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Comparison

RAGLight vs ragflow

Verdict

Pick RAGLight if rAGLight emerges as an adaptable framework for Retrieval-Augmented Generation, offering integration flexibility with multiple LLMs and external tools through MCP; pick ragflow if rAGFlow is an open-source RAG engine that integrates agent capabilities to enhance context management for LLMs, built in Go under the Apache-2.0 license.

Markdown twin · RAGLight alternatives · ragflow alternatives

GraphCanon updated Sep 20, 2026

10views this month

RAGLight logo

RAGLight

Bessouat40/RAGLight

★ 673pushed Sep 2, 2026
vs
ragflow logo

ragflow

infiniflow/ragflow

★ 91kpushed Sep 18, 2026

Trust & integrity

SignalRAGLightragflow
Maintenance
Active (17d since push)
As of Sep 20, 2026 · github_public_v1
Very active (0d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Sep 20, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
Published findings
As of Sep 18, 2026 · 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
RAGLight trust reportragflow trust reportTrust methodology

Tagline

RAGLight
A modular framework for Retrieval-Augmented Generation that supports integration with various LLMs and external tools.
ragflow
Retrieval-Augmented Generation (RAG) engine with agent capabilities

Stars

RAGLight
673
ragflow
91k

Forks

RAGLight
102
ragflow
11k

Open issues

RAGLight
22
ragflow
1.4k

Language

RAGLight
Python
ragflow
Go

Adopt for

RAGLight
RAGLight emerges as an adaptable framework for Retrieval-Augmented Generation, offering integration flexibility with multiple LLMs and external tools through MCP.
ragflow
RAGFlow is an open-source RAG engine that integrates agent capabilities to enhance context management for LLMs, built in Go under the Apache-2.0 license.

Persona

RAGLight
-
ragflow
-

Runtime

RAGLight
-
ragflow
-

License

RAGLight
MIT
ragflow
Apache-2.0

Last pushed

RAGLight
Sep 2, 2026
ragflow
Sep 18, 2026

Categories

RAGLight
AI Agents, Data & Retrieval
ragflow
AI Agents, Data & Retrieval

Trust and health

Maintenance

RAGLight
Active (82%)
ragflow
Very active (96%)

Days since push

RAGLight
17d
ragflow
0d

Open issues (now)

RAGLight
22
ragflow
1.4k

Stars delta

RAGLight
+3 (30d)
ragflow
+4.4k (30d)

Open issues delta

RAGLight
+10 (30d)
ragflow
-553 (30d)

Owner type

RAGLight
User
ragflow
Organization

OSV dependency advisories

RAGLight
No lockfile (source not queried)
ragflow
Published findings

Full report

RAGLight
Trust report
ragflow
Trust report

Choose RAGLight if…

  • RAGLight is primarily Python; ragflow is Go.
  • License: RAGLight is MIT, ragflow is Apache-2.0.
  • Tags unique to RAGLight: data-science, framework, huggingface, mcp-tools.
  • When you require seamless integration with various Language Models (LLMs) like Hugging Face or OpenAI models, making RAGLight a suitable choice for diverse model environments.

When NOT to use RAGLight

  • Avoid using RAGLight if your workflow strictly demands proprietary integration methods that are not supported by its modular framework structure.
  • If the project focuses on a specific LLM without the need for flexibility or interchangeability, the overhead of configuring diverse integrations in RAGLight might be unnecessary.
GitHubDetails

Choose ragflow if…

  • ragflow is primarily Go; RAGLight is Python.
  • License: ragflow is Apache-2.0, RAGLight is MIT.
  • Tags unique to ragflow: agent-harness, agentic-retrieval, agentic-search, context-engine.
  • ragflow ships Docker support for self-hosted deployment.
  • When you need an open-source RAG engine that can integrate with external LLM and embedding services to enhance context management.

When NOT to use ragflow

  • If your project does not require integration with external LLM and embedding services, as RAGFlow relies on these services.
  • If you are looking for a lightweight solution, as RAGFlow's Docker image is approximately 2 GB in size.
  • When you prefer tools that do not require building a Docker image, as RAGFlow's setup involves creating a Docker image.
GitHubDetails

Explore

RAGLight trust report →ragflow trust report →AI Agents category →Data & Retrieval category →All comparisonsStack workflowsTrending tools

Sources

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

GitHub stars on cards: RAGLight 673 · ragflow 91k (synced Sep 20, 2026).

Common questions

What is the difference between RAGLight and ragflow?
RAGLight: A modular framework for Retrieval-Augmented Generation that supports integration with various LLMs and external tools.. ragflow: Retrieval-Augmented Generation (RAG) engine with agent capabilities. See the comparison table for live GitHub stats and shared categories.
When should I choose RAGLight over ragflow?
Choose RAGLight over ragflow when RAGLight is primarily Python; ragflow is Go; License: RAGLight is MIT, ragflow is Apache-2.0; Tags unique to RAGLight: data-science, framework, huggingface, mcp-tools; When you require seamless integration with various Language Models (LLMs) like Hugging Face or OpenAI models, making RAGLight a suitable choice for diverse model environments.
When should I choose ragflow over RAGLight?
Choose ragflow over RAGLight when ragflow is primarily Go; RAGLight is Python; License: ragflow is Apache-2.0, RAGLight is MIT; Tags unique to ragflow: agent-harness, agentic-retrieval, agentic-search, context-engine; ragflow ships Docker support for self-hosted deployment; When you need an open-source RAG engine that can integrate with external LLM and embedding services to enhance context management.
When should I avoid RAGLight?
Avoid using RAGLight if your workflow strictly demands proprietary integration methods that are not supported by its modular framework structure. If the project focuses on a specific LLM without the need for flexibility or interchangeability, the overhead of configuring diverse integrations in RAGLight might be unnecessary.
When should I avoid ragflow?
If your project does not require integration with external LLM and embedding services, as RAGFlow relies on these services. If you are looking for a lightweight solution, as RAGFlow's Docker image is approximately 2 GB in size. When you prefer tools that do not require building a Docker image, as RAGFlow's setup involves creating a Docker image.
Is RAGLight or ragflow more popular on GitHub?
ragflow has more GitHub stars (90,923 vs 673). Stars measure visibility, not whether either tool fits your constraints.
Are RAGLight and ragflow open source?
Yes - both are open-source projects on GitHub (RAGLight: MIT, ragflow: Apache-2.0).
Where can I find alternatives to RAGLight or ragflow?
GraphCanon lists graph-backed alternatives at RAGLight alternatives and ragflow alternatives (RAGLight 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, RAGLight or ragflow?
RAGLight: 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 RAGLight and ragflow?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: RAGLight trust report; ragflow trust report.

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