Overview
Research summary
RAGFlow prepares documents for retrieval-augmented generation through parsing, configurable chunking, indexing and retrieval workflows. Its interface supports inspecting chunks and tracing answers back to source material, while APIs and a Python SDK connect the engine to applications. The project also includes agentic retrieval and knowledge compilation features. The current 1.0 release candidate introduces Go services; earlier releases used a Python backend. This entry covers the Apache-2.0 repository, while the separately operated RAGFlow Cloud service has its own terms.
Repository summary
- Stars
- Unavailable
- Open issues
- Unavailable
- Last push
- Unavailable
- Commits, 90 days
- Unavailable
- Repository activity
- Not scored
- Version
- Unavailable
Recorded catalogue figures. View repository data and provenance →
Classification
Pricing & services
Paid services available
The provider offers paid products or services. Free options may also be available.
Commercial offering checked 2026-10-02.
Licence scope
Implementation
Recorded implementation details and interfaces for RAGFlow.
Implementation details
Go services and document parsing with Python SDK and React web interface
- Languages
- Go, Python, TypeScript
- Repository type
- source
Recorded interfaces and capabilities
Licence scope
Repository
Repository snapshots, release information and recorded maintenance signals.
Repository snapshot
- Stars
- Unavailable
- Open issues
- Unavailable
- Last push
- Unavailable
- Commits, 90 days
- Unavailable
- Repository activity
- Not scored
- Archived
- Not recorded
Repository activity is a snapshot, not a quality or popularity ranking. It combines recent-push freshness (50%), 90-day commits (30%) and issue pressure (20%).
Maintenance and provenance
- Catalogue snapshot
- 2026-10-06
Documentation
Recorded references and research provenance for this entry.
Recorded sources 4
- https://ragflow.io Project page · Research reference · Commercial offering evidence
- https://github.com/infiniflow/ragflow Linked repository · Research reference
- https://github.com/infiniflow/ragflow/blob/main/README.md Research reference
- https://www.linkedin.com/company/infiniflow/ Research reference
Research metadata
- Research date
- 2026-10-01
- Catalogue snapshot
- 2026-10-06
Community & social 5 channels
Video
- YouTubeInfiniFlow / RAGFlowOfficial
Communities
Social & updates
- XInfiniFlowOfficial
- LinkedInInfiniFlowOfficialAnnouncementsReleases
Link details for LinkedIn — InfiniFlow
- Last checked