haystack
Haystack is an open-source Python framework for orchestrating LLM applications through modular, traceable pipelines and agent workflows. It gives explicit control over retrieval, routing, memory, and generation to build RAG systems, semantic search, and autonomous agents that are model- and vendor-agnostic.
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[](https://opensources.dev/resource/haystack)Apache-2.0Permissive — free to use in commercial and proprietary software, with attribution.View license →
Production readiness
4/5- Actively maintainedCommits in the last 6 months
- No known vulnerabilitiesNot yet scanned
- Clear, usable licenseApache-2.0 (permissive)
- Proven adoptionWidely used
- Has documentationDocumentation indexed
Our analysis
Haystack is a Python orchestration framework from deepset for building production LLM applications, structured around composable pipelines and agent workflows where retrieval, ranking, memory, tool calling, and generation are explicit, connectable components.
When to use haystack
Choose Haystack when building RAG systems, semantic search, question answering, or agents and you want a transparent, graph-style pipeline with explicit control over data flow, plus the freedom to swap between OpenAI, Anthropic, Cohere, Hugging Face, local models, and many vector stores without rewriting your stack.
When not to
If you only need a thin wrapper around a single LLM API, prompt chaining, or a quick prototype, a lighter SDK is simpler. For document-centric indexing with batteries-included data connectors, LlamaIndex may fit better, and teams already invested in LangChain's broad integration surface may not gain enough to switch.
Strengths
- Pipeline architecture makes data flow explicit, traceable, and easier to debug than opaque chains
- Genuinely vendor-agnostic across model providers and vector databases
- Mature engineering practices (typed, tested, coverage, OpenSSF best practices) signal production focus
- Extensible component interface and a separate core-integrations repo encourage a healthy ecosystem
- Companion Hayhooks project cleanly exposes pipelines as REST/MCP endpoints
Trade-offs
- The pipeline/component abstraction has a real learning curve versus simpler imperative code
- Anonymous telemetry is collected by default and requires opting out
- Some advanced features and support are steered toward the paid Haystack Enterprise offering
- Smaller third-party integration breadth than LangChain in some niches
Maturity
Very mature and actively maintained with 25k+ stars, frequent releases of the 2.x (haystack-ai) line, strong CI, and adoption by large organizations. Backed by deepset, which monetizes via Enterprise Starter and Platform offerings, giving the project a sustainable commercial model.
CI/CD
Haystack is an open-source AI orchestration framework for building production-ready LLM applications in Python.
Design 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.
Table of Contents
Installation
The simplest way to get Haystack is via pip:
pip install haystack-ai
Install nightly pre-releases to try the newest features:
pip install --pre haystack-ai
Haystack supports multiple installation methods, including Docker images. For a comprehensive guide, please refer to the documentation.
Documentation
If you're new to the project, check out "What is Haystack?" then go through the "Get Started Guide" and build your first LLM application in a matter of minutes. Keep learning with the tutorials. For more advanced use cases, or just to get some inspiration, you can browse our Haystack recipes in the Cookbook.
At any given point, hit the documentation to learn more about Haystack, what it can do for you, and the technology behind.
Features
Built for context engineering Design 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.
Model- and vendor-agnostic Integrate with OpenAI, Mistral, Anthropic, Cohere, Hugging Face, Azure OpenAI, AWS Bedrock, local models, and many others. Swap models or infrastructure components without rewriting your system.
Modular and customizable Use 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.
Extensible ecosystem Build 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.
[!TIP]
Would you like to deploy and serve Haystack pipelines as REST APIs or MCP servers? 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.
Haystack Enterprise: Support & Platform
Get 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.
👉 Get Haystack Enterprise Starter
Need a managed production setup for Haystack? The Haystack Enterprise Platform helps you build, test, deploy and operate Haystack pipelines with built-in observability, collaboration, governance, and access controls. It’s available as a managed cloud service or as a self-hosted solution.
👉 Learn more about Haystack Enterprise Platform or try it free
Telemetry
Haystack collects anonymous usage statistics of pipeline components. We receive an event every time these components are initialized. This way, we know which components are most relevant to our community.
Read more about telemetry in Haystack or how you can opt out in Haystack docs.
🖖 Community
If you have a feature request or a bug report, feel free to open an issue in GitHub. We regularly check these, so you can expect a quick response. If you'd like to discuss a topic or get more general advice on how to make Haystack work for your project, you can start a thread in Github Discussions or our Discord channel. We also check 𝕏 (Twitter) and Stack Overflow.
Contributing to Haystack
We are very open to the community's contributions - be it a quick fix of a typo, or a completely new feature! You don't need to be a Haystack expert to provide meaningful improvements. To learn how to get started, check out our Contributor Guidelines first.
There are several ways you can contribute to Haystack:
Contribute to the main Haystack project
Contribute an integration on haystack-core-integrations
Contribute to the documentation in haystack/docs-website
[!TIP] 👉 Check out the full list of issues that are open to contributions
Organizations using Haystack
Haystack is used by thousands of teams building production AI systems across industries, including:
Technology & AI Infrastructure: Apple, Meta, Databricks, NVIDIA, Intel
Public Sector AI Initiatives: European Commission, German Federal Ministry of Research, Technology, and Space (BMFTR), PD, Baden-Württemberg State
Enterprise & Industrial AI Applications: Airbus, Lufthansa Industry Solutions, Infineon, LEGO, Comcast, Accenture, TELUS Agriculture & Consumer Goods
Knowledge & Content Platforms: Netflix, ZEIT Online, Rakuten, Oxford University Press, Manz, YPulse
Are you also using Haystack? Open a PR or tell us your story