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

# raft vs awesome

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick raft when license: raft is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, raft is Apache-2.0.

[raft](https://docs.rapids.ai/api/raft/stable/) reports 1.0k GitHub stars, 240 forks, and 448 open issues, last pushed Jul 11, 2026. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [raft's repository](https://github.com/NVIDIA/raft) and [awesome's repository](https://github.com/sindresorhus/awesome).

| | [raft](/tools/nvidia-raft.md) | [awesome](/tools/sindresorhus-awesome.md) |
| --- | --- | --- |
| Tagline | RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing hig | 😎 Curated list of awesome topics including hardware resources |
| Stars | 1,026 | 484,026 |
| Forks | 240 | 35,799 |
| Open issues | 448 | 92 |
| Language | Cuda | - |
| Adopt for | - | - |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | CC0-1.0 |
| Categories | Data & Retrieval, LLM Frameworks, Vector Databases | LLM Frameworks |

## Trust and health

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

| | [raft](/tools/nvidia-raft.md) | [awesome](/tools/sindresorhus-awesome.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Active (82%) |
| Days since push | 0d | 11d |
| Open issues (now) | 448 | 92 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/nvidia-raft/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) |

## Choose when

### Choose raft if…

- License: raft is Apache-2.0, awesome is CC0-1.0.
- Tags unique to raft: anns, building-blocks, clustering, cuda.
- Also covers Data & Retrieval, Vector Databases.

### Choose awesome if…

- License: awesome is CC0-1.0, raft is Apache-2.0.
- Tags unique to awesome: awesome-list, resources.
- More GitHub stars (484k vs 1.0k) - visibility, not fit.

## When NOT to use raft

- Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## When NOT to use awesome

- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

## Common questions

### What is the difference between raft and awesome?

raft: RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing hig. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.

### When should I choose raft over awesome?

Choose raft over awesome when License: raft is Apache-2.0, awesome is CC0-1.0; Tags unique to raft: anns, building-blocks, clustering, cuda; Also covers Data & Retrieval, Vector Databases.

### When should I choose awesome over raft?

Choose awesome over raft when License: awesome is CC0-1.0, raft is Apache-2.0; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 1.0k) - visibility, not fit.

### When should I avoid raft?

Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### When should I avoid awesome?

LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

### Is raft or awesome more popular on GitHub?

awesome has more GitHub stars (484,026 vs 1,026). Stars measure visibility, not whether either tool fits your constraints.

### Are raft and awesome open source?

Yes - both are open-source projects on GitHub (raft: Apache-2.0, awesome: CC0-1.0).

### Where can I find alternatives to raft or awesome?

GraphCanon lists graph-backed alternatives at [raft alternatives](/tools/nvidia-raft/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([raft markdown twin](/tools/nvidia-raft/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, raft or awesome?

raft: Very active. awesome: 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?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [raft trust report](/tools/nvidia-raft/trust); [awesome trust report](/tools/sindresorhus-awesome/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/_