Comparison
RAGLight vs LightRAG
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 LightRAG if lightRAG is a retrieval-augmented generation tool that emphasizes simplicity and speed, ideal for efficient knowledge graph augmentation in large language models.
Markdown twin · RAGLight alternatives · LightRAG alternatives
GraphCanon updated Sep 20, 2026
9views this month
Trust & integrity
| Signal | RAGLight | LightRAG |
|---|---|---|
| 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 | No lockfile (source not queried) 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 |
Tagline
- RAGLight
- A modular framework for Retrieval-Augmented Generation that supports integration with various LLMs and external tools.
- LightRAG
- Simple and Fast Retrieval-Augmented Generation
Stars
- RAGLight
- 673
- LightRAG
- 40k
Forks
- RAGLight
- 102
- LightRAG
- 5.6k
Open issues
- RAGLight
- 22
- LightRAG
- 249
Language
- RAGLight
- Python
- LightRAG
- Python
Adopt for
- RAGLight
- RAGLight emerges as an adaptable framework for Retrieval-Augmented Generation, offering integration flexibility with multiple LLMs and external tools through MCP.
- LightRAG
- LightRAG is a retrieval-augmented generation tool that emphasizes simplicity and speed, ideal for efficient knowledge graph augmentation in large language models.
Persona
- RAGLight
- -
- LightRAG
- -
Runtime
- RAGLight
- -
- LightRAG
- -
License
- RAGLight
- MIT
- LightRAG
- MIT
Last pushed
- RAGLight
- Sep 2, 2026
- LightRAG
- Sep 18, 2026
Categories
- RAGLight
- AI Agents, Data & Retrieval
- LightRAG
- Data & Retrieval, Model Training
Trust and health
Maintenance
- RAGLight
- Active (82%)
- LightRAG
- Very active (96%)
Days since push
- RAGLight
- 17d
- LightRAG
- 0d
Open issues (now)
- RAGLight
- 22
- LightRAG
- 249
Stars delta
- RAGLight
- +3 (30d)
- LightRAG
- +848 (30d)
Open issues delta
- RAGLight
- +10 (30d)
- LightRAG
- +22 (30d)
Owner type
- RAGLight
- User
- LightRAG
- Organization
Full report
- RAGLight
- Trust report
- LightRAG
- Trust report
Choose RAGLight if…
- Tags unique to RAGLight: agentic-ai, data-science, framework, huggingface.
- Also covers AI Agents.
- 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 LightRAG if…
- Tags unique to LightRAG: docling, genai, gpt, graphrag.
- Also covers Model Training.
- LightRAG ships Docker support for self-hosted deployment.
- When you need a tool that prioritizes simplicity and speed in retrieval-augmented generation tasks.
When NOT to use LightRAG
- If your project does not require efficient knowledge graph augmentation or if speed and simplicity are not primary concerns.
- When you prefer not to use uv for package management and are satisfied with traditional methods like pip.
- In scenarios where the installation of additional libraries such as libcairo2 for handling SVGs in markdown/textpack documents is not feasible or desired.
- If your deployment environment strictly prohibits the use of external package managers like uv.
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 Sep 20, 2026
- GitHub forks (Bessouat40/RAGLight) · observed Sep 20, 2026
- Last push (Bessouat40/RAGLight) · observed Sep 2, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (HKUDS/LightRAG) · observed Sep 20, 2026
- GitHub forks (HKUDS/LightRAG) · observed Sep 20, 2026
- Last push (HKUDS/LightRAG) · observed Sep 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: RAGLight 673 · LightRAG 40k (synced Sep 20, 2026).
Common questions
- What is the difference between RAGLight and LightRAG?
- RAGLight: A modular framework for Retrieval-Augmented Generation that supports integration with various LLMs and external tools.. LightRAG: Simple and Fast Retrieval-Augmented Generation. See the comparison table for live GitHub stats and shared categories.
- When should I choose RAGLight over LightRAG?
- Choose RAGLight over LightRAG when Tags unique to RAGLight: agentic-ai, data-science, framework, huggingface; Also covers AI Agents; 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 LightRAG over RAGLight?
- Choose LightRAG over RAGLight when Tags unique to LightRAG: docling, genai, gpt, graphrag; Also covers Model Training; LightRAG ships Docker support for self-hosted deployment; When you need a tool that prioritizes simplicity and speed in retrieval-augmented generation tasks.
- 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 LightRAG?
- If your project does not require efficient knowledge graph augmentation or if speed and simplicity are not primary concerns. When you prefer not to use uv for package management and are satisfied with traditional methods like pip. In scenarios where the installation of additional libraries such as libcairo2 for handling SVGs in markdown/textpack documents is not feasible or desired. If your deployment environment strictly prohibits the use of external package managers like uv.
- Is RAGLight or LightRAG more popular on GitHub?
- LightRAG has more GitHub stars (39,743 vs 673). Stars measure visibility, not whether either tool fits your constraints.
- Are RAGLight and LightRAG open source?
- Yes - both are open-source projects on GitHub (RAGLight: MIT, LightRAG: MIT).
- Where can I find alternatives to RAGLight or LightRAG?
- GraphCanon lists graph-backed alternatives at RAGLight alternatives and LightRAG alternatives (RAGLight markdown twin, LightRAG 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 LightRAG?
- RAGLight: Active. LightRAG: 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 LightRAG?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: RAGLight trust report; LightRAG trust report.