--- title: "search vs awesome-2vec" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelindar-search-vs-maxwellrebo-awesome-2vec" tools: ["kelindar-search", "maxwellrebo-awesome-2vec"] --- # search vs awesome-2vec *GraphCanon updated Sep 20, 2026* ## Verdict Pick search if search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework; pick awesome-2vec if curated list of various 2Vec embedding models, essential for specific AI applications needing diverse model approaches. [search](https://github.com/kelindar/search) reports 559 GitHub stars, 24 forks, and 5 open issues, last pushed Sep 19, 2026. [awesome-2vec](https://github.com/MaxwellRebo/awesome-2vec) has 933 stars, 179 forks, and 0 open issues, last pushed Dec 8, 2022. Figures are from public GitHub metadata via [search's repository](https://github.com/kelindar/search) and [awesome-2vec's repository](https://github.com/MaxwellRebo/awesome-2vec). | | [search](/tools/kelindar-search.md) | [awesome-2vec](/tools/maxwellrebo-awesome-2vec.md) | | --- | --- | --- | | Tagline | Go library for embedded vector search and semantic embeddings with llamacpp | Curated list of 2vec-type embedding models | | Stars | 559 | 933 | | Forks | 24 | 179 | | Open issues | 5 | 0 | | Language | Go | - | | Adopt for | search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. | Curated list of various 2Vec embedding models, essential for specific AI applications needing diverse model approaches. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Data & Retrieval, Vector Databases | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [search](/tools/kelindar-search.md) | [awesome-2vec](/tools/maxwellrebo-awesome-2vec.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1381d | | Open issues (now) | 5 | 0 | | Stars delta | +4 (30d) | -1 (30d) | | Full report | [trust report](/tools/kelindar-search/trust.md) | [trust report](/tools/maxwellrebo-awesome-2vec/trust.md) | ## Decision facts: search - **Adopt for:** search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. ## Decision facts: awesome-2vec - **Adopt for:** Curated list of various 2Vec embedding models, essential for specific AI applications needing diverse model approaches. ## Choose when ### Choose search if… - Tags unique to search: ai, bert, gguf, gpu. - Also covers Data & Retrieval. - Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go ### Choose awesome-2vec if… - Tags unique to awesome-2vec: list, model. - Need a variety of pre-implemented 2Vec embedding models - More GitHub stars (933 vs 559) - visibility, not fit. ## When NOT to use search - Avoid if relying on out-of-the-box support beyond Go or requiring heavy customization that is not supported directly by llama.cpp's capabilities - Not suitable when a more comprehensive database service with extensive querying and integration features is desired over an embedded solution ## When NOT to use awesome-2vec - Seeking specialized, deep integration with a single embedding model type - Project requires real-time tuning or development of unique 2Vec models ## Common questions ### What is the difference between search and awesome-2vec? search: Go library for embedded vector search and semantic embeddings with llamacpp. awesome-2vec: Curated list of 2vec-type embedding models. See the comparison table for live GitHub stats and shared categories. ### When should I choose search over awesome-2vec? Choose search over awesome-2vec when Tags unique to search: ai, bert, gguf, gpu; Also covers Data & Retrieval; Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go. ### When should I choose awesome-2vec over search? Choose awesome-2vec over search when Tags unique to awesome-2vec: list, model; Need a variety of pre-implemented 2Vec embedding models; More GitHub stars (933 vs 559) - visibility, not fit. ### When should I avoid search? Avoid if relying on out-of-the-box support beyond Go or requiring heavy customization that is not supported directly by llama.cpp's capabilities Not suitable when a more comprehensive database service with extensive querying and integration features is desired over an embedded solution ### When should I avoid awesome-2vec? Seeking specialized, deep integration with a single embedding model type Project requires real-time tuning or development of unique 2Vec models ### Is search or awesome-2vec more popular on GitHub? awesome-2vec has more GitHub stars (933 vs 559). Stars measure visibility, not whether either tool fits your constraints. ### Are search and awesome-2vec open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to search or awesome-2vec? GraphCanon lists graph-backed alternatives at [search alternatives](/tools/kelindar-search/alternatives) and [awesome-2vec alternatives](/tools/maxwellrebo-awesome-2vec/alternatives) ([search markdown twin](/tools/kelindar-search/alternatives.md), [awesome-2vec markdown twin](/tools/maxwellrebo-awesome-2vec/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/kelindar-search-vs-maxwellrebo-awesome-2vec.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, search or awesome-2vec? search: Very active. awesome-2vec: Dormant. 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 search and awesome-2vec? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [search trust report](/tools/kelindar-search/trust); [awesome-2vec trust report](/tools/maxwellrebo-awesome-2vec/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelindar-search`](/api/graphcanon/graph?tool=kelindar-search) - 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/_