--- title: "rag_api vs vault-ai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/danny-avila-rag-api-vs-pashpashpash-vault-ai" tools: ["danny-avila-rag-api", "pashpashpash-vault-ai"] --- # rag_api vs vault-ai *GraphCanon updated Aug 23, 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 vault-ai if vault-ai is a tool that gives long-term memory capabilities to ChatGPT by integrating Pinecone Vector Database with an easy-to-use React frontend for uploading various types of files into the system. [rag_api](https://librechat.ai/) reports 885 GitHub stars, 387 forks, and 44 open issues, last pushed Aug 15, 2026. [vault-ai](https://vault.pash.city) has 3.4k stars, 296 forks, and 50 open issues, last pushed Jul 8, 2025. Figures are from public GitHub metadata via [rag_api's repository](https://github.com/danny-avila/rag_api) and [vault-ai's repository](https://github.com/pashpashpash/vault-ai). | | [rag_api](/tools/danny-avila-rag-api.md) | [vault-ai](/tools/pashpashpash-vault-ai.md) | | --- | --- | --- | | Tagline | ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector | Enhances ChatGPT with long-term memory using Pinecone Vector Database and React frontend for custom knowledge base uploads. | | Stars | 885 | 3,387 | | Forks | 387 | 296 | | Open issues | 44 | 50 | | Language | Python | JavaScript | | Adopt for | Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration | vault-ai is a tool that gives long-term memory capabilities to ChatGPT by integrating Pinecone Vector Database with an easy-to-use React frontend for uploading various types of files into the system. | | 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) | [vault-ai](/tools/pashpashpash-vault-ai.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 6d | 410d | | Open issues (now) | 44 | 50 | | Stars delta | +19 (30d) | 0 (30d) | | Open issues delta | -3 (30d) | 0 (30d) | | Full report | [trust report](/tools/danny-avila-rag-api/trust.md) | [trust report](/tools/pashpashpash-vault-ai/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: vault-ai - **Adopt for:** vault-ai is a tool that gives long-term memory capabilities to ChatGPT by integrating Pinecone Vector Database with an easy-to-use React frontend for uploading various types of files into the system. ## Choose when ### Choose rag_api if… - rag_api is primarily Python; vault-ai is JavaScript. - Tags unique to rag_api: api, api-rest, 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 vault-ai if… - vault-ai is primarily JavaScript; rag_api is Python. - Tags unique to vault-ai: ai, artificial-intelligence, chatgpt, generative. - When you need a custom knowledge base that can be queried using a generative AI model, such as extending ChatGPT with historical context from uploaded documents in formats like PDFs or txt. ## 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 vault-ai - When your requirements do not include uploading custom content for the AI to learn from, as vault-ai focuses on integrating a knowledge base with ChatGPT. - If you prefer using other vector search databases such as Qdrant instead of Pinecone, as vault-ai is specifically designed around Pinecone. ## Common questions ### What is the difference between rag_api and vault-ai? rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. vault-ai: Enhances ChatGPT with long-term memory using Pinecone Vector Database and React frontend for custom knowledge base uploads.. See the comparison table for live GitHub stats and shared categories. ### When should I choose rag_api over vault-ai? Choose rag_api over vault-ai when rag_api is primarily Python; vault-ai is JavaScript; Tags unique to rag_api: api, api-rest, 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 vault-ai over rag_api? Choose vault-ai over rag_api when vault-ai is primarily JavaScript; rag_api is Python; Tags unique to vault-ai: ai, artificial-intelligence, chatgpt, generative; When you need a custom knowledge base that can be queried using a generative AI model, such as extending ChatGPT with historical context from uploaded documents in formats like PDFs or txt. ### 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 vault-ai? When your requirements do not include uploading custom content for the AI to learn from, as vault-ai focuses on integrating a knowledge base with ChatGPT. If you prefer using other vector search databases such as Qdrant instead of Pinecone, as vault-ai is specifically designed around Pinecone. ### Is rag_api or vault-ai more popular on GitHub? vault-ai has more GitHub stars (3,387 vs 885). Stars measure visibility, not whether either tool fits your constraints. ### Are rag_api and vault-ai open source? Yes - both are open-source projects on GitHub (rag_api: MIT, vault-ai: MIT). ### Where can I find alternatives to rag_api or vault-ai? GraphCanon lists graph-backed alternatives at [rag_api alternatives](/tools/danny-avila-rag-api/alternatives) and [vault-ai alternatives](/tools/pashpashpash-vault-ai/alternatives) ([rag_api markdown twin](/tools/danny-avila-rag-api/alternatives.md), [vault-ai markdown twin](/tools/pashpashpash-vault-ai/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-pashpashpash-vault-ai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rag_api or vault-ai? rag_api: Very active. vault-ai: 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 vault-ai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rag_api trust report](/tools/danny-avila-rag-api/trust); [vault-ai trust report](/tools/pashpashpash-vault-ai/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/_