--- title: "fastembed-rs vs search" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/anush008-fastembed-rs-vs-kelindar-search" tools: ["anush008-fastembed-rs", "kelindar-search"] --- # fastembed-rs vs search *GraphCanon updated Aug 23, 2026* ## Verdict Pick fastembed-rs if fastembed-rs is a Rust-based library that specializes in generating vector embeddings and performing local reranking to improve retrieval-augmented generation processes; pick search if search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. [fastembed-rs](https://docs.rs/fastembed) reports 992 GitHub stars, 136 forks, and 1 open issues, last pushed Aug 16, 2026. [search](https://github.com/kelindar/search) has 558 stars, 24 forks, and 5 open issues, last pushed Mar 6, 2026. Figures are from public GitHub metadata via [fastembed-rs's repository](https://github.com/Anush008/fastembed-rs) and [search's repository](https://github.com/kelindar/search). | | [fastembed-rs](/tools/anush008-fastembed-rs.md) | [search](/tools/kelindar-search.md) | | --- | --- | --- | | Tagline | Rust library for generating vector embeddings and reranking locally. | Go library for embedded vector search and semantic embeddings with llamacpp | | Stars | 992 | 558 | | Forks | 136 | 24 | | Open issues | 1 | 5 | | Language | Rust | Go | | Adopt for | fastembed-rs is a Rust-based library that specializes in generating vector embeddings and performing local reranking to improve retrieval-augmented generation processes. | search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [fastembed-rs](/tools/anush008-fastembed-rs.md) | [search](/tools/kelindar-search.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 6d | 169d | | Open issues (now) | 1 | 5 | | Stars delta | +20 (30d) | +3 (30d) | | Open issues delta | -2 (30d) | 0 (30d) | | Full report | [trust report](/tools/anush008-fastembed-rs/trust.md) | [trust report](/tools/kelindar-search/trust.md) | ## Decision facts: fastembed-rs - **Adopt for:** fastembed-rs is a Rust-based library that specializes in generating vector embeddings and performing local reranking to improve retrieval-augmented generation processes. ## Decision facts: search - **Adopt for:** search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. ## Choose when ### Choose fastembed-rs if… - fastembed-rs is primarily Rust; search is Go. - License: fastembed-rs is Apache-2.0, search is MIT. - Tags unique to fastembed-rs: fastembed, rag, reranker, reranking. - When you seek high-performance embedding generation within an application written in Rust. ### Choose search if… - search is primarily Go; fastembed-rs is Rust. - License: search is MIT, fastembed-rs is Apache-2.0. - Tags unique to search: ai, bert, gguf, gpu. - Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go ## When NOT to use fastembed-rs - Avoid if your project demands integration with languages other than Rust, as the tool does not offer bindings for other programming languages. - Not recommended when the primary focus is on distributed or cloud-based embedding services, as fastembed-rs focuses specifically on local processing. ## 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 ## Common questions ### What is the difference between fastembed-rs and search? fastembed-rs: Rust library for generating vector embeddings and reranking locally.. search: Go library for embedded vector search and semantic embeddings with llamacpp. See the comparison table for live GitHub stats and shared categories. ### When should I choose fastembed-rs over search? Choose fastembed-rs over search when fastembed-rs is primarily Rust; search is Go; License: fastembed-rs is Apache-2.0, search is MIT; Tags unique to fastembed-rs: fastembed, rag, reranker, reranking; When you seek high-performance embedding generation within an application written in Rust. ### When should I choose search over fastembed-rs? Choose search over fastembed-rs when search is primarily Go; fastembed-rs is Rust; License: search is MIT, fastembed-rs is Apache-2.0; Tags unique to search: ai, bert, gguf, gpu; Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go. ### When should I avoid fastembed-rs? Avoid if your project demands integration with languages other than Rust, as the tool does not offer bindings for other programming languages. Not recommended when the primary focus is on distributed or cloud-based embedding services, as fastembed-rs focuses specifically on local processing. ### 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 ### Is fastembed-rs or search more popular on GitHub? fastembed-rs has more GitHub stars (992 vs 558). Stars measure visibility, not whether either tool fits your constraints. ### Are fastembed-rs and search open source? Yes - both are open-source projects on GitHub (fastembed-rs: Apache-2.0, search: MIT). ### Where can I find alternatives to fastembed-rs or search? GraphCanon lists graph-backed alternatives at [fastembed-rs alternatives](/tools/anush008-fastembed-rs/alternatives) and [search alternatives](/tools/kelindar-search/alternatives) ([fastembed-rs markdown twin](/tools/anush008-fastembed-rs/alternatives.md), [search markdown twin](/tools/kelindar-search/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-kelindar-search.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, fastembed-rs or search? fastembed-rs: Very active. search: Slowing. 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 search? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [fastembed-rs trust report](/tools/anush008-fastembed-rs/trust); [search trust report](/tools/kelindar-search/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/_