Comparison
fastembed-rs vs search
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.
Markdown twin · fastembed-rs alternatives · search alternatives
GraphCanon updated Sep 20, 2026
9views this month
vs
Trust & integrity
| Signal | fastembed-rs | search |
|---|---|---|
| Maintenance | Very active (3d since push) As of Sep 20, 2026 · github_public_v1 | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- fastembed-rs
- Rust library for generating vector embeddings and reranking locally.
- search
- Go library for embedded vector search and semantic embeddings with llamacpp
Stars
- fastembed-rs
- 1.0k
- search
- 559
Forks
- fastembed-rs
- 145
- search
- 24
Open issues
- fastembed-rs
- 0
- search
- 5
Language
- fastembed-rs
- Rust
- search
- Go
Adopt for
- fastembed-rs
- fastembed-rs is a Rust-based library that specializes in generating vector embeddings and performing local reranking to improve retrieval-augmented generation processes.
- search
- search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework.
Persona
- fastembed-rs
- -
- search
- -
Runtime
- fastembed-rs
- -
- search
- -
License
- fastembed-rs
- Apache-2.0
- search
- MIT
Last pushed
- fastembed-rs
- Sep 16, 2026
- search
- Sep 19, 2026
Categories
- fastembed-rs
- Data & Retrieval, Vector Databases
- search
- Data & Retrieval, Vector Databases
Trust and health
Days since push
- fastembed-rs
- 3d
- search
- 0d
Open issues (now)
- fastembed-rs
- 0
- search
- 5
Stars delta
- fastembed-rs
- +44 (30d)
- search
- +4 (30d)
Open issues delta
- fastembed-rs
- -3 (30d)
- search
- 0 (30d)
Full report
- fastembed-rs
- Trust report
- search
- Trust report
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.
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Anush008/fastembed-rs) · observed Sep 20, 2026
- GitHub forks (Anush008/fastembed-rs) · observed Sep 20, 2026
- Last push (Anush008/fastembed-rs) · observed Sep 16, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kelindar/search) · observed Sep 20, 2026
- GitHub forks (kelindar/search) · observed Sep 20, 2026
- Last push (kelindar/search) · observed Sep 19, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: fastembed-rs 1.0k · search 559 (synced Sep 20, 2026).
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 (1,016 vs 559). 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 and search alternatives (fastembed-rs markdown twin, search 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, fastembed-rs or search?
- fastembed-rs: Very active. search: Very 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 search?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: fastembed-rs trust report; search trust report.