Comparison
apps vs awesome-llm-apps
Verdict
Pick apps if aPPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets; pick awesome-llm-apps if awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python.
Markdown twin · apps alternatives · awesome-llm-apps alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | apps | awesome-llm-apps |
|---|---|---|
| Maintenance | Dormant (777d since push) As of 2w · github_public_v1 | Very active (4d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- apps
- APPS: Automated Programming Progress Standard
- awesome-llm-apps
- Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.
Stars
- apps
- 534
- awesome-llm-apps
- 131k
Forks
- apps
- 70
- awesome-llm-apps
- 19k
Open issues
- apps
- 4
- awesome-llm-apps
- 13
Language
- apps
- Python
- awesome-llm-apps
- Python
Adopt for
- apps
- APPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets.
- awesome-llm-apps
- awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python.
Persona
- apps
- -
- awesome-llm-apps
- -
Runtime
- apps
- -
- awesome-llm-apps
- -
License
- apps
- MIT
- awesome-llm-apps
- The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license.
Last pushed
- apps
- Jun 19, 2024
- awesome-llm-apps
- Aug 3, 2026
Categories
- apps
- Data & Retrieval, Evaluation & Observability
- awesome-llm-apps
- AI Agents, Data & Retrieval
Trust and health
Maintenance
- apps
- Dormant (18%)
- awesome-llm-apps
- Very active (96%)
Days since push
- apps
- 777d
- awesome-llm-apps
- 4d
Open issues (now)
- apps
- 4
- awesome-llm-apps
- 13
Stars delta
- apps
- Unknown
- awesome-llm-apps
- +14k (30d)
Open issues delta
- apps
- Unknown
- awesome-llm-apps
- +6 (30d)
OSV dependency advisories
- apps
- Published findings
- awesome-llm-apps
- No lockfile (source not queried)
Full report
- apps
- Trust report
- awesome-llm-apps
- Trust report
Choose apps if…
- License: apps is MIT, awesome-llm-apps is Apache-2.0.
- Tags unique to apps: code generation, program-synthesis.
- Also covers Evaluation & Observability.
- When you need benchmarking datasets specifically tailored for assessing the performance of your AI in solving programming tasks
When NOT to use apps
- If you solely require general datasets without a focus on coding challenges
- When your use case does not involve using Python-based tools for developing machine learning applications that include program synthesis and code generation
Choose awesome-llm-apps if…
- License: awesome-llm-apps is Apache-2.0, apps is MIT.
- Pricing: Free with open-source licensing, but commercial exploitation is allowed..
- Tags unique to awesome-llm-apps: agents, applications, customizable, deployable.
- Also covers AI Agents.
- When you need quick implementations of various real-world use cases for AI Agents and RAG.
When NOT to use awesome-llm-apps
- If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch.
- When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hendrycks/apps) · observed Aug 5, 2026
- GitHub forks (hendrycks/apps) · observed Aug 5, 2026
- Last push (hendrycks/apps) · observed Jun 19, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Shubhamsaboo/awesome-llm-apps) · observed Aug 7, 2026
- GitHub forks (Shubhamsaboo/awesome-llm-apps) · observed Aug 7, 2026
- Last push (Shubhamsaboo/awesome-llm-apps) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: apps 534 · awesome-llm-apps 131k (synced Aug 5, 2026).
Common questions
- What is the difference between apps and awesome-llm-apps?
- apps: APPS: Automated Programming Progress Standard. awesome-llm-apps: Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.. See the comparison table for live GitHub stats and shared categories.
- When should I choose apps over awesome-llm-apps?
- Choose apps over awesome-llm-apps when License: apps is MIT, awesome-llm-apps is Apache-2.0; Tags unique to apps: code generation, program-synthesis; Also covers Evaluation & Observability; When you need benchmarking datasets specifically tailored for assessing the performance of your AI in solving programming tasks.
- When should I choose awesome-llm-apps over apps?
- Choose awesome-llm-apps over apps when License: awesome-llm-apps is Apache-2.0, apps is MIT; Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: agents, applications, customizable, deployable; Also covers AI Agents; When you need quick implementations of various real-world use cases for AI Agents and RAG.
- When should I avoid apps?
- If you solely require general datasets without a focus on coding challenges When your use case does not involve using Python-based tools for developing machine learning applications that include program synthesis and code generation
- When should I avoid awesome-llm-apps?
- If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction.
- Is apps or awesome-llm-apps more popular on GitHub?
- awesome-llm-apps has more GitHub stars (131,230 vs 534). Stars measure visibility, not whether either tool fits your constraints.
- Are apps and awesome-llm-apps open source?
- Yes - both are open-source projects on GitHub (apps: MIT, awesome-llm-apps: Apache-2.0).
- Where can I find alternatives to apps or awesome-llm-apps?
- GraphCanon lists graph-backed alternatives at apps alternatives and awesome-llm-apps alternatives (apps markdown twin, awesome-llm-apps 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, apps or awesome-llm-apps?
- apps: Dormant. awesome-llm-apps: 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 apps and awesome-llm-apps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: apps trust report; awesome-llm-apps trust report.