--- title: "search vs fastembed" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelindar-search-vs-qdrant-fastembed" tools: ["kelindar-search", "qdrant-fastembed"] --- # search vs fastembed *GraphCanon updated Aug 23, 2026* ## 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. [search](https://github.com/kelindar/search) reports 558 GitHub stars, 24 forks, and 5 open issues, last pushed Mar 6, 2026. [fastembed](https://qdrant.github.io/fastembed/) has 3.2k stars, 231 forks, and 111 open issues, last pushed Aug 19, 2026. Figures are from public GitHub metadata via [search's repository](https://github.com/kelindar/search) and [fastembed's repository](https://github.com/qdrant/fastembed). | | [search](/tools/kelindar-search.md) | [fastembed](/tools/qdrant-fastembed.md) | | --- | --- | --- | | Tagline | Go library for embedded vector search and semantic embeddings with llamacpp | Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings | | Stars | 558 | 3,158 | | Forks | 24 | 231 | | Open issues | 5 | 111 | | Language | Go | Python | | Adopt for | search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. | Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 License | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [search](/tools/kelindar-search.md) | [fastembed](/tools/qdrant-fastembed.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 169d | 2d | | Open issues (now) | 5 | 111 | | Stars delta | +3 (30d) | +55 (30d) | | Open issues delta | 0 (30d) | -26 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/kelindar-search/trust.md) | [trust report](/tools/qdrant-fastembed/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: fastembed - **Requirements:** Does not require Docker, making the setup straightforward for Python environments. - **Adopt for:** Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. - **License detail:** Apache-2.0 License ## Choose when ### 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 ### 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 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 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. ## 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](/tools/kelindar-search/alternatives) and [fastembed alternatives](/tools/qdrant-fastembed/alternatives) ([search markdown twin](/tools/kelindar-search/alternatives.md), [fastembed markdown twin](/tools/qdrant-fastembed/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-qdrant-fastembed.md) 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](/tools/kelindar-search/trust); [fastembed trust report](/tools/qdrant-fastembed/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/_