--- title: "embedbase vs examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/different-ai-embedbase-vs-pinecone-io-examples" tools: ["different-ai-embedbase", "pinecone-io-examples"] --- # embedbase vs examples *GraphCanon updated Aug 15, 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 examples if examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. [embedbase](https://docs.embedbase.xyz) reports 524 GitHub stars, 55 forks, and 35 open issues, last pushed Nov 27, 2024. [examples](https://docs.pinecone.io) has 3.0k stars, 1.1k forks, and 61 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [embedbase's repository](https://github.com/different-ai/embedbase) and [examples's repository](https://github.com/pinecone-io/examples). | | [embedbase](/tools/different-ai-embedbase.md) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Tagline | A dead-simple API to build LLM-powered apps | Jupyter Notebooks to help you get hands-on with Pinecone vector databases | | Stars | 524 | 3,036 | | Forks | 55 | 1,073 | | Open issues | 35 | 61 | | 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. | Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. | | 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) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 601d | 0d | | Open issues (now) | 35 | 61 | | Stars delta | Unknown | +8 (30d) | | Open issues delta | Unknown | -3 (30d) | | Full report | [trust report](/tools/different-ai-embedbase/trust.md) | [trust report](/tools/pinecone-io-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: examples - **Adopt for:** Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. ## Choose when ### Choose embedbase if… - embedbase is primarily TypeScript; examples is Jupyter Notebook. - Tags unique to embedbase: artificial-intelligence, chatgpt, embeddings, machine-learning. - * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### Choose examples if… - examples is primarily Jupyter Notebook; embedbase is TypeScript. - Tags unique to examples: jupyter-notebook, llm, python, semantic-search. - When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks. ## 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 examples - Avoid if you're looking for generic tools applicable to a wide range of vector databases; this repository focuses exclusively on Pinecone. - Not ideal if you prefer starting with theoretical understanding before practical application; the provided guidance is geared toward immediate experimentation in Google Colab. ## Common questions ### What is the difference between embedbase and examples? embedbase: A dead-simple API to build LLM-powered apps. examples: Jupyter Notebooks to help you get hands-on with Pinecone vector databases. See the comparison table for live GitHub stats and shared categories. ### When should I choose embedbase over examples? Choose embedbase over examples when embedbase is primarily TypeScript; examples is Jupyter Notebook; Tags unique to embedbase: artificial-intelligence, chatgpt, embeddings, machine-learning; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### When should I choose examples over embedbase? Choose examples over embedbase when examples is primarily Jupyter Notebook; embedbase is TypeScript; Tags unique to examples: jupyter-notebook, llm, python, semantic-search; When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks. ### 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 examples? Avoid if you're looking for generic tools applicable to a wide range of vector databases; this repository focuses exclusively on Pinecone. Not ideal if you prefer starting with theoretical understanding before practical application; the provided guidance is geared toward immediate experimentation in Google Colab. ### Is embedbase or examples more popular on GitHub? examples has more GitHub stars (3,036 vs 524). Stars measure visibility, not whether either tool fits your constraints. ### Are embedbase and examples open source? Yes - both are open-source projects on GitHub (embedbase: MIT, examples: MIT). ### Where can I find alternatives to embedbase or examples? GraphCanon lists graph-backed alternatives at [embedbase alternatives](/tools/different-ai-embedbase/alternatives) and [examples alternatives](/tools/pinecone-io-examples/alternatives) ([embedbase markdown twin](/tools/different-ai-embedbase/alternatives.md), [examples markdown twin](/tools/pinecone-io-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-pinecone-io-examples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, embedbase or examples? embedbase: Dormant. examples: Very 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 examples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [embedbase trust report](/tools/different-ai-embedbase/trust); [examples trust report](/tools/pinecone-io-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/_