--- title: "rag_api vs qdrant" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/danny-avila-rag-api-vs-qdrant-qdrant" tools: ["danny-avila-rag-api", "qdrant-qdrant"] --- # rag_api vs qdrant *GraphCanon updated Jul 11, 2026* ## 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 qdrant if high-performance vector database with support for distributed deployment. [rag_api](https://librechat.ai/) reports 863 GitHub stars, 376 forks, and 44 open issues, last pushed Jun 18, 2026. [qdrant](https://qdrant.tech) has 33k stars, 2.5k forks, and 631 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [rag_api's repository](https://github.com/danny-avila/rag_api) and [qdrant's repository](https://github.com/qdrant/qdrant). | | [rag_api](/tools/danny-avila-rag-api.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Tagline | ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector | High-performance, massive-scale Vector Database and Vector Search Engine | | Stars | 863 | 33,143 | | Forks | 376 | 2,483 | | Open issues | 44 | 631 | | Language | Python | Rust | | Adopt for | Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration | High-performance vector database with support for distributed deployment. | | Persona | - | - | | Runtime | - | - | | License | MIT | Qdrant is available under the Apache License 2.0. | | Categories | Vector Databases, Data & Retrieval | Vector Databases, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rag_api](/tools/danny-avila-rag-api.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 22d | 0d | | Open issues (now) | 44 | 631 | | Owner type | User | Organization | | Full report | [trust report](/tools/danny-avila-rag-api/trust.md) | [trust report](/tools/qdrant-qdrant/trust.md) | ## Decision facts: rag_api - **Adopt for:** Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration ## Decision facts: qdrant - **Hosting:** self hosted - Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/. - **Requirements:** - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections. - **Adopt for:** High-performance vector database with support for distributed deployment. - **License detail:** Qdrant is available under the Apache License 2.0. ## Choose when ### Choose rag_api if… - rag_api is primarily Python; qdrant is Rust. - License: rag_api is MIT, qdrant is Apache-2.0. - Tags unique to rag_api: postgresql, psql, embeddings, fastapi. - 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. ### Choose qdrant if… - qdrant is primarily Rust; rag_api is Python. - License: qdrant is Apache-2.0, rag_api is MIT. - Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/. - Requirements: - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections.. - Tags unique to qdrant: knn-algorithm, vector-search-engine, vector-database, embeddings-similarity. - - When scalability and performance are paramount in handling large-scale embeddings. ## 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. ## When NOT to use qdrant - - Avoid if your project requires more traditional relational database features as Qdrant focuses exclusively on vectors. - - If minimalistic setup is crucial, since Qdrant's capability for distributed deployment may introduce complexity that is not necessary for smaller-scale applications. - - For use cases where non-Rust environments significantly limit the feasibility of integrating external tools. ## Common questions ### What is the difference between rag_api and qdrant? rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. qdrant: High-performance, massive-scale Vector Database and Vector Search Engine. See the comparison table for live GitHub stats and shared categories. ### When should I choose rag_api over qdrant? Choose rag_api over qdrant when rag_api is primarily Python; qdrant is Rust; License: rag_api is MIT, qdrant is Apache-2.0; Tags unique to rag_api: postgresql, psql, embeddings, fastapi; 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 qdrant over rag_api? Choose qdrant over rag_api when qdrant is primarily Rust; rag_api is Python; License: qdrant is Apache-2.0, rag_api is MIT; Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/; Requirements: - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections.; Tags unique to qdrant: knn-algorithm, vector-search-engine, vector-database, embeddings-similarity; - When scalability and performance are paramount in handling large-scale embeddings. ### 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 qdrant? - Avoid if your project requires more traditional relational database features as Qdrant focuses exclusively on vectors. - If minimalistic setup is crucial, since Qdrant's capability for distributed deployment may introduce complexity that is not necessary for smaller-scale applications. - For use cases where non-Rust environments significantly limit the feasibility of integrating external tools. ### Is rag_api or qdrant more popular on GitHub? qdrant has more GitHub stars (33,143 vs 863). Stars measure visibility, not whether either tool fits your constraints. ### Are rag_api and qdrant open source? Yes - both are open-source projects on GitHub (rag_api: MIT, qdrant: Apache-2.0). ### Where can I find alternatives to rag_api or qdrant? GraphCanon lists graph-backed alternatives at [rag_api alternatives](/tools/danny-avila-rag-api/alternatives) and [qdrant alternatives](/tools/qdrant-qdrant/alternatives) ([rag_api markdown twin](/tools/danny-avila-rag-api/alternatives.md), [qdrant markdown twin](/tools/qdrant-qdrant/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/danny-avila-rag-api-vs-qdrant-qdrant.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rag_api or qdrant? rag_api: Active. qdrant: 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 rag_api and qdrant? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rag_api trust report](/tools/danny-avila-rag-api/trust); [qdrant trust report](/tools/qdrant-qdrant/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=danny-avila-rag-api`](/api/graphcanon/graph?tool=danny-avila-rag-api) - 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/_