Home/Compare/infinity vs ragflow

Comparison

infinity vs ragflow

Verdict

Coexists - RAGflow focuses specifically on fusing Agent capabilities with LLM context management, which might be a more specialized or focused subset of what Infinity aims to provide.

Markdown twin · infinity alternatives · ragflow alternatives

GraphCanon updated 1d

infinity logo

infinity

infiniflow/infinity

4.7kpushed Aug 17, 2026
vs
ragflow logo

ragflow

infiniflow/ragflow

87kpushed Jul 31, 2026

Trust & integrity

Signalinfinityragflow
Maintenance
Very active (3d since push)
As of 1d · github_public_v1
Very active (0d since push)
As of 3w · github_public_v1
Provenance
Not a fork · Organization account
As of 1d · github_public_v1
Not a fork · Organization account
As of 3w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
Published findings
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

infinity
AI-native database for LLM applications offering fast hybrid search capabilities.
ragflow
Retrieval-Augmented Generation engine with agent capabilities

Stars

infinity
4.7k
ragflow
87k

Forks

infinity
437
ragflow
10k

Open issues

infinity
64
ragflow
2.0k

Language

infinity
C++
ragflow
Go

Adopt for

infinity
Designed for high-speed hybrid searches in LLM applications, infinity supports dense vector, sparse vector, tensor, and full-text data types.
ragflow
RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license.

Persona

infinity
-
ragflow
-

Runtime

infinity
-
ragflow
-

License

infinity
Apache-2.0
ragflow
Apache-2.0 License

Last pushed

infinity
Aug 17, 2026
ragflow
Jul 31, 2026

Categories

infinity
Data & Retrieval, Vector Databases
ragflow
AI Agents, Data & Retrieval

Trust and health

Days since push

infinity
3d
ragflow
0d

Open issues (now)

infinity
64
ragflow
2.0k

Stars delta

infinity
+51 (30d)
ragflow
Unknown

Open issues delta

infinity
-2 (30d)
ragflow
Unknown

OSV dependency advisories

infinity
No lockfile (source not queried)
ragflow
Published findings

Full report

infinity
Trust report

Typed relationship

infinity successor ragflowInfinity likely builds on RAGFlow by providing a more comprehensive and advanced solution to Retrieval-Augmented Generation, aiming for high performance across various data types including vectors and texts.Coexists - RAGflow focuses specifically on fusing Agent capabilities with LLM context management, which might be a more specialized or focused subset of what Infinity aims to provide.

Choose infinity if…

  • infinity is primarily C++; ragflow is Go.
  • Infinity likely builds on RAGFlow by providing a more comprehensive and advanced solution to Retrieval-Augmented Generation, aiming for high performance across various data types including vectors and texts.
  • Tags unique to infinity: ai-native, approximate-nearest-neighbor-search, bm25, cpp20.
  • Also covers Vector Databases.
  • When your application requires rapid hybrid search capabilities across multiple data types including tensors and full texts.

When NOT to use infinity

  • If your project does not benefit from fast hybrid search features or if you prefer not to use an AI-native database solution.
  • When support for only dense vectors is sufficient, and the added complexity of supporting tensors and full texts is unnecessary.

Choose ragflow if…

  • ragflow is primarily Go; infinity is C++.
  • Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services..
  • Infinity likely builds on RAGFlow by providing a more comprehensive and advanced solution to Retrieval-Augmented Generation, aiming for high performance across various data types including vectors and texts.
  • Tags unique to ragflow: agentic-ai, context management, rag, retrieval-augmented-generation.
  • Also covers AI Agents.
  • ragflow ships Docker support for self-hosted deployment.
  • - You need an integrated RAG system with AI agent capabilities for better context management in your applications.

When NOT to use ragflow

  • - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go.
  • - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB).
  • - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: infinity 4.7k · ragflow 87k (synced Aug 21, 2026).

Common questions

What is the difference between infinity and ragflow?
infinity: AI-native database for LLM applications offering fast hybrid search capabilities.. ragflow: Retrieval-Augmented Generation engine with agent capabilities. See the comparison table for live GitHub stats and shared categories.
When should I choose infinity over ragflow?
Choose infinity over ragflow when infinity is primarily C++; ragflow is Go; Infinity likely builds on RAGFlow by providing a more comprehensive and advanced solution to Retrieval-Augmented Generation, aiming for high performance across various data types including vectors and texts; Tags unique to infinity: ai-native, approximate-nearest-neighbor-search, bm25, cpp20; Also covers Vector Databases; When your application requires rapid hybrid search capabilities across multiple data types including tensors and full texts.
When should I choose ragflow over infinity?
Choose ragflow over infinity when ragflow is primarily Go; infinity is C++; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; Infinity likely builds on RAGFlow by providing a more comprehensive and advanced solution to Retrieval-Augmented Generation, aiming for high performance across various data types including vectors and texts; Tags unique to ragflow: agentic-ai, context management, rag, retrieval-augmented-generation; Also covers AI Agents; ragflow ships Docker support for self-hosted deployment; - You need an integrated RAG system with AI agent capabilities for better context management in your applications.
When should I avoid infinity?
If your project does not benefit from fast hybrid search features or if you prefer not to use an AI-native database solution. When support for only dense vectors is sufficient, and the added complexity of supporting tensors and full texts is unnecessary.
When should I avoid ragflow?
- If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go. - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB). - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function.
Is infinity or ragflow more popular on GitHub?
ragflow has more GitHub stars (86,541 vs 4,675). Stars measure visibility, not whether either tool fits your constraints.
Are infinity and ragflow open source?
Yes - both are open-source projects on GitHub (infinity: Apache-2.0, ragflow: Apache-2.0).
Where can I find alternatives to infinity or ragflow?
GraphCanon lists graph-backed alternatives at infinity alternatives and ragflow alternatives (infinity markdown twin, ragflow markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, infinity or ragflow?
infinity: Very active. ragflow: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for infinity and ragflow?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: infinity trust report; ragflow trust report.

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