Haystack
Open-source AI orchestration framework for building production-ready LLM applications
Founded
2018
Starting Price
$0
Category
Developer Tools
Last updated March 8, 2026
About Haystack
Haystack by deepset is an open-source AI framework for building production-ready AI agents, RAG pipelines, semantic search, and multimodal applications. It provides a modular architecture with explicit control over retrieval, routing, memory, and generation, connecting to major LLM providers and vector databases with no vendor lock-in.
Pros & Cons
Pros
- Fully open-source with no vendor lock-in across LLMs and vector databases
- Highly modular architecture allowing custom components and third-party integrations
- Strong RAG capabilities with comprehensive pipeline building tools
- Production-ready with Kubernetes support, serialization, and monitoring
- Active community and extensive documentation with tutorials
Key Features
Modular Pipeline Architecture
Build AI applications as composable pipelines of retrievers, routers, memory layers, tools, evaluators, and generators with branching and looping flows
RAG Pipeline Builder
Create retrieval-augmented generation systems for question answering, document summarization, content generation, and knowledge management
AI Agent Orchestration
Design production-ready AI agents with standardized tool calling, scalable context engineering, and multi-step decision flows
Multi-LLM Integration
Connect to OpenAI, Anthropic, Mistral, Hugging Face, and more with no vendor lock-in through a unified API
Vector Database Support
Integrate with Weaviate, Pinecone, Elasticsearch, Qdrant, and other vector stores for semantic search and retrieval
Multimodal Processing
Handle text, images, audio, and other modalities for next-generation AI applications beyond text-only workflows
Production Deployment
Kubernetes-ready, cloud-agnostic pipelines with serialization, logging, monitoring, and deployment guides
Pricing
Open Source
$0
- Full framework access
- Community support
- All integrations
- Self-hosted deployment
- GitHub Issues support
Best For
Enterprise RAG Systems
Build retrieval-augmented generation pipelines that ground LLM responses in your organization's documents to reduce hallucinations and improve accuracy
Semantic Search Engines
Create intelligent search systems that understand natural language queries and return contextually relevant results from large document collections
AI-Powered Chatbots
Develop conversational AI agents with access to custom knowledge bases, tools, and multi-step reasoning capabilities
Document Processing Pipelines
Automate document ingestion, classification, summarization, and extraction across various file formats and modalities
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