Comparison
raft vs awesome
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.
Markdown twin · raft alternatives · awesome alternatives
GraphCanon updated today
Trust & integrity
| Signal | raft | awesome |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- 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
Stars
- raft
- 1.0k
- awesome
- 484k
Forks
- raft
- 240
- awesome
- 36k
Open issues
- raft
- 448
- awesome
- 92
Language
- raft
- Cuda
- awesome
- -
Adopt for
- raft
- -
- awesome
- -
Persona
- raft
- -
- awesome
- -
Runtime
- raft
- -
- awesome
- -
License
- raft
- Apache-2.0
- awesome
- CC0-1.0
Last pushed
- raft
- Jul 11, 2026
- awesome
- Jun 30, 2026
Categories
- raft
- Vector Databases, LLM Frameworks, Data & Retrieval
- awesome
- LLM Frameworks
Trust and health
Maintenance
- raft
- Very active (96%)
- awesome
- Active (82%)
Days since push
- raft
- 0d
- awesome
- 11d
Open issues (now)
- raft
- 448
- awesome
- 92
Owner type
- raft
- Organization
- awesome
- User
Full report
- raft
- Trust report
- awesome
- Trust report
Choose raft if…
- License: raft is Apache-2.0, awesome is CC0-1.0.
- Tags unique to raft: clustering, anns, gpu, building-blocks.
- Also covers Vector Databases, Data & Retrieval.
When NOT to use raft
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
Choose awesome if…
- License: awesome is CC0-1.0, raft is Apache-2.0.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 1.0k) - visibility, not fit.
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.
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 Jul 11, 2026
- GitHub forks (NVIDIA/raft) · observed Jul 11, 2026
- Last push (NVIDIA/raft) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: raft 1.0k · awesome 484k (synced Jul 11, 2026).
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: clustering, anns, gpu, building-blocks; Also covers Vector Databases, Data & Retrieval.
- 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: resources, awesome-list; More GitHub stars (484k vs 1.0k) - visibility, not fit.
- When should I avoid raft?
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
- 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 and awesome alternatives (raft markdown twin, awesome 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?
- 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; awesome trust report.