--- title: "embedbase vs search" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/different-ai-embedbase-vs-kelindar-search" tools: ["different-ai-embedbase", "kelindar-search"] --- # embedbase vs search *GraphCanon updated Aug 23, 2026* ## Verdict Pick embedbase if embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases; pick search if search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. [embedbase](https://docs.embedbase.xyz) reports 523 GitHub stars, 54 forks, and 35 open issues, last pushed Nov 27, 2024. [search](https://github.com/kelindar/search) has 558 stars, 24 forks, and 5 open issues, last pushed Mar 6, 2026. Figures are from public GitHub metadata via [embedbase's repository](https://github.com/different-ai/embedbase) and [search's repository](https://github.com/kelindar/search). | | [embedbase](/tools/different-ai-embedbase.md) | [search](/tools/kelindar-search.md) | | --- | --- | --- | | Tagline | A dead-simple API to build LLM-powered apps | Go library for embedded vector search and semantic embeddings with llamacpp | | Stars | 523 | 558 | | Forks | 54 | 24 | | Open issues | 35 | 5 | | Language | TypeScript | Go | | Adopt for | Embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases. | 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._ | | [embedbase](/tools/different-ai-embedbase.md) | [search](/tools/kelindar-search.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 632d | 169d | | Open issues (now) | 35 | 5 | | Stars delta | -1 (30d) | +3 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/different-ai-embedbase/trust.md) | [trust report](/tools/kelindar-search/trust.md) | ## Decision facts: embedbase - **Adopt for:** Embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases. ## 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 embedbase if… - embedbase is primarily TypeScript; search is Go. - Tags unique to embedbase: artificial-intelligence, chatgpt, machine-learning, natural-language-processing. - * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### Choose search if… - search is primarily Go; embedbase is TypeScript. - 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 embedbase - * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python. - * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers. ## 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 embedbase and search? embedbase: A dead-simple API to build LLM-powered apps. 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 embedbase over search? Choose embedbase over search when embedbase is primarily TypeScript; search is Go; Tags unique to embedbase: artificial-intelligence, chatgpt, machine-learning, natural-language-processing; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### When should I choose search over embedbase? Choose search over embedbase when search is primarily Go; embedbase is TypeScript; 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 embedbase? * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python. * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers. ### 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 embedbase or search more popular on GitHub? search has more GitHub stars (558 vs 523). Stars measure visibility, not whether either tool fits your constraints. ### Are embedbase and search open source? Yes - both are open-source projects on GitHub (embedbase: MIT, search: MIT). ### Where can I find alternatives to embedbase or search? GraphCanon lists graph-backed alternatives at [embedbase alternatives](/tools/different-ai-embedbase/alternatives) and [search alternatives](/tools/kelindar-search/alternatives) ([embedbase markdown twin](/tools/different-ai-embedbase/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/different-ai-embedbase-vs-kelindar-search.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, embedbase or search? embedbase: Dormant. 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 embedbase and search? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [embedbase trust report](/tools/different-ai-embedbase/trust); [search trust report](/tools/kelindar-search/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=different-ai-embedbase`](/api/graphcanon/graph?tool=different-ai-embedbase) - 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/_