{"repo":"deepset-ai/haystack","free":true,"listed":false,"github":"https://github.com/deepset-ai/haystack","clone":"git clone https://github.com/deepset-ai/haystack.git","description":"Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.","language":"Python","stars":26224,"topics":["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"],"license":"Apache-2.0","category":"llm_orchestration_framework","readme_excerpt":"\n
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\n\n# ππβ¨ Haystack 3.0 is out! β¨ππ\n\n### Read the announcement [here](https://haystack.deepset.ai/blog/haystack-3-release)!\n\n## π₯³ π π πͺ
π πΎ π₯ π π\n\n
\n\n[Haystack](https://haystack.deepset.ai/) is an open-source AI orchestration framework for building production-ready LLM applications in Python.\n\nDesign modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Build scalable RAG systems, multimodal applications, semantic search, question answering, and autonomous agents, all in a transparent architecture that lets you experiment, customize deeply, and deploy with confidence.\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Documentation](#documentation)\n- [Features](#features)\n- [Haystack Enterprise: Support & Platform](#haystack-enterprise-support--platform)\n- [Telemetry](#telemetry)\n- [π Community](#-community)\n- [Contributing to Haystack](#contributing-to-haystack)\n- [Organizations using Haystack](#organizations-using-haystack)\n\n\n## Installation\n\nThe simplest way to get Haystack is via pip:\n\n```sh\npip install haystack-ai\n```\n\nInstall nightly pre-releases to try the newest features:\n```sh\npip install --pre haystack-ai\n```\n\nHaystack supports multiple installation methods, including Docker images. For a comprehensive guide, please refer\nto the [documentation](https://docs.haystack.deepset.ai/docs/installation).\n\n## Documentation\n\nIf you're new to the project, check out [\"What is Haystack?\"](https://haystack.deepset.ai/overview/intro) then go\nthrough the [\"Get Started Guide\"](https://haystack.deepset.ai/overview/quick-start) and build your first LLM application\nin a matter of minutes. Keep learning with the [tutorials](https://haystack.deepset.ai/tutorials). For more advanced\nuse cases, or just to get some inspiration, you can browse our Haystack recipes in the\n[Cookbook](https://haystack.deepset.ai/cookbook).\n\nAt any given point, hit the [documentation](https://docs.haystack.deepset.ai/docs/intro) to learn more about Haystack, what it can do for you, and the technology behind.\n\n## Features\n\n**Agents built for production** \nExtend agent behavior with lifecycle hooks (`before_llm`, `before_tool`, `on_exit`, β¦) for guardrails and custom logic, and track `step_count`, `token_usage`, and tool calls out of the box for monitoring and cost control. Get started fast with ready-made agents from [Agent Pack](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/agent_pack) (e.g., a deep research agent, or an advanced RAG agent) or give your own agents progressive skill discovery via `SkillToolset`, so skill descriptions only enter context when needed.\n\n**Built for context engineering** \nDesign flexible systems with explicit control over how information is retrieved, ranked, filtered, combined, structured, and routed before it reaches the model. Define pipelines and agent workflows where retrieval, memory, tools, and generation are transparent and traceable.\n\n**Native Async Support** \nOne `Pipeline` runs synchronously or asynchronously and streams token by token. `Agent` can run concurrent tool calls. \n\n**Modular and customizable** \nUse built-in components for retrieval, indexing, tool calling, memory, and evaluation, or create your own. Add loops, branches, and conditional logic to precisely control how context moves through your pipelines and agent workflows.\n\n**Model- and vendor-agnostic** \nIntegrate with OpenAI, Mistral, Anthropic, Cohere, Hugging Face, Google, Azure OpenAI, AWS Bedrock, local models, and many others. Swap models or infrastructure components without rewriting your system.\n\n**Extensible ecosystem** \nBuild and share custom components through a consistent interface that makes it easy for the community and third parties to extend Haystack and contribute to an open ecosystem.\n\n> [!TIP]\n>\n> Would you like to deploy and serve Haystack pipelines as **REST APIs** or **MCP servers**? [Hayhooks](https://github.com/deepset-ai/hayhooks) provides a simple way for you to wrap pipelines and agents with custom logic and expose them through HTTP endpoints or MCP. It also supports OpenAI-compatible chat completion endpoints and works with chat UIs like [open-webui](https://openwebui.com/).\n\n## Haystack Enterprise: Support & Platform\n\nGet expert support from the Haystack team, build faster with enterprise-grade templates, and scale securely with deployment guides for cloud and on-prem environments with **Haystack Enterprise Starter**. Read more about it in the [announcement post](https://haystack.deepset.ai/blog/announcing-haystack-enterprise).\n\nπ [Get Haystack Enterprise Starter](https://www.deepset.ai/products-and-services/haystack-enterprise-starter?utm_source=github.com&utm_medium=referral&utm_campaign=haystack_enterprise)\n\nNeed a managed production setup for Haystack? The **Haystack Enterprise Platform** helps you build, test, deploy and operate 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