--- title: "llm-app vs deep-research" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-u14app-deep-research" tools: ["pathwaycom-llm-app", "u14app-deep-research"] --- # llm-app vs deep-research *GraphCanon updated Jul 15, 2026* ## Verdict Pick llm-app when llm-app is primarily Jupyter Notebook; deep-research is JavaScript; pick deep-research when deep-research is primarily JavaScript; 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. [deep-research](https://research.u14.app) has 4.6k stars, 1.1k forks, and 36 open issues, last pushed Jun 18, 2026. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [deep-research's repository](https://github.com/u14app/deep-research). | | [llm-app](/tools/pathwaycom-llm-app.md) | [deep-research](/tools/u14app-deep-research.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | Use any LLMs (Large Language Models) for Deep Research. Support SSE API and MCP server. | | Stars | 59,068 | 4,632 | | Forks | 1,432 | 1,055 | | Open issues | 10 | 36 | | Language | Jupyter Notebook | JavaScript | | 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 | Data & Retrieval, LLM Frameworks, Vector Databases | Inference & Serving, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [deep-research](/tools/u14app-deep-research.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 5d | 26d | | Open issues (now) | 10 | 36 | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/u14app-deep-research/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; deep-research is JavaScript. - 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 deep-research if… - deep-research is primarily JavaScript; llm-app is Jupyter Notebook. - Tags unique to deep-research: anthropic, deep-research, deep-research-api, deepresearch. - Also covers Inference & Serving. - deep-research ships Docker support for self-hosted deployment. ## 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 deep-research - 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. ## Common questions ### What is the difference between llm-app and deep-research? llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. deep-research: Use any LLMs (Large Language Models) for Deep Research. Support SSE API and MCP server.. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-app over deep-research? Choose llm-app over deep-research when llm-app is primarily Jupyter Notebook; deep-research is JavaScript; 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 deep-research over llm-app? Choose deep-research over llm-app when deep-research is primarily JavaScript; llm-app is Jupyter Notebook; Tags unique to deep-research: anthropic, deep-research, deep-research-api, deepresearch; Also covers Inference & Serving; deep-research ships Docker support for self-hosted deployment. ### 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 deep-research? 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. ### Is llm-app or deep-research more popular on GitHub? llm-app has more GitHub stars (59,068 vs 4,632). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-app and deep-research open source? Yes - both are open-source projects on GitHub (llm-app: MIT, deep-research: MIT). ### Where can I find alternatives to llm-app or deep-research? GraphCanon lists graph-backed alternatives at [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) and [deep-research alternatives](/tools/u14app-deep-research/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/alternatives.md), [deep-research markdown twin](/tools/u14app-deep-research/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-u14app-deep-research.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-app or deep-research? llm-app: Very active. deep-research: 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 deep-research? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-app trust report](/tools/pathwaycom-llm-app/trust); [deep-research trust report](/tools/u14app-deep-research/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/_