SAsaikatsg·Mar 9, 2026Submission
Ragflow: fuses RAG with Agent capabilities to create context layer for LLMs
2 points · Open discussioninfiniflow
ragflow
Go2024–2026
infiniflow / ragflow
ragflow
RAGFlow is an agent-native database: a Retrieval-Augmented Generation (RAG) engine that grew into context infrastructure.
9Distinct people
16Mentions
0Explicit recommenders
94.1KGitHub stars
What to know before exploring
What it is
RAGFlow is an agent-native database: a Retrieval-Augmented Generation (RAG) engine that grew into context infrastructure.
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Where it is used
No documented product use has been verified yet.
What people are saying
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month16 dated mentions
Observed mentions: 2024-04-01 17:50:54 UTC to 2026-03-09 18:54:37 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Apr 2024 | 2024-04-01 00:00:00 UTC | 2024-05-01 00:00:00 UTC | 7 | 1 | 6 |
| May 2024 | 2024-05-01 00:00:00 UTC | 2024-06-01 00:00:00 UTC | 6 | 2 | 4 |
| Jun 2024 | 2024-06-01 00:00:00 UTC | 2024-07-01 00:00:00 UTC | 1 | 0 | 1 |
| Jul 2024 | 2024-07-01 00:00:00 UTC | 2024-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2024 | 2024-08-01 00:00:00 UTC | 2024-09-01 00:00:00 UTC | 1 | 1 | 0 |
| Sep 2024 | 2024-09-01 00:00:00 UTC | 2024-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2024 | 2024-10-01 00:00:00 UTC | 2024-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2024 | 2024-11-01 00:00:00 UTC | 2024-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2024 | 2024-12-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2025 | 2025-01-01 00:00:00 UTC | 2025-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2025 | 2025-02-01 00:00:00 UTC | 2025-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2025 | 2025-03-01 00:00:00 UTC | 2025-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2025 | 2025-04-01 00:00:00 UTC | 2025-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2025 | 2025-05-01 00:00:00 UTC | 2025-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2025 | 2025-06-01 00:00:00 UTC | 2025-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2025 | 2025-07-01 00:00:00 UTC | 2025-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2025 | 2025-08-01 00:00:00 UTC | 2025-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2025 | 2025-09-01 00:00:00 UTC | 2025-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2025 | 2025-12-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2026 | 2026-01-01 00:00:00 UTC | 2026-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
16 sourcesRAGFlow 0.9 is released to support GraphRAG end-to-end
3 points · Open discussionFrom 0.8, RAGFlow(https://github.com/infiniflow/ragflow) will provide no code workflow orchestration. This article describes what kind of graph orchestration engine is needed, and how it can be used to implement Agentic RAG.
In Agentic RAG: Definition and Low-Code ImplementationRRF is a simple and effective means of fused ranking for multiple recall.
Within our open source RAG product RAGFlow(https://github.com/infiniflow/ragflow), Elasticsearch is currently used instead of other general vector databases, because it can provide hybrid search right now. Under the default cases, embedding based reranker is not required, just RRF is enough, while even if reranker is used, keywords based retrieval is also a MUST to be hybridized with embedding based retrieval, that's just what RAGFlow's latest 0.7 release has provided.
On the other hand let me introduce another database we developed, Infinity(https://github.com/infiniflow/infinity), which can provide the hybrid search, you can see the performance here(https://github.com/infiniflow/infinity/blob/main/docs/refere...), both vector search and full-text search could perf
Read the rest
orm much faster than other open source alternatives.
From the next version(weeks later), Infinity will also provide more comprehensive hybrid search capabilities, what you have mentioned the 3-way recalls(dense vector, sparse vector, keyword search) could be provided within single request.
Integrated Rerankers, implemented RAPTOR, RAGFlow 0.7 released
2 points · Open discussionThere are numerous strategies and methods available to enhance RAG performance, particularly when it comes to improving performance in parsing vast amounts of unstructured data. Additionally, various scenarios call for different parsing techniques. I would suggest exploring a RAG project that excels in document parsing: https://github.com/infiniflow/ragflow
In Ask HN: RAG and unstructured data from several docsTry RAPTOR: https://arxiv.org/html/2401.18059v1
An implementation: github.com/infiniflow/ragflow
In Systematically Improving Your RAGRAGFlow (github.com/infiniflow/ragflow) use OCR/layout recognition/TSR(table structure recognition) to understand the document structure and context. Is there any difference between RAGFlow and ZenDB?
In Towards accurate and efficient document analytics with large language modelsDeepSeek-V2 integrated, RAGFlow v0.5.0 is released
7 points · Open discussionJust link them to https://github.com/infiniflow/ragflow/blob/main/rag/llm/chat... :)
In RAGFlow is an open-source RAG engine based on OCR and document parsingit's https://huggingface.co/InfiniFlow/deepdoc and the code for usage is in https://github.com/infiniflow/ragflow/blob/main/deepdoc/READ... – it took me a bit of trial and error to get it working
It seems to be a YOLOv8 fine-tune, I only did a couple tests but results were decent. Another model that is supposed to be fine tuned for borderless is https://huggingface.co/keremberke/yolov8m-table-extraction but I haven't had great results myself with it, but maybe worth a try for you.
In RAGFlow is an open-source RAG engine based on OCR and document parsingLooks like they do but aren't really documented yet:
https://github.com/infiniflow/ragflow/pull/119
In RAGFlow is an open-source RAG engine based on OCR and document parsingLooks like they do but aren't really documented yet:
https://github.com/infiniflow/ragflow/pull/119
In RAGFlow is an open-source RAG engine based on OCR and document parsingApparently "deep document understanding" refers to OCR and structured document parsing: https://github.com/infiniflow/ragflow/blob/main/deepdoc/READ...
Since "deep document understanding" is not a term of art, I would have just said "OCR and document parsing".
How well does it work? Please include benchmarks. You may be interested in
https://paperswithcode.com/sota/optical-character-recognitio...
https://paperswithcode.com/task/document-layout-analysis
The models seem to be closed source, hosted here: https://huggingface.co/InfiniFlow/deepdoc
In RAGFlow is an open-source RAG engine based on OCR and document parsingShowing the 15 most recent of 16 sources. Enable JavaScript to search and browse every indexed mention.