---
title: "raft vs awesome-llm-apps"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/nvidia-raft-vs-shubhamsaboo-awesome-llm-apps"
tools: ["nvidia-raft", "shubhamsaboo-awesome-llm-apps"]
---

# raft vs awesome-llm-apps

*GraphCanon updated Aug 23, 2026*

## 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.

[raft](https://docs.rapids.ai/api/raft/stable/) reports 1.0k GitHub stars, 248 forks, and 446 open issues, last pushed Aug 22, 2026. [awesome-llm-apps](https://www.theunwindai.com) has 131k stars, 19k forks, and 13 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [raft's repository](https://github.com/NVIDIA/raft) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps).

| | [raft](/tools/nvidia-raft.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) |
| --- | --- | --- |
| Tagline | A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications. | Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy. |
| Stars | 1,036 | 131,230 |
| Forks | 248 | 19,346 |
| Open issues | 446 | 13 |
| Language | Cuda | Python |
| Adopt for | RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. | 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 | - | - |
| Runtime | - | - |
| License | Apache-2.0 | 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. |
| Categories | Data & Retrieval, Model Training | AI Agents, Data & Retrieval |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [raft](/tools/nvidia-raft.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) |
| --- | --- | --- |
| Days since push | 1d | 4d |
| Open issues (now) | 446 | 13 |
| Stars delta | +5 (30d) | +14k (30d) |
| Open issues delta | +2 (30d) | +6 (30d) |
| Owner type | Organization | User |
| Full report | [trust report](/tools/nvidia-raft/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) |

## Shared compatibility

- **Python**: [raft](/tools/nvidia-raft.md) - Python runtime; [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - Python runtime

## Decision facts: raft

- **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.
- **Adopt for:** RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications.

## Decision facts: awesome-llm-apps

- **Pricing:** freemium - Free with open-source licensing, but commercial exploitation is allowed.
- **Adopt for:** 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.
- **License detail:** 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.

## Choose when

### 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.

### 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 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 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.

## 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](/tools/nvidia-raft/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([raft markdown twin](/tools/nvidia-raft/alternatives.md), [awesome-llm-apps markdown twin](/tools/shubhamsaboo-awesome-llm-apps/alternatives.md)), 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](/compare/nvidia-raft-vs-shubhamsaboo-awesome-llm-apps.md) 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](/tools/nvidia-raft/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=nvidia-raft`](/api/graphcanon/graph?tool=nvidia-raft)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_