--- title: "awesome-ai-safety vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/giskard-ai-awesome-ai-safety-vs-pathwaycom-llm-app" tools: ["giskard-ai-awesome-ai-safety", "pathwaycom-llm-app"] --- # awesome-ai-safety vs llm-app *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-ai-safety when license: awesome-ai-safety is Apache-2.0, llm-app is MIT; pick llm-app when license: llm-app is MIT, awesome-ai-safety is Apache-2.0. [awesome-ai-safety](https://giskard.ai) reports 218 GitHub stars, 38 forks, and 17 open issues, last pushed Apr 14, 2025. [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 [awesome-ai-safety's repository](https://github.com/Giskard-AI/awesome-ai-safety) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | 📚 A curated list of papers & technical articles on AI Quality & Safety | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | | Stars | 218 | 59,068 | | Forks | 38 | 1,432 | | Open issues | 17 | 10 | | Language | - | 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 | Computer Vision, Data & Retrieval, LLM Frameworks | Data & Retrieval, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 452d | 5d | | Open issues (now) | 17 | 10 | | Full report | [trust report](/tools/giskard-ai-awesome-ai-safety/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 awesome-ai-safety if… - License: awesome-ai-safety is Apache-2.0, llm-app is MIT. - Tags unique to awesome-ai-safety: ai, ai-alignment, ai-quality, ai-safety. - Also covers Computer Vision. ### Choose llm-app if… - License: llm-app is MIT, awesome-ai-safety 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 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 NOT to use awesome-ai-safety - Last GitHub push was 453 days ago (dormant maintenance, Apr 14, 2025). Validate activity before betting a new project on awesome-ai-safety. - 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. ## 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 awesome-ai-safety and llm-app? awesome-ai-safety: 📚 A curated list of papers & technical articles on AI Quality & Safety. 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 awesome-ai-safety over llm-app? Choose awesome-ai-safety over llm-app when License: awesome-ai-safety is Apache-2.0, llm-app is MIT; Tags unique to awesome-ai-safety: ai, ai-alignment, ai-quality, ai-safety; Also covers Computer Vision. ### When should I choose llm-app over awesome-ai-safety? Choose llm-app over awesome-ai-safety when License: llm-app is MIT, awesome-ai-safety 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 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 avoid awesome-ai-safety? Last GitHub push was 453 days ago (dormant maintenance, Apr 14, 2025). Validate activity before betting a new project on awesome-ai-safety. 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. ### 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 awesome-ai-safety or llm-app more popular on GitHub? llm-app has more GitHub stars (59,068 vs 218). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-safety and llm-app open source? Yes - both are open-source projects on GitHub (awesome-ai-safety: Apache-2.0, llm-app: MIT). ### Where can I find alternatives to awesome-ai-safety or llm-app? GraphCanon lists graph-backed alternatives at [awesome-ai-safety alternatives](/tools/giskard-ai-awesome-ai-safety/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([awesome-ai-safety markdown twin](/tools/giskard-ai-awesome-ai-safety/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/giskard-ai-awesome-ai-safety-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, awesome-ai-safety or llm-app? awesome-ai-safety: Dormant. 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 awesome-ai-safety and llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-ai-safety trust report](/tools/giskard-ai-awesome-ai-safety/trust); [llm-app trust report](/tools/pathwaycom-llm-app/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=giskard-ai-awesome-ai-safety`](/api/graphcanon/graph?tool=giskard-ai-awesome-ai-safety) - 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/_