Comparison
LLMSys-PaperList vs deep-research
Verdict
Pick LLMSys-PaperList if lLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems; pick deep-research if deep Research is a JavaScript-based framework enabling integration of various Large Language Models for deep research projects using SSE and MCP.
Markdown twin · LLMSys-PaperList alternatives · deep-research alternatives
GraphCanon updated Sep 20, 2026
9views this month
vs
Trust & integrity
| Signal | LLMSys-PaperList | deep-research |
|---|---|---|
| Maintenance | Steady (43d since push) As of Sep 6, 2026 · github_public_v1 | Slowing (93d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 6, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Aug 30, 2026 · 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
- LLMSys-PaperList
- Curated list of academic papers related to Large Language Model systems
- deep-research
- Use any LLMs for Deep Research with SSE API and MCP server
Stars
- LLMSys-PaperList
- 2.2k
- deep-research
- 4.7k
Forks
- LLMSys-PaperList
- 120
- deep-research
- 1.1k
Open issues
- LLMSys-PaperList
- 1
- deep-research
- 39
Language
- LLMSys-PaperList
- Python
- deep-research
- JavaScript
Adopt for
- LLMSys-PaperList
- LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems.
- deep-research
- Deep Research is a JavaScript-based framework enabling integration of various Large Language Models for deep research projects using SSE and MCP.
Persona
- LLMSys-PaperList
- -
- deep-research
- -
Runtime
- LLMSys-PaperList
- -
- deep-research
- -
License
- LLMSys-PaperList
- (unknown)
- deep-research
- MIT
Last pushed
- LLMSys-PaperList
- Jul 25, 2026
- deep-research
- Jun 18, 2026
Categories
- LLMSys-PaperList
- Inference & Serving, LLM Frameworks, Model Training
- deep-research
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- LLMSys-PaperList
- Steady (60%)
- deep-research
- Slowing (36%)
Days since push
- LLMSys-PaperList
- 43d
- deep-research
- 93d
Open issues (now)
- LLMSys-PaperList
- 1
- deep-research
- 39
Stars delta
- LLMSys-PaperList
- +21 (30d)
- deep-research
- +2 (30d)
Open issues delta
- LLMSys-PaperList
- 0 (30d)
- deep-research
- +3 (30d)
Owner type
- LLMSys-PaperList
- User
- deep-research
- Organization
Full report
- LLMSys-PaperList
- Trust report
- deep-research
- Trust report
Choose LLMSys-PaperList if…
- LLMSys-PaperList is primarily Python; deep-research is JavaScript.
- (repository does not specify hosting environment)
- Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers.
- Also covers Model Training.
- - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
When NOT to use LLMSys-PaperList
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models.
- - When your primary need is documentation or code examples rather than academic papers and project insights.
- - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
Choose deep-research if…
- deep-research is primarily JavaScript; LLMSys-PaperList is Python.
- Tags unique to deep-research: anthropic, deep-research-api, gemini, grok.
- deep-research ships Docker support for self-hosted deployment.
- - When requiring an API interface that supports Server-Sent Events (SSE) and Model Control Protocol (MCP) for integrating large language models
When NOT to use deep-research
- - When working with environments that do not support JavaScript, as Deep Research is primarily built on this language
- - For projects that require real-time bidirectional communication with models, as Deep Research might only provide unidirectional data flow through SSE
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AmberLJC/LLMSys-PaperList) · observed Sep 20, 2026
- GitHub forks (AmberLJC/LLMSys-PaperList) · observed Sep 20, 2026
- Last push (AmberLJC/LLMSys-PaperList) · observed Jul 25, 2026
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (u14app/deep-research) · observed Sep 20, 2026
- GitHub forks (u14app/deep-research) · observed Sep 20, 2026
- Last push (u14app/deep-research) · observed Jun 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Aug 30, 2026
GitHub stars on cards: LLMSys-PaperList 2.2k · deep-research 4.7k (synced Sep 20, 2026).
Common questions
- What is the difference between LLMSys-PaperList and deep-research?
- LLMSys-PaperList: Curated list of academic papers related to Large Language Model systems. deep-research: Use any LLMs for Deep Research with SSE API and MCP server. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMSys-PaperList over deep-research?
- Choose LLMSys-PaperList over deep-research when LLMSys-PaperList is primarily Python; deep-research is JavaScript; (repository does not specify hosting environment); Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers; Also covers Model Training; - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
- When should I choose deep-research over LLMSys-PaperList?
- Choose deep-research over LLMSys-PaperList when deep-research is primarily JavaScript; LLMSys-PaperList is Python; Tags unique to deep-research: anthropic, deep-research-api, gemini, grok; deep-research ships Docker support for self-hosted deployment; - When requiring an API interface that supports Server-Sent Events (SSE) and Model Control Protocol (MCP) for integrating large language models.
- When should I avoid LLMSys-PaperList?
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - When your primary need is documentation or code examples rather than academic papers and project insights. - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
- When should I avoid deep-research?
- - When working with environments that do not support JavaScript, as Deep Research is primarily built on this language - For projects that require real-time bidirectional communication with models, as Deep Research might only provide unidirectional data flow through SSE
- Is LLMSys-PaperList or deep-research more popular on GitHub?
- deep-research has more GitHub stars (4,688 vs 2,241). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMSys-PaperList and deep-research open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LLMSys-PaperList or deep-research?
- GraphCanon lists graph-backed alternatives at LLMSys-PaperList alternatives and deep-research alternatives (LLMSys-PaperList 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, LLMSys-PaperList or deep-research?
- LLMSys-PaperList: Steady. deep-research: Slowing. 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 LLMSys-PaperList and deep-research?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMSys-PaperList trust report; deep-research trust report.