Comparison
rag_api vs embedbase
Verdict
Pick rag_api if key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration; 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.
Markdown twin · rag_api alternatives · embedbase alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | rag_api | embedbase |
|---|---|---|
| Maintenance | Very active (6d since push) As of 3d · github_public_v1 | Dormant (632d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3d · github_public_v1 | Not a fork · Organization account As of 3d · 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
- rag_api
- ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector
- embedbase
- A dead-simple API to build LLM-powered apps
Stars
- rag_api
- 885
- embedbase
- 523
Forks
- rag_api
- 387
- embedbase
- 54
Open issues
- rag_api
- 44
- embedbase
- 35
Language
- rag_api
- Python
- embedbase
- TypeScript
Adopt for
- rag_api
- Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration
- 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.
Persona
- rag_api
- -
- embedbase
- -
Runtime
- rag_api
- -
- embedbase
- -
License
- rag_api
- MIT
- embedbase
- MIT
Last pushed
- rag_api
- Aug 15, 2026
- embedbase
- Nov 27, 2024
Categories
- rag_api
- Data & Retrieval, Vector Databases
- embedbase
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- rag_api
- Very active (96%)
- embedbase
- Dormant (18%)
Days since push
- rag_api
- 6d
- embedbase
- 632d
Open issues (now)
- rag_api
- 44
- embedbase
- 35
Stars delta
- rag_api
- +19 (30d)
- embedbase
- -1 (30d)
Open issues delta
- rag_api
- -3 (30d)
- embedbase
- 0 (30d)
Owner type
- rag_api
- User
- embedbase
- Organization
Full report
- rag_api
- Trust report
- embedbase
- Trust report
Choose rag_api if…
- rag_api is primarily Python; embedbase is TypeScript.
- Tags unique to rag_api: api, api-rest, fastapi, langchain.
- rag_api ships Docker support for self-hosted deployment.
- When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage.
When NOT to use rag_api
- Avoid using if your project cannot leverage PostgreSQL/pgvector due to license or compatibility constraints.
- Not recommended for scenarios where high-level orchestration of multiple APIs and services is necessary without a direct need for FastAPI's simplicity.
Choose embedbase if…
- embedbase is primarily TypeScript; rag_api is Python.
- Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning.
- * 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (danny-avila/rag_api) · observed Aug 21, 2026
- GitHub forks (danny-avila/rag_api) · observed Aug 21, 2026
- Last push (danny-avila/rag_api) · observed Aug 15, 2026
- License file (MIT) · observed Aug 21, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: rag_api 885 · embedbase 523 (synced Aug 21, 2026).
Common questions
- What is the difference between rag_api and embedbase?
- rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. embedbase: A dead-simple API to build LLM-powered apps. See the comparison table for live GitHub stats and shared categories.
- When should I choose rag_api over embedbase?
- Choose rag_api over embedbase when rag_api is primarily Python; embedbase is TypeScript; Tags unique to rag_api: api, api-rest, fastapi, langchain; rag_api ships Docker support for self-hosted deployment; When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage.
- When should I choose embedbase over rag_api?
- Choose embedbase over rag_api when embedbase is primarily TypeScript; rag_api is Python; Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase.
- When should I avoid rag_api?
- Avoid using if your project cannot leverage PostgreSQL/pgvector due to license or compatibility constraints. Not recommended for scenarios where high-level orchestration of multiple APIs and services is necessary without a direct need for FastAPI's simplicity.
- 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.
- Is rag_api or embedbase more popular on GitHub?
- rag_api has more GitHub stars (885 vs 523). Stars measure visibility, not whether either tool fits your constraints.
- Are rag_api and embedbase open source?
- Yes - both are open-source projects on GitHub (rag_api: MIT, embedbase: MIT).
- Where can I find alternatives to rag_api or embedbase?
- GraphCanon lists graph-backed alternatives at rag_api alternatives and embedbase alternatives (rag_api markdown twin, embedbase 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, rag_api or embedbase?
- rag_api: Very active. embedbase: 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 rag_api and embedbase?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: rag_api trust report; embedbase trust report.