--- title: "search vs EmbedAnything" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelindar-search-vs-starlightsearch-embedanything" tools: ["kelindar-search", "starlightsearch-embedanything"] --- # search vs EmbedAnything *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 EmbedAnything if embedAnything is a Rust-based tool focused on highly performant and modular operations for inference, ingestion, and indexing of large language models, designed with memory safety and production-readiness in mind. [search](https://github.com/kelindar/search) reports 558 GitHub stars, 24 forks, and 5 open issues, last pushed Mar 6, 2026. [EmbedAnything](https://embed-anything.com/) has 1.3k stars, 143 forks, and 21 open issues, last pushed Aug 12, 2026. Figures are from public GitHub metadata via [search's repository](https://github.com/kelindar/search) and [EmbedAnything's repository](https://github.com/StarlightSearch/EmbedAnything). | | [search](/tools/kelindar-search.md) | [EmbedAnything](/tools/starlightsearch-embedanything.md) | | --- | --- | --- | | Tagline | Go library for embedded vector search and semantic embeddings with llamacpp | Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust | | Stars | 558 | 1,304 | | Forks | 24 | 143 | | Open issues | 5 | 21 | | Language | Go | Rust | | Adopt for | search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. | EmbedAnything is a Rust-based tool focused on highly performant and modular operations for inference, ingestion, and indexing of large language models, designed with memory safety and production-readiness in mind. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Inference & Serving, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [search](/tools/kelindar-search.md) | [EmbedAnything](/tools/starlightsearch-embedanything.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 169d | 9d | | Open issues (now) | 5 | 21 | | Stars delta | +3 (30d) | +18 (30d) | | Open issues delta | 0 (30d) | -2 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/kelindar-search/trust.md) | [trust report](/tools/starlightsearch-embedanything/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: EmbedAnything - **Adopt for:** EmbedAnything is a Rust-based tool focused on highly performant and modular operations for inference, ingestion, and indexing of large language models, designed with memory safety and production-readiness in mind. ## Choose when ### Choose search if… - search is primarily Go; EmbedAnything is Rust. - License: search is MIT, EmbedAnything is Apache-2.0. - Tags unique to search: bert, embeddings, gguf, gpu. - Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go ### Choose EmbedAnything if… - EmbedAnything is primarily Rust; search is Go. - License: EmbedAnything is Apache-2.0, search is MIT. - Tags unique to EmbedAnything: cloud, generative-ai, hacktoberfest, high-performance. - Also covers Inference & Serving. - EmbedAnything ships Docker support for self-hosted deployment. - - When you require high performance and memory safety for inference tasks due to its Rust foundation. ## 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 EmbedAnything - - In scenarios requiring direct Python support without additional bridging tools, since EmbedAnything's primary language is Rust. - - If you need a tool heavily optimized for edge computing where minimal memory usage trumps safety and performance considerations. ## Common questions ### What is the difference between search and EmbedAnything? search: Go library for embedded vector search and semantic embeddings with llamacpp. EmbedAnything: Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust. See the comparison table for live GitHub stats and shared categories. ### When should I choose search over EmbedAnything? Choose search over EmbedAnything when search is primarily Go; EmbedAnything is Rust; License: search is MIT, EmbedAnything is Apache-2.0; Tags unique to search: bert, embeddings, gguf, gpu; Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go. ### When should I choose EmbedAnything over search? Choose EmbedAnything over search when EmbedAnything is primarily Rust; search is Go; License: EmbedAnything is Apache-2.0, search is MIT; Tags unique to EmbedAnything: cloud, generative-ai, hacktoberfest, high-performance; Also covers Inference & Serving; EmbedAnything ships Docker support for self-hosted deployment; - When you require high performance and memory safety for inference tasks due to its Rust foundation. ### 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 EmbedAnything? - In scenarios requiring direct Python support without additional bridging tools, since EmbedAnything's primary language is Rust. - If you need a tool heavily optimized for edge computing where minimal memory usage trumps safety and performance considerations. ### Is search or EmbedAnything more popular on GitHub? EmbedAnything has more GitHub stars (1,304 vs 558). Stars measure visibility, not whether either tool fits your constraints. ### Are search and EmbedAnything open source? Yes - both are open-source projects on GitHub (search: MIT, EmbedAnything: Apache-2.0). ### Where can I find alternatives to search or EmbedAnything? GraphCanon lists graph-backed alternatives at [search alternatives](/tools/kelindar-search/alternatives) and [EmbedAnything alternatives](/tools/starlightsearch-embedanything/alternatives) ([search markdown twin](/tools/kelindar-search/alternatives.md), [EmbedAnything markdown twin](/tools/starlightsearch-embedanything/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-starlightsearch-embedanything.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, search or EmbedAnything? search: Slowing. EmbedAnything: 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 EmbedAnything? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [search trust report](/tools/kelindar-search/trust); [EmbedAnything trust report](/tools/starlightsearch-embedanything/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/_