--- title: "fastembed-rs vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/anush008-fastembed-rs-vs-sindresorhus-awesome" tools: ["anush008-fastembed-rs", "sindresorhus-awesome"] --- # fastembed-rs vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick fastembed-rs when license: fastembed-rs is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, fastembed-rs is Apache-2.0. [fastembed-rs](https://docs.rs/fastembed) reports 958 GitHub stars, 133 forks, and 2 open issues, last pushed Jun 30, 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 [fastembed-rs's repository](https://github.com/Anush008/fastembed-rs) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [fastembed-rs](/tools/anush008-fastembed-rs.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Rust library for generating vector embeddings, reranking locally! | 😎 Curated list of awesome topics including hardware resources | | Stars | 958 | 484,026 | | Forks | 133 | 35,799 | | Open issues | 2 | 92 | | Language | Rust | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | Vector Databases, Data & Retrieval, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [fastembed-rs](/tools/anush008-fastembed-rs.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Days since push | 10d | 11d | | Open issues (now) | 2 | 92 | | Full report | [trust report](/tools/anush008-fastembed-rs/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose fastembed-rs if… - License: fastembed-rs is Apache-2.0, awesome is CC0-1.0. - Tags unique to fastembed-rs: embeddings, fastembed, reranker, rag. - Also covers Vector Databases, Data & Retrieval. ### Choose awesome if… - License: awesome is CC0-1.0, fastembed-rs is Apache-2.0. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 958) - visibility, not fit. ## When NOT to use fastembed-rs - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - 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. ## 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 fastembed-rs and awesome? fastembed-rs: Rust library for generating vector embeddings, reranking locally!. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose fastembed-rs over awesome? Choose fastembed-rs over awesome when License: fastembed-rs is Apache-2.0, awesome is CC0-1.0; Tags unique to fastembed-rs: embeddings, fastembed, reranker, rag; Also covers Vector Databases, Data & Retrieval. ### When should I choose awesome over fastembed-rs? Choose awesome over fastembed-rs when License: awesome is CC0-1.0, fastembed-rs is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 958) - visibility, not fit. ### When should I avoid fastembed-rs? Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. 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. ### 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 fastembed-rs or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 958). Stars measure visibility, not whether either tool fits your constraints. ### Are fastembed-rs and awesome open source? Yes - both are open-source projects on GitHub (fastembed-rs: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to fastembed-rs or awesome? GraphCanon lists graph-backed alternatives at [fastembed-rs alternatives](/tools/anush008-fastembed-rs/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([fastembed-rs markdown twin](/tools/anush008-fastembed-rs/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/anush008-fastembed-rs-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, fastembed-rs or awesome? fastembed-rs: 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 fastembed-rs and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [fastembed-rs trust report](/tools/anush008-fastembed-rs/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=anush008-fastembed-rs`](/api/graphcanon/graph?tool=anush008-fastembed-rs) - 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/_