Comparison
embedbase vs recipes
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.
Markdown twin · embedbase alternatives · recipes alternatives
GraphCanon updated Sep 20, 2026
8views this month
vs
Trust & integrity
| Signal | embedbase | recipes |
|---|---|---|
| Maintenance | Dormant (661d since push) As of Sep 20, 2026 · github_public_v1 | Very active (3d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- recipes
- End-to-end notebooks for using Weaviate features and integrations.
Stars
- embedbase
- 523
- recipes
- 947
Forks
- embedbase
- 53
- recipes
- 198
Open issues
- embedbase
- 35
- recipes
- 4
Language
- embedbase
- TypeScript
- recipes
- Jupyter Notebook
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.
- recipes
- Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases.
Persona
- embedbase
- -
- recipes
- -
Runtime
- embedbase
- -
- recipes
- -
License
- embedbase
- MIT
- recipes
- -
Last pushed
- embedbase
- Nov 27, 2024
- recipes
- Sep 16, 2026
Categories
- embedbase
- Data & Retrieval, Vector Databases
- recipes
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- embedbase
- Dormant (18%)
- recipes
- Very active (96%)
Days since push
- embedbase
- 661d
- recipes
- 3d
Open issues (now)
- embedbase
- 35
- recipes
- 4
Stars delta
- embedbase
- -1 (30d)
- recipes
- +6 (30d)
Open issues delta
- embedbase
- 0 (30d)
- recipes
- -2 (30d)
Full report
- embedbase
- Trust report
- recipes
- Trust report
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.
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 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 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
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 Sep 20, 2026
- GitHub forks (different-ai/embedbase) · observed Sep 20, 2026
- Last push (different-ai/embedbase) · observed Nov 27, 2024
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (weaviate/recipes) · observed Sep 20, 2026
- GitHub forks (weaviate/recipes) · observed Sep 20, 2026
- Last push (weaviate/recipes) · observed Sep 16, 2026
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: embedbase 523 · recipes 947 (synced Sep 20, 2026).
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 (947 vs 523). 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 and recipes alternatives (embedbase markdown twin, recipes 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 recipes?
- embedbase: Dormant. recipes: 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 recipes?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: embedbase trust report; recipes trust report.