#17 · Primary category: LLM Application Frameworks

haystack

agent-framework agentic-ai agentic-rag agents ai ai-agents context-engineering framework genai generative-ai information-retrieval large-language-models llm mcp multi-agent orchestration python rag retrieval-augmented-generation semantic-search

Open-source AI orchestration framework for building production-ready LLM applications with modular pipelines, agent workflows, and explicit control over retrieval, routing, memory, and generation.

Project last updated:08/29/26

GitHub Stars

26.4K

Forks

3.0K

Contributors

432

License

Apache-2.0

Why we included this project

Haystack is a Python framework for building production LLM applications from small, composable components. You assemble those components into explicit pipelines, so you decide how retrieval, routing, memory, and generation fit together instead of accepting a black-box stack. That control is what makes it a good fit for RAG and agent systems where you need to know exactly what happens at each step of a request. The component model is easy to extend, and the built-in integrations cover vector stores, model providers, and document loaders, so custom logic can sit right next to the standard pieces. It is a solid choice once you are past the prototype stage and need clear seams for testing, observability, and swapping parts as requirements change.

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