--- title: "embedbase vs weaviate-examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/different-ai-embedbase-vs-weaviate-weaviate-examples" tools: ["different-ai-embedbase", "weaviate-weaviate-examples"] --- # embedbase vs weaviate-examples *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 weaviate-examples if weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. [embedbase](https://docs.embedbase.xyz) reports 523 GitHub stars, 54 forks, and 35 open issues, last pushed Nov 27, 2024. [weaviate-examples](https://github.com/weaviate/weaviate-examples) has 331 stars, 86 forks, and 12 open issues, last pushed Aug 7, 2025. Figures are from public GitHub metadata via [embedbase's repository](https://github.com/different-ai/embedbase) and [weaviate-examples's repository](https://github.com/weaviate/weaviate-examples). | | [embedbase](/tools/different-ai-embedbase.md) | [weaviate-examples](/tools/weaviate-weaviate-examples.md) | | --- | --- | --- | | Tagline | A dead-simple API to build LLM-powered apps | Weaviate vector database – examples | | Stars | 523 | 331 | | Forks | 54 | 86 | | Open issues | 35 | 12 | | Language | TypeScript | HTML | | 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. | weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. | | 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) | [weaviate-examples](/tools/weaviate-weaviate-examples.md) | | --- | --- | --- | | Days since push | 632d | 380d | | Open issues (now) | 35 | 12 | | Full report | [trust report](/tools/different-ai-embedbase/trust.md) | [trust report](/tools/weaviate-weaviate-examples/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: weaviate-examples - **Adopt for:** weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. ## Choose when ### Choose embedbase if… - embedbase is primarily TypeScript; weaviate-examples is HTML. - Tags unique to embedbase: ai, artificial-intelligence, chatgpt, embeddings. - * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### Choose weaviate-examples if… - weaviate-examples is primarily HTML; embedbase is TypeScript. - Tags unique to weaviate-examples: deep-learning, examples, vector-search, vector-search-engine. - You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality. ## 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 weaviate-examples - Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing. - You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics. ## Common questions ### What is the difference between embedbase and weaviate-examples? embedbase: A dead-simple API to build LLM-powered apps. weaviate-examples: Weaviate vector database – examples. See the comparison table for live GitHub stats and shared categories. ### When should I choose embedbase over weaviate-examples? Choose embedbase over weaviate-examples when embedbase is primarily TypeScript; weaviate-examples is HTML; Tags unique to embedbase: ai, artificial-intelligence, chatgpt, embeddings; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### When should I choose weaviate-examples over embedbase? Choose weaviate-examples over embedbase when weaviate-examples is primarily HTML; embedbase is TypeScript; Tags unique to weaviate-examples: deep-learning, examples, vector-search, vector-search-engine; You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality. ### 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 weaviate-examples? Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing. You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics. ### Is embedbase or weaviate-examples more popular on GitHub? embedbase has more GitHub stars (523 vs 331). Stars measure visibility, not whether either tool fits your constraints. ### Are embedbase and weaviate-examples open source? Yes - both are open-source projects on GitHub (embedbase: MIT, weaviate-examples: MIT). ### Where can I find alternatives to embedbase or weaviate-examples? GraphCanon lists graph-backed alternatives at [embedbase alternatives](/tools/different-ai-embedbase/alternatives) and [weaviate-examples alternatives](/tools/weaviate-weaviate-examples/alternatives) ([embedbase markdown twin](/tools/different-ai-embedbase/alternatives.md), [weaviate-examples markdown twin](/tools/weaviate-weaviate-examples/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-weaviate-weaviate-examples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, embedbase or weaviate-examples? embedbase: Dormant. weaviate-examples: Dormant. 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 weaviate-examples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [embedbase trust report](/tools/different-ai-embedbase/trust); [weaviate-examples trust report](/tools/weaviate-weaviate-examples/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/_