A Python framework for building modular AI pipelines, agents, and retrieval-augmented generation systems with transparent control. It allows developers to construct production-ready LLM applications using customizable, model-agnostic components.
Project overview
It provides explicit control over how information is retrieved, routed, and processed before reaching the model, while allowing infrastructure components and models to be swapped without rewriting the system.
Project type
RAG · AI Search
Use cases
Knowledge Q&A · Search & Research
Deployment
Python environment
License
Apache-2.0
Best for
Developers and AI engineers who want to build production-ready RAG applications and custom agent workflows using a Python library.
Key capabilities
Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation.
Integrate with various LLM providers and swap models or infrastructure components without rewriting your system.
Build and share custom components for retrieval, indexing, tool calling, memory, and evaluation, or create your own.
Wrap pipelines and agents with custom logic and expose them through HTTP endpoints or MCP via Hayhooks.
Limitations and risks
The framework collects anonymous usage statistics of pipeline components on initialization, though this behavior is disabled by default.
Building pipelines and custom components requires Python programming; it operates primarily as a library.
Getting started
The framework can be installed using the pip command: pip install haystack-ai.
Alternatives and comparisons
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