Haystack
What is Haystack?
Haystack is an open-source AI orchestration framework for developers who need explicit control over retrieval, routing, memory, tools, evaluation, and generation in modular pipelines. It supports advanced RAG, multimodal apps, and agent workflows, with interchangeable components and inspectable context flow. Haystack integrates with OpenAI, Anthropic, Mistral, Hugging Face, Weaviate, Pinecone, and Elasticsearch, and is used by Zeit Online, Lufthansa Industry Solutions, NVIDIA, Airbus, AWS, and Comcast.
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At a glance
- Haystack is best for AI developers who need transparent, modular pipelines for agents and RAG.
- Yes — Haystack can serve pipelines as REST APIs or MCP servers via Hayhooks.
What does Haystack do?
Haystack handles the orchestration layer for AI applications by turning retrieval, routing, memory, tools, evaluation, and generation into explicit pipelines. That modular setup lets teams inspect each step, swap components independently, and keep context flow intentional instead of hidden inside prompts. It also supports multimodal applications, agent workflows, and advanced RAG patterns, so the same framework can cover document processing, text-to-SQL, and tool-using assistants. At scale, Haystack is built for production environments: the project has 25.3k GitHub stars, supports 126 integrations, and is used by teams such as Zeit Online, Lufthansa Industry Solutions, NVIDIA, Airbus, AWS, and Comcast. Pipelines are serializable, cloud-agnostic, and Kubernetes-ready, and Hayhooks can serve them as REST APIs or MCP servers. Haystack also supports self-hosting, so teams can run it in their own environment while using tracing, logging, and evaluation to monitor and improve systems over time.
Why use Haystack?
- Its explicit pipeline design makes context flow inspectable, which helps teams debug and tune AI behavior without guessing.
- The modular architecture lets you replace retrievers, generators, or model providers without rewriting the whole system.
- Hayhooks can expose pipelines as REST APIs or MCP servers, so production deployment fits existing service architectures.
- Tracing, logging, and evaluation are built into the workflow, making continuous improvement part of the development loop.
Who is Haystack for?
- AI developers who need explicit control over retrieval, memory, and tool use.
- Platform engineers who want cloud-agnostic pipelines they can run in their own environment.
- Teams building enterprise AI systems that need observability and iterative evaluation.
- Developers creating multimodal or agentic applications with interchangeable components.
What are Haystack's key features?
Build Transparent, Context Engineered AI Systems
Design modular pipelines with retrievers, routers, memory layers, tools, evaluators, and generators, so teams can inspect behavior and tune outputs for production use.
Integrate Freely with Your AI Stack
Connect Haystack to OpenAI, Anthropic, Mistral, Hugging Face, Weaviate, Pinecone, and Elasticsearch without locking into one model or database.
Develop and Deploy Faster
Use Hayhooks to serve pipelines as REST APIs or MCP servers, reducing handoff work between development and deployment for AI applications.
Operate at Enterprise Scale
Run self-hosted deployments with tracing, logging, and evaluation, giving teams the controls needed to monitor and improve systems in production.
Multimodal AI
Build multimodal applications that combine text and other inputs, using Haystack's pipeline components to route data through the right processing steps.
Advanced RAG
Create advanced RAG systems with modular retrievers and routers, helping teams ground answers in external sources and improve retrieval quality.
AI Agents
Build production-ready AI agents with tools, memory layers, and evaluators, so workflows can act on tasks instead of only generating text.
What does Haystack integrate with?
- OpenAI
- Anthropic
- Mistral
- Hugging Face
- Weaviate
- Pinecone
- Elasticsearch
- GitHub
- Discord
- YouTube
- X (Twitter)
- DeepLearning.AI
- DataCamp
- AIMLAPI
- AlloyDB
- Amazon Bedrock
- Amazon Sagemaker
- Apify
- ArcadeDB
- Arize Phoenix
- Arize AI
- Asqav
- AssemblyAI
- AstraDB
- Azure AI Search
- Azure CosmosDB
- Azure Document Intelligence
- Azure
- Brave Search
- Bright Data
What are Haystack's use cases?
AI developers build RAG apps
AI developers use Haystack to build retrieval-heavy assistants with explicit control over retrievers, routers, memory layers, and tools. They can wire in Advanced RAG and AI Agents to keep answers grounded, traceable, and easier to debug before shipping to production.
Platform teams run in-house
Platform engineers use Haystack to assemble cloud-agnostic pipelines they can run in their own environment, using Integrate Freely with Your AI Stack and modular pipelines to fit existing infrastructure. That helps them avoid lock-in while keeping deployment patterns consistent.
Enterprise teams evaluate iteratively
Teams building enterprise AI systems use Haystack to monitor and improve applications over time, using tracing, logging, and evaluation to spot failure modes and compare changes. Operate at Enterprise Scale supports the reliability and governance they need.
Multimodal builders ship agents
Developers creating multimodal or agentic applications use Haystack to combine text, media, and tool use in one workflow, using Multimodal AI and build production-ready AI agents. That makes it easier to prototype interchangeable components and move toward production-ready behavior.
How does Haystack work?
- Connect your first data source or model provider, then assemble a modular pipeline with retrievers, routers, memory layers, tools, and generators in the visual or code-first workflow.
- Add Advanced RAG or AI Agents components to shape how the system retrieves context, decides actions, and produces grounded responses for your application.
- Turn on tracing, logging, and evaluation to inspect each step, compare runs, and catch weak retrieval or tool-use behavior before release.
- Deploy the pipeline through REST APIs or MCP servers via Hayhooks, then run it in your own environment for cloud-agnostic operations.
- Iterate by swapping integrations like OpenAI, Anthropic, Hugging Face, or Elasticsearch as requirements change, while keeping the same pipeline structure.
Frequently asked questions
What is Haystack?
Haystack is an open-source AI orchestration framework for developers who need explicit control over retrieval, routing, memory, tools, evaluation, and generation in modular pipelines. It supports advanced RAG, multimodal apps, and agent workflows, with interchangeable components and inspectable context flow. Haystack integrates with OpenAI, Anthropic, Weaviate, and Pinecone, and is used by NVIDIA, Airbus, AWS, and Comcast.
What is Haystack used for? Who is it for?
Haystack is used for Build Transparent, Context Engineered AI Systems, Integrate Freely with Your AI Stack, and Develop and Deploy Faster. It's built for AI developers, Platform engineers, and Teams building enterprise AI systems that need observability and iterative evaluation.
Does Haystack have an API and what does it integrate with?
Haystack can serve pipelines as REST APIs or MCP servers via Hayhooks. It integrates with OpenAI, Anthropic, Mistral, Hugging Face, Weaviate, and 25 more.
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