--- title: "AutoRAG vs raglite" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/marker-inc-korea-autorag-vs-superlinear-ai-raglite" tools: ["marker-inc-korea-autorag", "superlinear-ai-raglite"] --- # AutoRAG vs raglite *GraphCanon updated Jul 12, 2026* ## Verdict Pick AutoRAG when license: AutoRAG is Apache-2.0, raglite is MPL-2.0; pick raglite when license: raglite is MPL-2.0, AutoRAG is Apache-2.0. [AutoRAG](https://marker-inc-korea.github.io/AutoRAG/) reports 4.9k GitHub stars, 407 forks, and 171 open issues, last pushed Jul 2, 2026. [raglite](https://github.com/superlinear-ai/raglite) has 1.2k stars, 108 forks, and 13 open issues, last pushed Jul 9, 2026. Figures are from public GitHub metadata via [AutoRAG's repository](https://github.com/Marker-Inc-Korea/AutoRAG) and [raglite's repository](https://github.com/superlinear-ai/raglite). | | [AutoRAG](/tools/marker-inc-korea-autorag.md) | [raglite](/tools/superlinear-ai-raglite.md) | | --- | --- | --- | | Tagline | AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation | Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL | | Stars | 4,862 | 1,194 | | Forks | 407 | 108 | | Open issues | 171 | 13 | | Language | Python | Python | | Adopt for | - | RAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MPL-2.0 | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AutoRAG](/tools/marker-inc-korea-autorag.md) | [raglite](/tools/superlinear-ai-raglite.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 9d | 2d | | Open issues (now) | 171 | 13 | | Full report | [trust report](/tools/marker-inc-korea-autorag/trust.md) | [trust report](/tools/superlinear-ai-raglite/trust.md) | ## Decision facts: raglite - **Adopt for:** RAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. ## Choose when ### Choose AutoRAG if… - License: AutoRAG is Apache-2.0, raglite is MPL-2.0. - Tags unique to AutoRAG: analysis, automl, benchmarking, document-parser. - Also covers LLM Frameworks, Vector Databases. ### Choose raglite if… - License: raglite is MPL-2.0, AutoRAG is Apache-2.0. - Tags unique to raglite: chainlit, colbert, duckdb, evals. - Also covers Model Training. - raglite ships Docker support for self-hosted deployment. - - You need to leverage Retriever-Reader architectures specifically optimized for either DuckDB or PostgreSQL backend databases. ## When NOT to use AutoRAG - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 AutoRAG and raglite? AutoRAG: AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation. 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 AutoRAG over raglite? Choose AutoRAG over raglite when License: AutoRAG is Apache-2.0, raglite is MPL-2.0; Tags unique to AutoRAG: analysis, automl, benchmarking, document-parser; Also covers LLM Frameworks, Vector Databases. ### When should I choose raglite over AutoRAG? Choose raglite over AutoRAG when License: raglite is MPL-2.0, AutoRAG is Apache-2.0; Tags unique to raglite: chainlit, colbert, duckdb, evals; Also covers Model Training; raglite ships Docker support for self-hosted deployment; - You need to leverage Retriever-Reader architectures specifically optimized for either DuckDB or PostgreSQL backend databases. ### When should I avoid AutoRAG? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 AutoRAG or raglite more popular on GitHub? AutoRAG has more GitHub stars (4,862 vs 1,194). Stars measure visibility, not whether either tool fits your constraints. ### Are AutoRAG and raglite open source? Yes - both are open-source projects on GitHub (AutoRAG: Apache-2.0, raglite: MPL-2.0). ### Where can I find alternatives to AutoRAG or raglite? GraphCanon lists graph-backed alternatives at [AutoRAG alternatives](/tools/marker-inc-korea-autorag/alternatives) and [raglite alternatives](/tools/superlinear-ai-raglite/alternatives) ([AutoRAG markdown twin](/tools/marker-inc-korea-autorag/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/marker-inc-korea-autorag-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, AutoRAG or raglite? AutoRAG: Active. raglite: 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 AutoRAG and raglite? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AutoRAG trust report](/tools/marker-inc-korea-autorag/trust); [raglite trust report](/tools/superlinear-ai-raglite/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=marker-inc-korea-autorag`](/api/graphcanon/graph?tool=marker-inc-korea-autorag) - 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/_