Answer-first summary58/100 GEO

Haystack

Open-source AI application framework.

AI FrameworksFair citabilityVisit site →

Your Citability Score

58/100
Completeness13/25
Citable structure20/25
Freshness25/25
Verified claim0/25
What to improve to rank higher
  • Completeness: Fill in description, category, URL, logo, tags, and audience.
  • Citable structure: Add an answer-first summary, a deeper description, a citable outcome, and a valid URL.
  • Verified claim: Claim this project as its owner (and get verified) to earn full trust.

Promotion (Boost) does not change this score — it only changes ordering. This number reflects real, verifiable citability.

H

Haystack

Invited

Open-source AI application framework.

AI FrameworksCH-VER-967280Listed July 27, 2026
Visit Website
AI-Extractable Summary
What:Open-source AI application framework.
For whom:Developers, ML engineers, and product teams building custom search, RAG, and LLM applications
Key outcome:Teams can build, evaluate, and deploy production-ready RAG and LLM applications faster with reusable pipelines and retrieval components
Category:AI Frameworks

Structured for AI systems to extract and cite.

Citability Score

49/100
60
Identity
45
Evidence
15
Trust
50
Freshness
80
Classification
24
Impressions
0
Clicks
0
Saves
0
GQI Earned

Citable Outcome

Teams can build, evaluate, and deploy production-ready RAG and LLM applications faster with reusable pipelines and retrieval components.

About

Open-source framework by deepset for building production-ready AI applications with RAG pipelines and agent capabilities.

Target Audience: Developers, ML engineers, and product teams building custom search, RAG, and LLM applications.
Not ideal for: Organizations looking for a no-code chatbot builder or a fully managed end-user application rather than a developer framework.

What makes it different

  • Open-source framework focused on production-grade AI application development
  • Modular pipeline architecture for retrieval, generation, evaluation, and orchestration
  • Strong support for retrieval-augmented generation and semantic search use cases
  • Built for extensibility so teams can swap models, retrievers, and vector stores as needed

Tags & Classification

retrieval augmented generationsemantic searchquestion answeringdocument searchllm application development
software developersmachine learning engineersai product teamsdata scientists
technologysaasfinancial serviceshealthcare
Platform: FrameworkModel: Open Source

Links & Transparency

Website

Cite this Project

BibTeX
@misc{citablehub_haystack,
  title = {Haystack},
  url = {https://citablehub.com/p/haystack},
  note = {Listed July 27, 2026. CitableHub ID: CH-VER-967280},
  year = {2026}
}
APA
Haystack. (2026). CitableHub Software Index. https://citablehub.com/p/haystack.
MLA
"Haystack." CitableHub, 2026, https://citablehub.com/p/haystack.