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Data as of May 29, 2026
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Ragas
Framework for evaluating retrieval augmented generation pipelines
AI InfrastructureFounded 2023#27 of 55 in AI Infrastructure
Updated May 29, 2026
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Company profile
What is Ragas?
Ragas is an open-source framework designed to evaluate Retrieval Augmented Generation (RAG) pipelines. It provides a suite of metrics to assess various aspects of RAG systems, including faithfulness, answer relevance, and context quality, helping developers improve the performance of their AI applications.
Latest company data
Primary data point
Downloads
1.4M/30d
Tracked package: PyPI ragas
▲ +17%Updated 1d ago
Other data points
Dependent projects
154
Projects depending on tracked package: PyPI ragas
Updated 17h ago
GitHub stars
14.1K
Main repository stars
Updated 17h ago
Code usage
1
Public projects using this company's tools
Updated 17h ago
Repository health
Maintenance data from the main open-source repository.
Releases (30d)Source: GitHub release dataUpdates: WeeklyCaveat: Published releases in the last 30 daysMethodology →
0
Repository usage
Public repositories and source files importing packages tied to Ragas.
Repos importingMetric: Public GitHub repos using company packagesSource: ecosyste.ms Packages APIScope: UTC daily snapshotUpdates: DailyFreshness: 48h target; 48h expected source lagConfidence: HighCaveat: Public GitHub dependency data only; private repos and direct API use outside those packages are not visible.Methodology →
10%
About Ragas
Ragas is an open-source framework designed to evaluate Retrieval Augmented Generation (RAG) pipelines. It provides a suite of metrics to assess various aspects of RAG systems, including faithfulness, answer relevance, and context quality, helping developers improve the performance of their AI applications.
FoundersShahul ES, Jithin James
Funding
$3M · 1 roundRaised $3M total. Category position #27 of 55 in AI Infrastructure.
Seed2024
$3MTQ Ventures
Investors
TQ Ventures
Tracked packages (1)
1 PyPI
ragas
PyPIMain PyPI packageragas
Framework for evaluating retrieval augmented generation pipelines