Comparison
search vs fastembed
Verdict
Pick search if search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework; pick fastembed if fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings.
Markdown twin · search alternatives · fastembed alternatives
GraphCanon updated 1d
Trust & integrity
| Signal | search | fastembed |
|---|---|---|
| Maintenance | Slowing (169d since push) As of 1d · github_public_v1 | Very active (2d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1d · github_public_v1 | Not a fork · Organization account As of 2d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- search
- Go library for embedded vector search and semantic embeddings with llamacpp
- fastembed
- Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings
Stars
- search
- 558
- fastembed
- 3.2k
Forks
- search
- 24
- fastembed
- 231
Open issues
- search
- 5
- fastembed
- 111
Language
- search
- Go
- fastembed
- Python
Adopt for
- search
- search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework.
- fastembed
- Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings.
Persona
- search
- -
- fastembed
- -
Runtime
- search
- -
- fastembed
- -
License
- search
- MIT
- fastembed
- Apache-2.0 License
Last pushed
- search
- Mar 6, 2026
- fastembed
- Aug 19, 2026
Categories
- search
- Data & Retrieval, Vector Databases
- fastembed
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- search
- Slowing (36%)
- fastembed
- Very active (96%)
Days since push
- search
- 169d
- fastembed
- 2d
Open issues (now)
- search
- 5
- fastembed
- 111
Stars delta
- search
- +3 (30d)
- fastembed
- +55 (30d)
Open issues delta
- search
- 0 (30d)
- fastembed
- -26 (30d)
Owner type
- search
- User
- fastembed
- Organization
Full report
- search
- Trust report
- fastembed
- Trust report
Choose search if…
- search is primarily Go; fastembed is Python.
- License: search is MIT, fastembed 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
Choose fastembed if…
- fastembed is primarily Python; search is Go.
- License: fastembed is Apache-2.0, search is MIT.
- Requirements: Does not require Docker, making the setup straightforward for Python environments..
- Tags unique to fastembed: openai, rag, retrieval-augmented-generation, vector-search.
- When you need to generate high-quality embeddings quickly in Python.
When NOT to use fastembed
- If your project is not using Python, as Fastembed does not offer support for other programming languages directly.
- In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (kelindar/search) · observed Aug 23, 2026
- GitHub forks (kelindar/search) · observed Aug 23, 2026
- Last push (kelindar/search) · observed Mar 6, 2026
- License file (MIT) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (qdrant/fastembed) · observed Aug 22, 2026
- GitHub forks (qdrant/fastembed) · observed Aug 22, 2026
- Last push (qdrant/fastembed) · observed Aug 19, 2026
- License file (Apache-2.0) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: search 558 · fastembed 3.2k (synced Aug 23, 2026).
Common questions
- What is the difference between search and fastembed?
- search: Go library for embedded vector search and semantic embeddings with llamacpp. fastembed: Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings. See the comparison table for live GitHub stats and shared categories.
- When should I choose search over fastembed?
- Choose search over fastembed when search is primarily Go; fastembed is Python; License: search is MIT, fastembed 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 choose fastembed over search?
- Choose fastembed over search when fastembed is primarily Python; search is Go; License: fastembed is Apache-2.0, search is MIT; Requirements: Does not require Docker, making the setup straightforward for Python environments.; Tags unique to fastembed: openai, rag, retrieval-augmented-generation, vector-search; When you need to generate high-quality embeddings quickly in Python.
- 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 fastembed?
- If your project is not using Python, as Fastembed does not offer support for other programming languages directly. In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility.
- Is search or fastembed more popular on GitHub?
- fastembed has more GitHub stars (3,158 vs 558). Stars measure visibility, not whether either tool fits your constraints.
- Are search and fastembed open source?
- Yes - both are open-source projects on GitHub (search: MIT, fastembed: Apache-2.0).
- Where can I find alternatives to search or fastembed?
- GraphCanon lists graph-backed alternatives at search alternatives and fastembed alternatives (search markdown twin, fastembed 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, search or fastembed?
- search: Slowing. fastembed: 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 search and fastembed?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: search trust report; fastembed trust report.