelines. Each integration is a Python package wrapping an external service (AWS, OpenAI, Cohere, Jina, Brave, MongoDB, Qdrant, Azure, etc.) or providing specialized NLP/agent functionality. The code is library code, not research code — every component follows the Haystack component contract: @component decorator, default_to_dict/from_dict serialization, lazy warm_up, run/run_async, close_async, and Secret-based authentication. The dominant architecture is a collection of thin-to-medium wrappers unified by Haystack's pipeline abstractions, with consistent FilterPolicy (REPLACE/MERGE) document filtering and output type declarations across all integrations.