Comparison
llm-app vs deep-research
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.
Markdown twin · llm-app alternatives · deep-research alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | llm-app | deep-research |
|---|---|---|
| Maintenance | Very active (5d since push) As of 4d · github_public_v1 | Active (26d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 4d · osv@v1 | No lockfile (source not queried) As of today · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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.
Stars
- llm-app
- 59k
- deep-research
- 4.6k
Forks
- llm-app
- 1.4k
- deep-research
- 1.1k
Open issues
- llm-app
- 10
- deep-research
- 36
Language
- llm-app
- Jupyter Notebook
- deep-research
- JavaScript
Adopt for
- llm-app
- 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
- deep-research
- -
Persona
- llm-app
- -
- deep-research
- -
Runtime
- llm-app
- -
- deep-research
- -
License
- llm-app
- MIT
- deep-research
- MIT
Last pushed
- llm-app
- Jul 5, 2026
- deep-research
- Jun 18, 2026
Categories
- llm-app
- Data & Retrieval, LLM Frameworks, Vector Databases
- deep-research
- Inference & Serving, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- llm-app
- Very active (96%)
- deep-research
- Active (82%)
Days since push
- llm-app
- 5d
- deep-research
- 26d
Open issues (now)
- llm-app
- 10
- deep-research
- 36
Full report
- llm-app
- Trust report
- deep-research
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (pathwaycom/llm-app) · observed Jul 11, 2026
- GitHub forks (pathwaycom/llm-app) · observed Jul 11, 2026
- Last push (pathwaycom/llm-app) · observed Jul 5, 2026
- License file (MIT) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (u14app/deep-research) · observed Jul 15, 2026
- GitHub forks (u14app/deep-research) · observed Jul 15, 2026
- Last push (u14app/deep-research) · observed Jun 18, 2026
- License file (MIT) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: llm-app 59k · deep-research 4.6k (synced Jul 11, 2026).
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 and deep-research alternatives (llm-app markdown twin, deep-research markdown twin), 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 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; deep-research trust report.