--- title: "LightRAG vs raglite" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hkuds-lightrag-vs-superlinear-ai-raglite" tools: ["hkuds-lightrag", "superlinear-ai-raglite"] --- # LightRAG vs raglite *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick raglite if rAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. [LightRAG](https://arxiv.org/abs/2410.05779) reports 40k GitHub stars, 5.6k forks, and 249 open issues, last pushed Sep 18, 2026. [raglite](https://github.com/superlinear-ai/raglite) has 1.2k stars, 109 forks, and 14 open issues, last pushed Aug 17, 2026. Figures are from public GitHub metadata via [LightRAG's repository](https://github.com/HKUDS/LightRAG) and [raglite's repository](https://github.com/superlinear-ai/raglite). | | [LightRAG](/tools/hkuds-lightrag.md) | [raglite](/tools/superlinear-ai-raglite.md) | | --- | --- | --- | | Tagline | Simple and Fast Retrieval-Augmented Generation | Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL | | Stars | 39,743 | 1,202 | | Forks | 5,589 | 109 | | Open issues | 249 | 14 | | Language | Python | Python | | Adopt for | LightRAG is a retrieval-augmented generation tool that emphasizes simplicity and speed, ideal for efficient knowledge graph augmentation in large language models. | RAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. | | Persona | - | - | | Runtime | - | - | | License | MIT | MPL-2.0 | | Categories | Data & Retrieval, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LightRAG](/tools/hkuds-lightrag.md) | [raglite](/tools/superlinear-ai-raglite.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 33d | | Open issues (now) | 249 | 14 | | Stars delta | +848 (30d) | +6 (30d) | | Open issues delta | +22 (30d) | +1 (30d) | | Full report | [trust report](/tools/hkuds-lightrag/trust.md) | [trust report](/tools/superlinear-ai-raglite/trust.md) | ## Shared compatibility - **Python**: [LightRAG](/tools/hkuds-lightrag.md) - Python runtime; [raglite](/tools/superlinear-ai-raglite.md) - Python runtime ## Decision facts: LightRAG - **Adopt for:** LightRAG is a retrieval-augmented generation tool that emphasizes simplicity and speed, ideal for efficient knowledge graph augmentation in large language models. ## Decision facts: raglite - **Adopt for:** RAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. ## Choose when ### Choose LightRAG if… - License: LightRAG is MIT, raglite is MPL-2.0. - Tags unique to LightRAG: docling, genai, gpt, graphrag. - When you need a tool that prioritizes simplicity and speed in retrieval-augmented generation tasks. ### Choose raglite if… - License: raglite is MPL-2.0, LightRAG is MIT. - Tags unique to raglite: chainlit, colbert, duckdb, evals. - - You need to leverage Retriever-Reader architectures specifically optimized for either DuckDB or PostgreSQL backend databases. ## 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. ## When NOT to use raglite - - The project demands integration with RAG systems that natively support database backends other than DuckDB and PostgreSQL, as RAGLite is limited to these two options. - - You are looking for a more generalized framework that supports multiple vector search engines besides those compatible with DuckDB or PostgreSQL. ## Common questions ### What is the difference between LightRAG and raglite? LightRAG: Simple and Fast Retrieval-Augmented Generation. raglite: Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL. See the comparison table for live GitHub stats and shared categories. ### When should I choose LightRAG over raglite? Choose LightRAG over raglite when License: LightRAG is MIT, raglite is MPL-2.0; Tags unique to LightRAG: docling, genai, gpt, graphrag; When you need a tool that prioritizes simplicity and speed in retrieval-augmented generation tasks. ### When should I choose raglite over LightRAG? Choose raglite over LightRAG when License: raglite is MPL-2.0, LightRAG is MIT; Tags unique to raglite: chainlit, colbert, duckdb, evals; - You need to leverage Retriever-Reader architectures specifically optimized for either DuckDB or PostgreSQL backend databases. ### 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. ### When should I avoid raglite? - The project demands integration with RAG systems that natively support database backends other than DuckDB and PostgreSQL, as RAGLite is limited to these two options. - You are looking for a more generalized framework that supports multiple vector search engines besides those compatible with DuckDB or PostgreSQL. ### Is LightRAG or raglite more popular on GitHub? LightRAG has more GitHub stars (39,743 vs 1,202). Stars measure visibility, not whether either tool fits your constraints. ### Are LightRAG and raglite open source? Yes - both are open-source projects on GitHub (LightRAG: MIT, raglite: MPL-2.0). ### Where can I find alternatives to LightRAG or raglite? GraphCanon lists graph-backed alternatives at [LightRAG alternatives](/tools/hkuds-lightrag/alternatives) and [raglite alternatives](/tools/superlinear-ai-raglite/alternatives) ([LightRAG markdown twin](/tools/hkuds-lightrag/alternatives.md), [raglite markdown twin](/tools/superlinear-ai-raglite/alternatives.md)), 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](/compare/hkuds-lightrag-vs-superlinear-ai-raglite.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LightRAG or raglite? LightRAG: Very active. raglite: Steady. 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 LightRAG and raglite? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LightRAG trust report](/tools/hkuds-lightrag/trust); [raglite trust report](/tools/superlinear-ai-raglite/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hkuds-lightrag`](/api/graphcanon/graph?tool=hkuds-lightrag) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_