--- title: "SeaGOAT vs search" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kantord-seagoat-vs-kelindar-search" tools: ["kantord-seagoat", "kelindar-search"] --- # SeaGOAT vs search *GraphCanon updated Aug 21, 2026* ## Verdict Pick SeaGOAT if seaGOAT leverages local-first processing and semantic embeddings to offer an enhanced understanding of codebases compared to traditional grep-based searches; pick search if search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. [SeaGOAT](https://kantord.github.io/SeaGOAT/) reports 1.3k GitHub stars, 91 forks, and 44 open issues, last pushed Jul 21, 2026. [search](https://github.com/kelindar/search) has 555 stars, 24 forks, and 5 open issues, last pushed Mar 6, 2026. Figures are from public GitHub metadata via [SeaGOAT's repository](https://github.com/kantord/SeaGOAT) and [search's repository](https://github.com/kelindar/search). | | [SeaGOAT](/tools/kantord-seagoat.md) | [search](/tools/kelindar-search.md) | | --- | --- | --- | | Tagline | local-first semantic code search engine | Go library for embedded vector search and semantic embeddings with llamacpp | | Stars | 1,302 | 555 | | Forks | 91 | 24 | | Open issues | 44 | 5 | | Language | Python | Go | | Adopt for | SeaGOAT leverages local-first processing and semantic embeddings to offer an enhanced understanding of codebases compared to traditional grep-based searches. | search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [SeaGOAT](/tools/kantord-seagoat.md) | [search](/tools/kelindar-search.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 31d | 138d | | Open issues (now) | 44 | 5 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/kantord-seagoat/trust.md) | [trust report](/tools/kelindar-search/trust.md) | ## Decision facts: SeaGOAT - **Requirements:** Runs locally and only requires a machine setup with Python environment and possibly extra dependencies for vector database operations. - **Adopt for:** SeaGOAT leverages local-first processing and semantic embeddings to offer an enhanced understanding of codebases compared to traditional grep-based searches. ## 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 SeaGOAT if… - SeaGOAT is primarily Python; search is Go. - Requirements: Runs locally and only requires a machine setup with Python environment and possibly extra dependencies for vector database operations.. - Tags unique to SeaGOAT: code-search, vector-embeddings. - When you are working with large codebases that require a deeper understanding than regular expressions can provide, SeaGOAT's semantic capabilities shine. ### Choose search if… - search is primarily Go; SeaGOAT is Python. - Tags unique to search: bert, gguf, gpu, llamacpp. - Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go ## When NOT to use SeaGOAT - If your primary need is simple pattern matching with text-based operations rather than deeper code semantics, you might find SeaGOAT overkill and prefer a straightforward grep tool instead. - When real-time updates or cloud integration are required for continuous monitoring or remote access to search data, SeaGOAT's local-first approach could be limiting. ## 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 SeaGOAT and search? SeaGOAT: local-first semantic code search engine. 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 SeaGOAT over search? Choose SeaGOAT over search when SeaGOAT is primarily Python; search is Go; Requirements: Runs locally and only requires a machine setup with Python environment and possibly extra dependencies for vector database operations.; Tags unique to SeaGOAT: code-search, vector-embeddings; When you are working with large codebases that require a deeper understanding than regular expressions can provide, SeaGOAT's semantic capabilities shine. ### When should I choose search over SeaGOAT? Choose search over SeaGOAT when search is primarily Go; SeaGOAT is Python; Tags unique to search: bert, gguf, gpu, llamacpp; Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go. ### When should I avoid SeaGOAT? If your primary need is simple pattern matching with text-based operations rather than deeper code semantics, you might find SeaGOAT overkill and prefer a straightforward grep tool instead. When real-time updates or cloud integration are required for continuous monitoring or remote access to search data, SeaGOAT's local-first approach could be limiting. ### 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 SeaGOAT or search more popular on GitHub? SeaGOAT has more GitHub stars (1,302 vs 555). Stars measure visibility, not whether either tool fits your constraints. ### Are SeaGOAT and search open source? Yes - both are open-source projects on GitHub (SeaGOAT: MIT, search: MIT). ### Where can I find alternatives to SeaGOAT or search? GraphCanon lists graph-backed alternatives at [SeaGOAT alternatives](/tools/kantord-seagoat/alternatives) and [search alternatives](/tools/kelindar-search/alternatives) ([SeaGOAT markdown twin](/tools/kantord-seagoat/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/kantord-seagoat-vs-kelindar-search.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, SeaGOAT or search? SeaGOAT: Steady. 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 SeaGOAT and search? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [SeaGOAT trust report](/tools/kantord-seagoat/trust); [search trust report](/tools/kelindar-search/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kantord-seagoat`](/api/graphcanon/graph?tool=kantord-seagoat) - 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/_