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 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.
Markdown twin · RAGLight alternatives · ragflow alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | RAGLight | ragflow |
|---|---|---|
| Maintenance | Steady (57d since push) As of 1d · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal 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
- RAGLight
- A modular framework for Retrieval-Augmented Generation that supports integration with various LLMs and external tools.
- ragflow
- Retrieval-Augmented Generation engine with agent capabilities
Stars
- RAGLight
- 670
- ragflow
- 87k
Forks
- RAGLight
- 101
- ragflow
- 10k
Open issues
- RAGLight
- 12
- ragflow
- 2.0k
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 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
- RAGLight
- -
- ragflow
- -
Runtime
- RAGLight
- -
- ragflow
- -
License
- RAGLight
- MIT
- ragflow
- Apache-2.0 License
Last pushed
- RAGLight
- Jun 25, 2026
- ragflow
- Jul 31, 2026
Categories
- RAGLight
- AI Agents, Data & Retrieval
- ragflow
- AI Agents, Data & Retrieval
Trust and health
Maintenance
- RAGLight
- Steady (60%)
- ragflow
- Very active (96%)
Days since push
- RAGLight
- 57d
- ragflow
- 0d
Open issues (now)
- RAGLight
- 12
- ragflow
- 2.0k
Stars delta
- RAGLight
- 0 (30d)
- ragflow
- Unknown
Open issues delta
- RAGLight
- 0 (30d)
- ragflow
- Unknown
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.
Choose ragflow if…
- ragflow is primarily Go; RAGLight is Python.
- License: ragflow is Apache-2.0, RAGLight is MIT.
- Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services..
- Tags unique to ragflow: context management, rag.
- 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 (Bessouat40/RAGLight) · observed Aug 22, 2026
- GitHub forks (Bessouat40/RAGLight) · observed Aug 22, 2026
- Last push (Bessouat40/RAGLight) · observed Jun 25, 2026
- License file (MIT) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (infiniflow/ragflow) · observed Aug 1, 2026
- GitHub forks (infiniflow/ragflow) · observed Aug 1, 2026
- Last push (infiniflow/ragflow) · observed Jul 31, 2026
- License file (Apache-2.0) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: RAGLight 670 · ragflow 87k (synced Aug 22, 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 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; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; Tags unique to ragflow: context management, rag; 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 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 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 RAGLight or ragflow more popular on GitHub?
- ragflow has more GitHub stars (86,541 vs 670). 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: Steady. 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.