--- title: "rag_api vs VectorDBBench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/danny-avila-rag-api-vs-zilliztech-vectordbbench" tools: ["danny-avila-rag-api", "zilliztech-vectordbbench"] --- # rag_api vs VectorDBBench *GraphCanon updated Sep 20, 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 VectorDBBench if vectorDBBench is a benchmark tool for evaluating vector databases written in Python under the MIT license. [rag_api](https://librechat.ai/) reports 901 GitHub stars, 404 forks, and 45 open issues, last pushed Aug 15, 2026. [VectorDBBench](https://zilliz.com/vector-database-benchmark-tool) has 1.2k stars, 439 forks, and 184 open issues, last pushed Sep 11, 2026. Figures are from public GitHub metadata via [rag_api's repository](https://github.com/danny-avila/rag_api) and [VectorDBBench's repository](https://github.com/zilliztech/VectorDBBench). | | [rag_api](/tools/danny-avila-rag-api.md) | [VectorDBBench](/tools/zilliztech-vectordbbench.md) | | --- | --- | --- | | Tagline | ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector | Benchmark for vector databases | | Stars | 901 | 1,178 | | Forks | 404 | 439 | | Open issues | 45 | 184 | | Language | Python | Python | | Adopt for | Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration | VectorDBBench is a benchmark tool for evaluating vector databases written in Python under the MIT license. | | 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._ | | [rag_api](/tools/danny-avila-rag-api.md) | [VectorDBBench](/tools/zilliztech-vectordbbench.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 35d | 8d | | Open issues (now) | 45 | 184 | | Stars delta | +35 (30d) | +31 (30d) | | Open issues delta | -2 (30d) | +30 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/danny-avila-rag-api/trust.md) | [trust report](/tools/zilliztech-vectordbbench/trust.md) | ## Shared compatibility - **Python**: [rag_api](/tools/danny-avila-rag-api.md) - Python runtime; [VectorDBBench](/tools/zilliztech-vectordbbench.md) - Python runtime ## 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: VectorDBBench - **Adopt for:** VectorDBBench is a benchmark tool for evaluating vector databases written in Python under the MIT license. ## Choose when ### Choose rag_api if… - Tags unique to rag_api: api, api-rest, embeddings, fastapi. - When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage. - Leaner open-issue backlog (45). ### Choose VectorDBBench if… - Tags unique to VectorDBBench: benchmark, cost-effectiveness, performance, vector-database. - When you need a comprehensive performance analysis of vector database solutions using Python - More GitHub stars (1.2k vs 901) - visibility, not fit. ## 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 VectorDBBench - If your evaluation framework requires languages other than Python or licenses other than MIT - When benchmarking non-vector type databases, as VectorDBBench is specialized for vector databases ## Common questions ### What is the difference between rag_api and VectorDBBench? rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. VectorDBBench: Benchmark for vector databases. See the comparison table for live GitHub stats and shared categories. ### When should I choose rag_api over VectorDBBench? Choose rag_api over VectorDBBench when Tags unique to rag_api: api, api-rest, embeddings, fastapi; When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage; Leaner open-issue backlog (45). ### When should I choose VectorDBBench over rag_api? Choose VectorDBBench over rag_api when Tags unique to VectorDBBench: benchmark, cost-effectiveness, performance, vector-database; When you need a comprehensive performance analysis of vector database solutions using Python; More GitHub stars (1.2k vs 901) - visibility, not fit. ### 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 VectorDBBench? If your evaluation framework requires languages other than Python or licenses other than MIT When benchmarking non-vector type databases, as VectorDBBench is specialized for vector databases ### Is rag_api or VectorDBBench more popular on GitHub? VectorDBBench has more GitHub stars (1,178 vs 901). Stars measure visibility, not whether either tool fits your constraints. ### Are rag_api and VectorDBBench open source? Yes - both are open-source projects on GitHub (rag_api: MIT, VectorDBBench: MIT). ### Where can I find alternatives to rag_api or VectorDBBench? GraphCanon lists graph-backed alternatives at [rag_api alternatives](/tools/danny-avila-rag-api/alternatives) and [VectorDBBench alternatives](/tools/zilliztech-vectordbbench/alternatives) ([rag_api markdown twin](/tools/danny-avila-rag-api/alternatives.md), [VectorDBBench markdown twin](/tools/zilliztech-vectordbbench/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-zilliztech-vectordbbench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rag_api or VectorDBBench? rag_api: Steady. VectorDBBench: 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 VectorDBBench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rag_api trust report](/tools/danny-avila-rag-api/trust); [VectorDBBench trust report](/tools/zilliztech-vectordbbench/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/_