| CI/CD |     |
| Docs |  |
| Package |          |
| Meta |   |
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
- Documentation
- Features
- Haystack Enterprise: Support & Platform
- Telemetry
- 🖖 Community
- Contributing to Haystack
- Organizations using Haystack
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
Agents built for production
Extend agent behavior with lifecycle hooks (before_llm, before_tool, on_exit, …) 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 (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.
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.
Native Async Support
One Pipeline runs synchronously or asynchronously and streams token by token. Agent can run concurrent tool calls.
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.
Model- and vendor-agnostic
Integrate 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.
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.
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
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
1 maintainer
Release files for haystack-ai 3.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| haystack_ai-3.2.0.tar.gz | 530.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| haystack_ai-3.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.3 MB
Release files / haystack_ai-3.2.0.tar.gz
| Download URL | haystack_ai-3.2.0.tar.gz |
|---|---|
| Size | 530.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
e4e9297616c5d398b3180c5e63baa0181c9a60924afc9752fbe4d8f667ce02be
|
|
BLAKE2b-256 checksum How to use checksums |
2f988170941cc940a8419ad4aa60545659ae04862150591e6943b8f710b9389d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.
Transparency logRelease files / haystack_ai-3.2.0-py3-none-any.whl
| Download URL | haystack_ai-3.2.0-py3-none-any.whl |
|---|---|
| Size | 734.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
072c7e5e06b20d2287db43525335804f457840b5277b301b7e7d4c7469266b2b
|
|
BLAKE2b-256 checksum How to use checksums |
625eb6e8bec88ecc1cbdd1b818c69fcd5b00fcee56179833ba31da8023b37bef
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.
Transparency log
