--- title: "embedbase vs recipes" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/different-ai-embedbase-vs-weaviate-recipes" tools: ["different-ai-embedbase", "weaviate-recipes"] --- # embedbase vs recipes *GraphCanon updated Aug 21, 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 recipes if comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. [embedbase](https://docs.embedbase.xyz) reports 524 GitHub stars, 55 forks, and 35 open issues, last pushed Nov 27, 2024. [recipes](https://github.com/weaviate/recipes) has 944 stars, 197 forks, and 4 open issues, last pushed Aug 13, 2026. Figures are from public GitHub metadata via [embedbase's repository](https://github.com/different-ai/embedbase) and [recipes's repository](https://github.com/weaviate/recipes). | | [embedbase](/tools/different-ai-embedbase.md) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Tagline | A dead-simple API to build LLM-powered apps | End-to-end notebooks for using Weaviate features and integrations. | | Stars | 524 | 944 | | Forks | 55 | 197 | | Open issues | 35 | 4 | | Language | TypeScript | Jupyter Notebook | | 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. | Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. | | Persona | - | - | | Runtime | - | - | | License | 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) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 601d | 8d | | Open issues (now) | 35 | 4 | | Stars delta | Unknown | +3 (30d) | | Open issues delta | Unknown | -2 (30d) | | Full report | [trust report](/tools/different-ai-embedbase/trust.md) | [trust report](/tools/weaviate-recipes/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: recipes - **Adopt for:** Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. ## Choose when ### Choose embedbase if… - embedbase is primarily TypeScript; recipes is Jupyter Notebook. - 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 recipes if… - recipes is primarily Jupyter Notebook; embedbase is TypeScript. - Tags unique to recipes: function-calling, generative-ai, llm frameworks, python. - When you are specifically interested in exploring various integrations with cloud hyperscalers (Google, AWS), LLM frameworks (LangChain, LlamaIndex), and other technologies mentioned, such as Databri ## 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 recipes - If you are looking for generalized vector database use case examples that do not specifically showcase Weaviate's unique integrations or features - When your focus is on understanding and using broad category services instead of the specific, detailed examples and configurations available in the Weaviate ecosystem - For cases where a competitor tool offers better support for other specific needs, such as more comprehensive integration with data platforms not specifically covered by Weaviate like MongoDB or Redis ## Common questions ### What is the difference between embedbase and recipes? embedbase: A dead-simple API to build LLM-powered apps. recipes: End-to-end notebooks for using Weaviate features and integrations.. See the comparison table for live GitHub stats and shared categories. ### When should I choose embedbase over recipes? Choose embedbase over recipes when embedbase is primarily TypeScript; recipes is Jupyter Notebook; 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 recipes over embedbase? Choose recipes over embedbase when recipes is primarily Jupyter Notebook; embedbase is TypeScript; Tags unique to recipes: function-calling, generative-ai, llm frameworks, python; When you are specifically interested in exploring various integrations with cloud hyperscalers (Google, AWS), LLM frameworks (LangChain, LlamaIndex), and other technologies mentioned, such as Databri. ### 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 recipes? If you are looking for generalized vector database use case examples that do not specifically showcase Weaviate's unique integrations or features When your focus is on understanding and using broad category services instead of the specific, detailed examples and configurations available in the Weaviate ecosystem For cases where a competitor tool offers better support for other specific needs, such as more comprehensive integration with data platforms not specifically covered by Weaviate like MongoDB or Redis ### Is embedbase or recipes more popular on GitHub? recipes has more GitHub stars (944 vs 524). Stars measure visibility, not whether either tool fits your constraints. ### Are embedbase and recipes open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to embedbase or recipes? GraphCanon lists graph-backed alternatives at [embedbase alternatives](/tools/different-ai-embedbase/alternatives) and [recipes alternatives](/tools/weaviate-recipes/alternatives) ([embedbase markdown twin](/tools/different-ai-embedbase/alternatives.md), [recipes markdown twin](/tools/weaviate-recipes/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-recipes.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, embedbase or recipes? embedbase: Dormant. recipes: 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 embedbase and recipes? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [embedbase trust report](/tools/different-ai-embedbase/trust); [recipes trust report](/tools/weaviate-recipes/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/_