--- title: "llm-app vs gpl" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-ukplab-gpl" tools: ["pathwaycom-llm-app", "ukplab-gpl"] --- # llm-app vs gpl *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-app when llm-app is primarily Jupyter Notebook; gpl is Python; pick gpl when gpl 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. [gpl](https://github.com/UKPLab/gpl) has 343 stars, 38 forks, and 26 open issues, last pushed Jul 6, 2023. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [gpl's repository](https://github.com/UKPLab/gpl). | | [llm-app](/tools/pathwaycom-llm-app.md) | [gpl](/tools/ukplab-gpl.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | Powerful unsupervised domain adaptation method for dense retrieval. Requires only unlabeled corpus and yields massive improvement: "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation o | | Stars | 59,068 | 343 | | Forks | 1,432 | 38 | | Open issues | 10 | 26 | | 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 | Data & Retrieval, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [gpl](/tools/ukplab-gpl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 1101d | | Open issues (now) | 10 | 26 | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/ukplab-gpl/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; gpl is Python. - License: llm-app is MIT, gpl 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 LLM Frameworks. - - 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 gpl if… - gpl is primarily Python; llm-app is Jupyter Notebook. - License: gpl is Apache-2.0, llm-app is MIT. - Tags unique to gpl: bert, domain-adaptation, information-retrieval, nlp. - 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 gpl - Last GitHub push was 1102 days ago (dormant maintenance, Jul 6, 2023). Validate activity before betting a new project on gpl. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - 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 gpl? llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. gpl: Powerful unsupervised domain adaptation method for dense retrieval. Requires only unlabeled corpus and yields massive improvement: "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation o. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-app over gpl? Choose llm-app over gpl when llm-app is primarily Jupyter Notebook; gpl is Python; License: llm-app is MIT, gpl 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 LLM Frameworks; - 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 gpl over llm-app? Choose gpl over llm-app when gpl is primarily Python; llm-app is Jupyter Notebook; License: gpl is Apache-2.0, llm-app is MIT; Tags unique to gpl: bert, domain-adaptation, information-retrieval, nlp; 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 gpl? Last GitHub push was 1102 days ago (dormant maintenance, Jul 6, 2023). Validate activity before betting a new project on gpl. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. 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 gpl more popular on GitHub? llm-app has more GitHub stars (59,068 vs 343). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-app and gpl open source? Yes - both are open-source projects on GitHub (llm-app: MIT, gpl: Apache-2.0). ### Where can I find alternatives to llm-app or gpl? GraphCanon lists graph-backed alternatives at [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) and [gpl alternatives](/tools/ukplab-gpl/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/alternatives.md), [gpl markdown twin](/tools/ukplab-gpl/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-ukplab-gpl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-app or gpl? llm-app: Very active. gpl: 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 gpl? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-app trust report](/tools/pathwaycom-llm-app/trust); [gpl trust report](/tools/ukplab-gpl/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/_