--- title: "AutoRAG vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/marker-inc-korea-autorag-vs-pathwaycom-llm-app" tools: ["marker-inc-korea-autorag", "pathwaycom-llm-app"] --- # AutoRAG vs llm-app *GraphCanon updated Jul 12, 2026* ## Verdict Pick AutoRAG when autoRAG is primarily Python; llm-app is Jupyter Notebook; pick llm-app when llm-app is primarily Jupyter Notebook; AutoRAG is Python. [AutoRAG](https://marker-inc-korea.github.io/AutoRAG/) reports 4.9k GitHub stars, 407 forks, and 171 open issues, last pushed Jul 2, 2026. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.4k forks, and 10 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [AutoRAG's repository](https://github.com/Marker-Inc-Korea/AutoRAG) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [AutoRAG](/tools/marker-inc-korea-autorag.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | | Stars | 4,862 | 59,068 | | Forks | 407 | 1,432 | | Open issues | 171 | 10 | | Language | Python | Jupyter Notebook | | Adopt for | - | llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | Data & Retrieval, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AutoRAG](/tools/marker-inc-korea-autorag.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 9d | 5d | | Open issues (now) | 171 | 10 | | Full report | [trust report](/tools/marker-inc-korea-autorag/trust.md) | [trust report](/tools/pathwaycom-llm-app/trust.md) | ## Decision facts: llm-app - **Requirements:** Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others. - **Adopt for:** llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz ## Choose when ### Choose AutoRAG if… - AutoRAG is primarily Python; llm-app is Jupyter Notebook. - License: AutoRAG is Apache-2.0, llm-app is MIT. - Tags unique to AutoRAG: analysis, automl, benchmarking, document-parser. ### Choose llm-app if… - llm-app is primarily Jupyter Notebook; AutoRAG is Python. - License: llm-app is MIT, AutoRAG is Apache-2.0. - Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.. - Tags unique to llm-app: chatbot, hugging-face, retrieval-augmented-generation, vector-database. - - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ## 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 llm-app - - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ## Common questions ### What is the difference between AutoRAG and llm-app? AutoRAG: AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose AutoRAG over llm-app? Choose AutoRAG over llm-app when AutoRAG is primarily Python; llm-app is Jupyter Notebook; License: AutoRAG is Apache-2.0, llm-app is MIT; Tags unique to AutoRAG: analysis, automl, benchmarking, document-parser. ### When should I choose llm-app over AutoRAG? Choose llm-app over AutoRAG when llm-app is primarily Jupyter Notebook; AutoRAG is Python; License: llm-app is MIT, AutoRAG is Apache-2.0; Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; Tags unique to llm-app: chatbot, hugging-face, retrieval-augmented-generation, vector-database; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### 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 llm-app? - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ### Is AutoRAG or llm-app more popular on GitHub? llm-app has more GitHub stars (59,068 vs 4,862). Stars measure visibility, not whether either tool fits your constraints. ### Are AutoRAG and llm-app open source? Yes - both are open-source projects on GitHub (AutoRAG: Apache-2.0, llm-app: MIT). ### Where can I find alternatives to AutoRAG or llm-app? GraphCanon lists graph-backed alternatives at [AutoRAG alternatives](/tools/marker-inc-korea-autorag/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([AutoRAG markdown twin](/tools/marker-inc-korea-autorag/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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-pathwaycom-llm-app.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AutoRAG or llm-app? AutoRAG: Active. llm-app: 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 llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AutoRAG trust report](/tools/marker-inc-korea-autorag/trust); [llm-app trust report](/tools/pathwaycom-llm-app/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/_