--- title: "fastembed vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/qdrant-fastembed-vs-sindresorhus-awesome" tools: ["qdrant-fastembed", "sindresorhus-awesome"] --- # fastembed vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick fastembed when license: fastembed is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, fastembed is Apache-2.0. [fastembed](https://qdrant.github.io/fastembed/) reports 3.1k GitHub stars, 213 forks, and 137 open issues, last pushed Jun 23, 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's repository](https://github.com/qdrant/fastembed) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [fastembed](/tools/qdrant-fastembed.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Fast, Accurate, Lightweight Python library to make State of the Art Embedding | 😎 Curated list of awesome topics including hardware resources | | Stars | 3,085 | 484,026 | | Forks | 213 | 35,799 | | Open issues | 137 | 92 | | Language | Python | - | | 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._ | | [fastembed](/tools/qdrant-fastembed.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Days since push | 18d | 11d | | Open issues (now) | 137 | 92 | | Owner type | Organization | User | | Full report | [trust report](/tools/qdrant-fastembed/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose fastembed if… - License: fastembed is Apache-2.0, awesome is CC0-1.0. - Tags unique to fastembed: embeddings, openai, python, rag. - Also covers Data & Retrieval, Vector Databases. ### Choose awesome if… - License: awesome is CC0-1.0, fastembed is Apache-2.0. - Tags unique to awesome: awesome-list, resources. - More GitHub stars (484k vs 3.1k) - visibility, not fit. ## When NOT to use fastembed - 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 fastembed and awesome? fastembed: Fast, Accurate, Lightweight Python library to make State of the Art Embedding. 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 over awesome? Choose fastembed over awesome when License: fastembed is Apache-2.0, awesome is CC0-1.0; Tags unique to fastembed: embeddings, openai, python, rag; Also covers Data & Retrieval, Vector Databases. ### When should I choose awesome over fastembed? Choose awesome over fastembed when License: awesome is CC0-1.0, fastembed is Apache-2.0; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 3.1k) - visibility, not fit. ### When should I avoid fastembed? 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 fastembed or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 3,085). Stars measure visibility, not whether either tool fits your constraints. ### Are fastembed and awesome open source? Yes - both are open-source projects on GitHub (fastembed: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to fastembed or awesome? GraphCanon lists graph-backed alternatives at [fastembed alternatives](/tools/qdrant-fastembed/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([fastembed markdown twin](/tools/qdrant-fastembed/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/qdrant-fastembed-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 or awesome? fastembed: 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 and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [fastembed trust report](/tools/qdrant-fastembed/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=qdrant-fastembed`](/api/graphcanon/graph?tool=qdrant-fastembed) - 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/_