Comparison
DeepResearch vs awesome-LLM-resources
Verdict
Pick DeepResearch if deepResearch is an open-source AI research agent specialized for information-seeking tasks, built with Python and licensed under Apache-2.0; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.
Markdown twin · DeepResearch alternatives · awesome-LLM-resources alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | DeepResearch | awesome-LLM-resources |
|---|---|---|
| Maintenance | Slowing (172d since push) As of Aug 19, 2026 · github_public_v1 | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 19, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 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
- DeepResearch
- Tongyi Deep Research, the Leading Open-source Deep Research Agent
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- DeepResearch
- 20k
- awesome-LLM-resources
- 9.0k
Forks
- DeepResearch
- 1.5k
- awesome-LLM-resources
- 993
Open issues
- DeepResearch
- 92
- awesome-LLM-resources
- 40
Language
- DeepResearch
- Python
- awesome-LLM-resources
- -
Adopt for
- DeepResearch
- DeepResearch is an open-source AI research agent specialized for information-seeking tasks, built with Python and licensed under Apache-2.0.
- awesome-LLM-resources
- awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.
Persona
- DeepResearch
- -
- awesome-LLM-resources
- -
Runtime
- DeepResearch
- -
- awesome-LLM-resources
- -
License
- DeepResearch
- Apache-2.0
- awesome-LLM-resources
- The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution.
Last pushed
- DeepResearch
- Feb 27, 2026
- awesome-LLM-resources
- Sep 14, 2026
Categories
- DeepResearch
- AI Agents, Inference & Serving
- awesome-LLM-resources
- AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- DeepResearch
- Slowing (36%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- DeepResearch
- 172d
- awesome-LLM-resources
- 3d
Open issues (now)
- DeepResearch
- 92
- awesome-LLM-resources
- 40
Stars delta
- DeepResearch
- +161 (30d)
- awesome-LLM-resources
- +123 (30d)
Open issues delta
- DeepResearch
- 0 (30d)
- awesome-LLM-resources
- +17 (30d)
Owner type
- DeepResearch
- Organization
- awesome-LLM-resources
- User
OSV dependency advisories
- DeepResearch
- Published findings
- awesome-LLM-resources
- No lockfile (source not queried)
Full report
- DeepResearch
- Trust report
- awesome-LLM-resources
- Trust report
Choose DeepResearch if…
- Tags unique to DeepResearch: agent, alibaba, artificial-intelligence, deep-research.
- When you need a tool backed by Alibaba’s ecosystem that offers a robust environment for conducting deep research using AI techniques.
- More GitHub stars (20k vs 9.0k) - visibility, not fit.
When NOT to use DeepResearch
- Avoid DeepResearch if you require specialized features that are not covered under its deep research and information-seeking scope.
- Do not use this tool if your project necessitates proprietary solutions, as the open-source nature might be a constraint in such scenarios.
Choose awesome-LLM-resources if…
- Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs..
- Requirements: The repository does not specify any technical requirements for accessing its content..
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models.
- Also covers Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, LLM Frameworks, Model Training.
- When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
When NOT to use awesome-LLM-resources
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider.
- When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
Explore
DeepResearch trust report →awesome-LLM-resources trust report →AI Agents category →Inference & Serving category →Computer Vision category →Data & Retrieval category →Developer Tools category →Evaluation & Observability category →LLM Frameworks category →Model Training category →All comparisonsStack workflowsTrending tools
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Alibaba-NLP/DeepResearch) · observed Sep 20, 2026
- GitHub forks (Alibaba-NLP/DeepResearch) · observed Sep 20, 2026
- Last push (Alibaba-NLP/DeepResearch) · observed Feb 27, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Sep 14, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: DeepResearch 20k · awesome-LLM-resources 9.0k (synced Sep 20, 2026).
Common questions
- What is the difference between DeepResearch and awesome-LLM-resources?
- DeepResearch: Tongyi Deep Research, the Leading Open-source Deep Research Agent. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose DeepResearch over awesome-LLM-resources?
- Choose DeepResearch over awesome-LLM-resources when Tags unique to DeepResearch: agent, alibaba, artificial-intelligence, deep-research; When you need a tool backed by Alibaba’s ecosystem that offers a robust environment for conducting deep research using AI techniques; More GitHub stars (20k vs 9.0k) - visibility, not fit.
- When should I choose awesome-LLM-resources over DeepResearch?
- Choose awesome-LLM-resources over DeepResearch when Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, LLM Frameworks, Model Training; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
- When should I avoid DeepResearch?
- Avoid DeepResearch if you require specialized features that are not covered under its deep research and information-seeking scope. Do not use this tool if your project necessitates proprietary solutions, as the open-source nature might be a constraint in such scenarios.
- When should I avoid awesome-LLM-resources?
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
- Is DeepResearch or awesome-LLM-resources more popular on GitHub?
- DeepResearch has more GitHub stars (19,846 vs 8,968). Stars measure visibility, not whether either tool fits your constraints.
- Are DeepResearch and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub (DeepResearch: Apache-2.0, awesome-LLM-resources: Apache-2.0).
- Where can I find alternatives to DeepResearch or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at DeepResearch alternatives and awesome-LLM-resources alternatives (DeepResearch markdown twin, awesome-LLM-resources 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, DeepResearch or awesome-LLM-resources?
- DeepResearch: Slowing. awesome-LLM-resources: 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 DeepResearch and awesome-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepResearch trust report; awesome-LLM-resources trust report.