--- title: "graphrag-rs vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automataia-graphrag-rs-vs-pathwaycom-llm-app" tools: ["automataia-graphrag-rs", "pathwaycom-llm-app"] --- # graphrag-rs vs llm-app *GraphCanon updated Jul 11, 2026* ## Verdict Pick graphrag-rs when graphrag-rs is primarily Rust; llm-app is Jupyter Notebook; pick llm-app when llm-app is primarily Jupyter Notebook; graphrag-rs is Rust. [graphrag-rs](https://automataia.github.io/graphrag-rs/) reports 518 GitHub stars, 47 forks, and 0 open issues, last pushed Jun 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 [graphrag-rs's repository](https://github.com/automataIA/graphrag-rs) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [graphrag-rs](/tools/automataia-graphrag-rs.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | GraphRAG-rs is a high-performance, state-of-the-art Rust implementation of GraphRAG (Graph-based Retrieval Augmented Generation) that builds knowledge graphs from documents and enables natural languag | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | | Stars | 518 | 59,068 | | Forks | 47 | 1,432 | | Open issues | 0 | 10 | | Language | Rust | 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 | MIT | MIT | | Categories | Inference & Serving, LLM Frameworks, Vector Databases | Data & Retrieval, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [graphrag-rs](/tools/automataia-graphrag-rs.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 38d | 5d | | Open issues (now) | 0 | 10 | | Owner type | User | Organization | | Full report | [trust report](/tools/automataia-graphrag-rs/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 graphrag-rs if… - graphrag-rs is primarily Rust; llm-app is Jupyter Notebook. - Tags unique to graphrag-rs: ai, embeddings, entity-extraction, graphrag. - Also covers Inference & Serving. ### Choose llm-app if… - llm-app is primarily Jupyter Notebook; graphrag-rs is Rust. - 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. - Also covers Data & Retrieval. - - 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 graphrag-rs - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - 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 graphrag-rs and llm-app? graphrag-rs: GraphRAG-rs is a high-performance, state-of-the-art Rust implementation of GraphRAG (Graph-based Retrieval Augmented Generation) that builds knowledge graphs from documents and enables natural languag. 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 graphrag-rs over llm-app? Choose graphrag-rs over llm-app when graphrag-rs is primarily Rust; llm-app is Jupyter Notebook; Tags unique to graphrag-rs: ai, embeddings, entity-extraction, graphrag; Also covers Inference & Serving. ### When should I choose llm-app over graphrag-rs? Choose llm-app over graphrag-rs when llm-app is primarily Jupyter Notebook; graphrag-rs is Rust; 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; Also covers Data & Retrieval; - 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 graphrag-rs? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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 graphrag-rs or llm-app more popular on GitHub? llm-app has more GitHub stars (59,068 vs 518). Stars measure visibility, not whether either tool fits your constraints. ### Are graphrag-rs and llm-app open source? Yes - both are open-source projects on GitHub (graphrag-rs: MIT, llm-app: MIT). ### Where can I find alternatives to graphrag-rs or llm-app? GraphCanon lists graph-backed alternatives at [graphrag-rs alternatives](/tools/automataia-graphrag-rs/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([graphrag-rs markdown twin](/tools/automataia-graphrag-rs/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/automataia-graphrag-rs-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, graphrag-rs or llm-app? graphrag-rs: Steady. 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 graphrag-rs and llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [graphrag-rs trust report](/tools/automataia-graphrag-rs/trust); [llm-app trust report](/tools/pathwaycom-llm-app/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automataia-graphrag-rs`](/api/graphcanon/graph?tool=automataia-graphrag-rs) - 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/_