Comparison
embedbase vs weaviate-examples
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.
Markdown twin · embedbase alternatives · weaviate-examples alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | embedbase | weaviate-examples |
|---|---|---|
| Maintenance | Dormant (632d since push) As of 2d · github_public_v1 | Dormant (380d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2d · github_public_v1 | Not a fork · Organization account As of 1d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- embedbase
- A dead-simple API to build LLM-powered apps
- weaviate-examples
- Weaviate vector database – examples
Stars
- embedbase
- 523
- weaviate-examples
- 331
Forks
- embedbase
- 54
- weaviate-examples
- 86
Open issues
- embedbase
- 35
- weaviate-examples
- 12
Language
- embedbase
- TypeScript
- weaviate-examples
- HTML
Adopt for
- embedbase
- 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
- weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications.
Persona
- embedbase
- -
- weaviate-examples
- -
Runtime
- embedbase
- -
- weaviate-examples
- -
License
- embedbase
- MIT
- weaviate-examples
- MIT
Last pushed
- embedbase
- Nov 27, 2024
- weaviate-examples
- Aug 7, 2025
Categories
- embedbase
- Data & Retrieval, Vector Databases
- weaviate-examples
- Data & Retrieval, Vector Databases
Trust and health
Days since push
- embedbase
- 632d
- weaviate-examples
- 380d
Open issues (now)
- embedbase
- 35
- weaviate-examples
- 12
Full report
- embedbase
- Trust report
- weaviate-examples
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (different-ai/embedbase) · observed Aug 22, 2026
- GitHub forks (different-ai/embedbase) · observed Aug 22, 2026
- Last push (different-ai/embedbase) · observed Nov 27, 2024
- License file (MIT) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (weaviate/weaviate-examples) · observed Aug 23, 2026
- GitHub forks (weaviate/weaviate-examples) · observed Aug 23, 2026
- Last push (weaviate/weaviate-examples) · observed Aug 7, 2025
- License file (MIT) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: embedbase 523 · weaviate-examples 331 (synced Aug 22, 2026).
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 and weaviate-examples alternatives (embedbase markdown twin, weaviate-examples markdown twin), 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 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; weaviate-examples trust report.