--- title: "llm-app vs stanford_alpaca" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-tatsu-lab-stanford-alpaca" tools: ["pathwaycom-llm-app", "tatsu-lab-stanford-alpaca"] --- # llm-app vs stanford_alpaca *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-app when llm-app is primarily Jupyter Notebook; stanford_alpaca is Python; pick stanford_alpaca when stanford_alpaca is primarily Python; llm-app is Jupyter Notebook. [llm-app](https://pathway.com/developers/templates/) reports 59k GitHub stars, 1.4k forks, and 10 open issues, last pushed Jul 5, 2026. [stanford_alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) has 30k stars, 4.0k forks, and 188 open issues, last pushed Jul 17, 2024. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [stanford_alpaca's repository](https://github.com/tatsu-lab/stanford_alpaca). | | [llm-app](/tools/pathwaycom-llm-app.md) | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | Code and documentation to train Stanford's Alpaca models, and generate the data. | | Stars | 59,068 | 30,250 | | Forks | 1,432 | 3,985 | | Open issues | 10 | 188 | | 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 | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | LLM Frameworks, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 724d | | Open issues (now) | 10 | 188 | | Security scan | No lockfile | 46 low (46 low) | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/tatsu-lab-stanford-alpaca/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 llm-app if… - llm-app is primarily Jupyter Notebook; stanford_alpaca is Python. - License: llm-app is MIT, stanford_alpaca 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, llm, retrieval-augmented-generation. - 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. ### Choose stanford_alpaca if… - stanford_alpaca is primarily Python; llm-app is Jupyter Notebook. - License: stanford_alpaca is Apache-2.0, llm-app is MIT. - Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model, python. - Also covers Model Training. ## 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 stanford_alpaca - Last GitHub push was 725 days ago (dormant maintenance, Jul 17, 2024). Validate activity before betting a new project on stanford_alpaca. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between llm-app and stanford_alpaca? llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. stanford_alpaca: Code and documentation to train Stanford's Alpaca models, and generate the data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-app over stanford_alpaca? Choose llm-app over stanford_alpaca when llm-app is primarily Jupyter Notebook; stanford_alpaca is Python; License: llm-app is MIT, stanford_alpaca 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, llm, retrieval-augmented-generation; 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 choose stanford_alpaca over llm-app? Choose stanford_alpaca over llm-app when stanford_alpaca is primarily Python; llm-app is Jupyter Notebook; License: stanford_alpaca is Apache-2.0, llm-app is MIT; Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model, python; Also covers Model Training. ### 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 stanford_alpaca? Last GitHub push was 725 days ago (dormant maintenance, Jul 17, 2024). Validate activity before betting a new project on stanford_alpaca. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is llm-app or stanford_alpaca more popular on GitHub? llm-app has more GitHub stars (59,068 vs 30,250). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-app and stanford_alpaca open source? Yes - both are open-source projects on GitHub (llm-app: MIT, stanford_alpaca: Apache-2.0). ### Where can I find alternatives to llm-app or stanford_alpaca? GraphCanon lists graph-backed alternatives at [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) and [stanford_alpaca alternatives](/tools/tatsu-lab-stanford-alpaca/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/alternatives.md), [stanford_alpaca markdown twin](/tools/tatsu-lab-stanford-alpaca/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-tatsu-lab-stanford-alpaca.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-app or stanford_alpaca? llm-app: Very active. stanford_alpaca: Dormant. 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 stanford_alpaca? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-app trust report](/tools/pathwaycom-llm-app/trust); [stanford_alpaca trust report](/tools/tatsu-lab-stanford-alpaca/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/_