Comparison
raft vs awesome-llm-apps
Verdict
Pick raft if rAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications; 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 · raft alternatives · awesome-llm-apps alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | raft | awesome-llm-apps |
|---|---|---|
| Maintenance | Very active (1d since push) As of today · github_public_v1 | Very active (4d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- raft
- A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications.
- awesome-llm-apps
- Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.
Stars
- raft
- 1.0k
- awesome-llm-apps
- 131k
Forks
- raft
- 248
- awesome-llm-apps
- 19k
Open issues
- raft
- 446
- awesome-llm-apps
- 13
Language
- raft
- Cuda
- awesome-llm-apps
- Python
Adopt for
- raft
- RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications.
- 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
- raft
- -
- awesome-llm-apps
- -
Runtime
- raft
- -
- awesome-llm-apps
- -
License
- raft
- Apache-2.0
- 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
- raft
- Aug 22, 2026
- awesome-llm-apps
- Aug 3, 2026
Categories
- raft
- Data & Retrieval, Model Training
- awesome-llm-apps
- AI Agents, Data & Retrieval
Trust and health
Days since push
- raft
- 1d
- awesome-llm-apps
- 4d
Open issues (now)
- raft
- 446
- awesome-llm-apps
- 13
Stars delta
- raft
- +5 (30d)
- awesome-llm-apps
- +14k (30d)
Open issues delta
- raft
- +2 (30d)
- awesome-llm-apps
- +6 (30d)
Owner type
- raft
- Organization
- awesome-llm-apps
- User
Full report
- raft
- Trust report
- awesome-llm-apps
- Trust report
Shared compatibility
- Python · raft: Python runtime · awesome-llm-apps: Python runtime
Choose raft if…
- raft is primarily Cuda; awesome-llm-apps is Python.
- Requirements: Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT..
- Tags unique to raft: anns, building-blocks, clustering, cuda.
- Also covers Model Training.
- - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives.
When NOT to use raft
- - Your application does not have access to NVIDIA GPUs, as RAFT's algorithms leverage CUDA specifically for performance gains.
- - If your workload requires more generalized machine learning libraries without a dependency on GPU-accelerated primitives and you are working in a multi-platform or cross-vendor environment.
Choose awesome-llm-apps if…
- awesome-llm-apps is primarily Python; raft is Cuda.
- 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 (NVIDIA/raft) · observed Aug 23, 2026
- GitHub forks (NVIDIA/raft) · observed Aug 23, 2026
- Last push (NVIDIA/raft) · observed Aug 22, 2026
- License file (Apache-2.0) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 12, 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: raft 1.0k · awesome-llm-apps 131k (synced Aug 23, 2026).
Common questions
- What is the difference between raft and awesome-llm-apps?
- raft: A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications.. 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 raft over awesome-llm-apps?
- Choose raft over awesome-llm-apps when raft is primarily Cuda; awesome-llm-apps is Python; Requirements: Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT.; Tags unique to raft: anns, building-blocks, clustering, cuda; Also covers Model Training; - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives.
- When should I choose awesome-llm-apps over raft?
- Choose awesome-llm-apps over raft when awesome-llm-apps is primarily Python; raft is Cuda; 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 raft?
- - Your application does not have access to NVIDIA GPUs, as RAFT's algorithms leverage CUDA specifically for performance gains. - If your workload requires more generalized machine learning libraries without a dependency on GPU-accelerated primitives and you are working in a multi-platform or cross-vendor environment.
- 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 raft or awesome-llm-apps more popular on GitHub?
- awesome-llm-apps has more GitHub stars (131,230 vs 1,036). Stars measure visibility, not whether either tool fits your constraints.
- Are raft and awesome-llm-apps open source?
- Yes - both are open-source projects on GitHub (raft: Apache-2.0, awesome-llm-apps: Apache-2.0).
- Where can I find alternatives to raft or awesome-llm-apps?
- GraphCanon lists graph-backed alternatives at raft alternatives and awesome-llm-apps alternatives (raft 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, raft or awesome-llm-apps?
- raft: Very active. 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 raft and awesome-llm-apps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: raft trust report; awesome-llm-apps trust report.