LLM framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data.
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Haystack is an end-to-end LLM framework that allows you to build applications powered by LLMs, Transformer models, vector search and more. Whether you want to perform retrieval-augmented generation (RAG), document search, question answering or answer generation, Haystack can orchestrate state-of-the-art embedding models and LLMs into pipelines to build end-to-end NLP applications and solve your use case.
The simplest way to get Haystack is via pip:
pip install haystack-ai
Install from the main branch to try the newest features:
pip install git+https://github.com/deepset-ai/haystack.git@main
Haystack supports multiple installation methods including Docker images. For a comprehensive guide please refer to the 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 can it do for you and the technology behind.
[!IMPORTANT] You are currently looking at the readme of Haystack 2.0. We are still maintaining Haystack 1.x to give everyone enough time to migrate to 2.0. Switch to Haystack 1.x here.
Some examples of what you can do with Haystack:
[!TIP]
Are you looking for a managed solution that benefits from Haystack? deepset Cloud is our fully managed, end-to-end platform to integrate LLMs with your data, which uses Haystack for the LLM pipelines architecture.
Use deepset Studio to visually create, deploy, and test your Haystack pipelines. Learn more about it in our announcement post.
👉 Sign up!
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.
If you have a feature request or a bug report, feel free to open an issue in Github. We regularly check these and 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.
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:
[!TIP] 👉 Check out the full list of issues that are open to contributions
Here's a list of projects and companies using Haystack. Want to add yours? Open a PR, add it to the list and let the world know that you use Haystack!
FAQs
LLM framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data.
We found that haystack-ai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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