Haystack
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
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What is Haystack?
Haystack is an open-source AI orchestration framework built by deepset that enables Python developers to build production-ready AI agents, multimodal applications, and advanced RAG (Retrieval-Augmented Generation) systems. The framework provides modular and customizable building blocks for creating agentic, context-engineered AI systems designed for real-world production environments.
Key features include modular pipeline architecture with retrievers, routers, memory layers, tools, evaluators, and generators that can be tested and replaced independently; full control over context engineering managing how information moves through systems from retrieval to tool use to memory and model execution; integration with 90+ model and technology providers including OpenAI, Anthropic, Mistral, Hugging Face, Weaviate, Pinecone, and Elasticsearch; built-in support for AI agents with standardized tool calling and scalable context engineering; advanced RAG pipelines with hybrid retrieval and self-correction loops; multimodal capabilities including image processing and audio transcription; and deployment options as REST APIs or MCP servers via Hayhooks. The framework is Kubernetes-ready, cloud-agnostic, and supports serializable pipelines.
Haystack is designed for AI developers and engineers creating AI systems that need to be accurate, modular, and transparent in enterprise environments. It's used by organizations including Airbus, The Economist, NVIDIA, Comcast, Apple, Netflix, Intel, Lufthansa, the European Commission, and over 24,000 GitHub stars. The framework supports everything from agent workflows to advanced RAG, multimodal applications, conversational AI, content generation, text-to-SQL pipelines, and document processing flows.
Haystack pricing
Pricing model: Freemium
Haystack offers three tiers: (1) Haystack Open Source (OSS/Community Edition) is 100% free and open source, self-supported, with pip install haystack-ai. (2) Haystack Enterprise Starter provides enterprise support with direct access to Haystack core maintainers, 4 hours/month remote technical consultation, priority updates, early access to select features, email support, and best practice pipeline templates for Lightweight RAG, Advanced RAG, Agentic RAG, Multimodal pipelines, and Multi-Agent pipelines with MCP. Pricing is flexible based on company size. (3) Haystack Enterprise Platform is a full-stack AI development toolset with visual pipeline editor, robust template library, iterative testing tools, centralized resources and security, cloud or on-prem deployment with autoscaling. A free trial (Haystack Enterprise Platform Trial) is available. Additionally, deepset AI Platform offers a free Studio plan with 1 workspace, 1 user, 100 pipeline hours, 50 files (max 10MB each), 2 development pipelines, cloud deployment, and community Discord support. Enterprise plan is custom-priced with unlimited workspaces, users, files, development and production pipelines, cloud or custom deployment, and dedicated account team with solution engineers and private Slack channel.
Haystack pros
- Open source with no vendor lock-in
- Modular architecture allowing independent component testing and replacement
- Full control over context engineering and data flow
- Integration with 90+ model providers including OpenAI, Anthropic, Mistral, Hugging Face
- Production-ready from day one with enterprise reliability
- Kubernetes-ready and cloud-agnostic deployment
- Serializable pipelines for easy deployment
- Built-in tracing, logging, and evaluation tools for continuous improvement
- Supports AI agents with standardized tool calling
- Advanced RAG with hybrid retrieval and self-correction loops
- Multimodal capabilities including image processing and audio transcription
- REST API and MCP server deployment via Hayhooks
- Active community with 4,000+ Discord members and 24,000+ GitHub stars
- Clean architecture with breaking change policy for production stability
- Works from prototype to production with same composable building blocks
Haystack cons
- Primarily designed for Python developers only
- Requires technical expertise to build and maintain pipelines
- Self-supported open source version lacks dedicated enterprise support
- Learning curve for context engineering concepts
- GPU setup required for optimal performance with some components
- Enterprise support requires contact for custom pricing
- No built-in visual pipeline editor in open source version
- API costs from external model providers not included
Frequently asked questions about Haystack
What is Haystack used for?
Haystack is used for building production-ready AI agents, multimodal applications, and advanced RAG (Retrieval-Augmented Generation) systems. It supports agent workflows, retrieval over millions of documents, text-to-SQL pipelines, document processing flows, conversational AI chatbots, semantic search, question answering, summarization, and content generation. Developers use it to create modular pipelines that control retrieval, ranking, memory, tools, and generation with full transparency and flexibility.
Is Haystack free to use?
Yes, Haystack open source is 100% free and open source (Community Edition). You can install it with pip install haystack-ai and build AI applications without paying. The framework is self-supported through the community on Discord and GitHub. For teams needing enterprise support, Haystack Enterprise Starter and Haystack Enterprise Platform offer paid options with dedicated support, templates, and deployment guides.
Which LLM providers does Haystack integrate with?
Haystack integrates with 90+ model and technology providers including OpenAI (GPT models), Anthropic (Claude), Mistral, Hugging Face Transformers, Cohere, NVIDIA, Amazon Bedrock, Google Gemini, and Ollama. It supports both cloud-hosted and local options. The framework has standardized interfaces for generators so you can swap models without rewriting your system.
What is context engineering in Haystack?
Context engineering is Haystack's approach to AI system design that focuses on explicit control over how contextual information is retrieved, structured, routed to language models, and evaluated after generation. Haystack gives developers full control over how context is retrieved, structured, routed, evaluated, and improved over time. This allows building AI systems with transparent data flow, tool usage, and configurable reasoning processes, addressing the fact that modern AI systems typically fail because context is not well managed rather than because of the model.
How do I deploy Haystack to production?
Haystack pipelines are serializable, cloud-agnostic, and Kubernetes-ready. You can serve pipelines as REST APIs or MCP servers using Hayhooks. The framework includes logging, monitoring, and deployment guides to support production workloads. You can run Haystack in your own infrastructure and integrate with enterprise architectures. For enterprise deployments, Haystack Enterprise Platform provides scalable cloud or on-prem deployment with autoscaling capabilities.
Can I build AI agents with Haystack?
Yes, Haystack supports building production-ready AI agents with standardized tool calling and scalable context engineering. You can create agents that use tools to gather information, perform actions, and interact with external systems. Branching and looping pipelines give you full control over complex, multi-step decision flows. The framework includes an Agent component that works with chat generators and tools like web search.
What vector databases work with Haystack?
Haystack integrates with multiple vector databases including Weaviate, Pinecone, Elasticsearch, OpenSearch, and has a built-in InMemoryDocumentStore for quick prototyping. The modular architecture allows you to swap vector databases without rewriting your system. There are integrations available for most popular vector database providers.
How do I evaluate my Haystack pipelines?
Haystack provides built-in tracing, logging, and evaluation tools to monitor retrieval quality, latency, tool usage, and outputs. You can use these tools to iteratively refine your system and improve performance continuously. The framework supports evaluation of retrieval quality and generation quality, allowing you to monitor and optimize your pipelines in production environments.
What is the difference between Haystack OSS and Haystack Enterprise?
Haystack OSS (Community Edition) is 100% open source and self-supported through Discord and GitHub communities. Haystack Enterprise Starter adds enterprise support with direct access to Haystack core maintainers, 4 hours/month technical consultation, best practice pipeline templates, priority updates, and early access to features. Haystack Enterprise Platform is a full-stack AI development toolset with visual pipeline editor, robust template library, testing tools, centralized security, and scalable deployment options. Enterprise pricing is based on organization size.
Who uses Haystack in production?
Thousands of organizations use Haystack including public sector leaders like the European Commission and Global 500 enterprises like Airbus, Intel, NVIDIA, Lufthansa, Netflix, Apple, Infineon, Alcatel-Lucent Enterprise, BetterUp, Etalab, Sooth.ai, Lego, The Economist, Oxford University Press, the German Federal Ministry of Research Technology and Space (BMFTR), Manz Verlag, and the German Armed Forces. The framework has over 24,000 GitHub stars and 4,000+ community members on Discord.