--- title: "llm-app vs Scrapegraph-ai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-scrapegraphai-scrapegraph-ai" tools: ["pathwaycom-llm-app", "scrapegraphai-scrapegraph-ai"] --- # llm-app vs Scrapegraph-ai *GraphCanon updated Aug 16, 2026* ## Verdict Pick llm-app if 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; pick Scrapegraph-ai if scrapegraph-ai is a Python-based scraping tool leveraging AI for smarter data extraction and search tasks. [llm-app](https://pathway.com/developers/templates/) reports 59k GitHub stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. [Scrapegraph-ai](https://scrapegraphai.com) has 30k stars, 2.9k forks, and 12 open issues, last pushed Jul 20, 2026. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [Scrapegraph-ai's repository](https://github.com/ScrapeGraphAI/Scrapegraph-ai). | | [llm-app](/tools/pathwaycom-llm-app.md) | [Scrapegraph-ai](/tools/scrapegraphai-scrapegraph-ai.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | Python scraper based on AI | | Stars | 59,037 | 29,618 | | Forks | 1,466 | 2,925 | | Open issues | 8 | 12 | | Language | Jupyter Notebook | Python | | 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 | Scrapegraph-ai is a Python-based scraping tool leveraging AI for smarter data extraction and search tasks. | | Persona | - | - | | Runtime | - | - | | License | MIT | Scrapegraph-ai operates under the MIT License, offering users flexible rights for modification and distribution of the code. | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [Scrapegraph-ai](/tools/scrapegraphai-scrapegraph-ai.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 41d | 27d | | Open issues (now) | 8 | 12 | | Stars delta | +11 (30d) | +1.2k (30d) | | Open issues delta | -2 (30d) | +5 (30d) | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/scrapegraphai-scrapegraph-ai/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 ## Decision facts: Scrapegraph-ai - **Pricing:** freemium - Free to use with potential charges for advanced features or premium services, as specified by its license. - **Requirements:** Developed in Python and can leverage existing libraries and frameworks related to AI and web scraping. - **Adopt for:** Scrapegraph-ai is a Python-based scraping tool leveraging AI for smarter data extraction and search tasks. - **License detail:** Scrapegraph-ai operates under the MIT License, offering users flexible rights for modification and distribution of the code. ## Choose when ### Choose llm-app if… - llm-app is primarily Jupyter Notebook; Scrapegraph-ai is Python. - 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 Vector Databases. - - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### Choose Scrapegraph-ai if… - Scrapegraph-ai is primarily Python; llm-app is Jupyter Notebook. - Pricing: Free to use with potential charges for advanced features or premium services, as specified by its license.. - Requirements: Developed in Python and can leverage existing libraries and frameworks related to AI and web scraping.. - Tags unique to Scrapegraph-ai: ai-crawler, crawler, data-extraction, large-language-model. - Scrapegraph-ai ships Docker support for self-hosted deployment. - When you need advanced AI capabilities to parse and understand the context of scraped web content. ## 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. ## When NOT to use Scrapegraph-ai - If you have simple and straightforward data extraction needs that do not require AI-powered intelligence. - When your project aims to scrape static or relatively unchanging datasets from websites with well-defined structures, as Scrapegraph-ai is more geared towards complex and dynamic scraping tasks where ## Common questions ### What is the difference between llm-app and Scrapegraph-ai? llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. Scrapegraph-ai: Python scraper based on AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-app over Scrapegraph-ai? Choose llm-app over Scrapegraph-ai when llm-app is primarily Jupyter Notebook; Scrapegraph-ai is Python; 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 Vector Databases; - 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 choose Scrapegraph-ai over llm-app? Choose Scrapegraph-ai over llm-app when Scrapegraph-ai is primarily Python; llm-app is Jupyter Notebook; Pricing: Free to use with potential charges for advanced features or premium services, as specified by its license.; Requirements: Developed in Python and can leverage existing libraries and frameworks related to AI and web scraping.; Tags unique to Scrapegraph-ai: ai-crawler, crawler, data-extraction, large-language-model; Scrapegraph-ai ships Docker support for self-hosted deployment; When you need advanced AI capabilities to parse and understand the context of scraped web content. ### 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. ### When should I avoid Scrapegraph-ai? If you have simple and straightforward data extraction needs that do not require AI-powered intelligence. When your project aims to scrape static or relatively unchanging datasets from websites with well-defined structures, as Scrapegraph-ai is more geared towards complex and dynamic scraping tasks where ### Is llm-app or Scrapegraph-ai more popular on GitHub? llm-app has more GitHub stars (59,037 vs 29,618). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-app and Scrapegraph-ai open source? Yes - both are open-source projects on GitHub (llm-app: MIT, Scrapegraph-ai: MIT). ### Where can I find alternatives to llm-app or Scrapegraph-ai? GraphCanon lists graph-backed alternatives at [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) and [Scrapegraph-ai alternatives](/tools/scrapegraphai-scrapegraph-ai/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/alternatives.md), [Scrapegraph-ai markdown twin](/tools/scrapegraphai-scrapegraph-ai/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/pathwaycom-llm-app-vs-scrapegraphai-scrapegraph-ai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-app or Scrapegraph-ai? llm-app: Steady. Scrapegraph-ai: 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 llm-app and Scrapegraph-ai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-app trust report](/tools/pathwaycom-llm-app/trust); [Scrapegraph-ai trust report](/tools/scrapegraphai-scrapegraph-ai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pathwaycom-llm-app`](/api/graphcanon/graph?tool=pathwaycom-llm-app) - 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/_