--- title: "rag_api vs recipes" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/danny-avila-rag-api-vs-weaviate-recipes" tools: ["danny-avila-rag-api", "weaviate-recipes"] --- # rag_api vs recipes *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 recipes if comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. [rag_api](https://librechat.ai/) reports 901 GitHub stars, 404 forks, and 45 open issues, last pushed Aug 15, 2026. [recipes](https://github.com/weaviate/recipes) has 947 stars, 198 forks, and 4 open issues, last pushed Sep 16, 2026. Figures are from public GitHub metadata via [rag_api's repository](https://github.com/danny-avila/rag_api) and [recipes's repository](https://github.com/weaviate/recipes). | | [rag_api](/tools/danny-avila-rag-api.md) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Tagline | ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector | End-to-end notebooks for using Weaviate features and integrations. | | Stars | 901 | 947 | | Forks | 404 | 198 | | Open issues | 45 | 4 | | Language | Python | Jupyter Notebook | | Adopt for | Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration | Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. | | Persona | - | - | | Runtime | - | - | | License | 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) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 35d | 3d | | Open issues (now) | 45 | 4 | | Stars delta | +35 (30d) | +6 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/danny-avila-rag-api/trust.md) | [trust report](/tools/weaviate-recipes/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: recipes - **Adopt for:** Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. ## Choose when ### Choose rag_api if… - rag_api is primarily Python; recipes is Jupyter Notebook. - 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 recipes if… - recipes is primarily Jupyter Notebook; rag_api is Python. - 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 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 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 ## Common questions ### What is the difference between rag_api and recipes? rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. 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 rag_api over recipes? Choose rag_api over recipes when rag_api is primarily Python; recipes is Jupyter Notebook; 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 recipes over rag_api? Choose recipes over rag_api when recipes is primarily Jupyter Notebook; rag_api is Python; 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 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 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 rag_api or recipes more popular on GitHub? recipes has more GitHub stars (947 vs 901). Stars measure visibility, not whether either tool fits your constraints. ### Are rag_api and recipes open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to rag_api or recipes? GraphCanon lists graph-backed alternatives at [rag_api alternatives](/tools/danny-avila-rag-api/alternatives) and [recipes alternatives](/tools/weaviate-recipes/alternatives) ([rag_api markdown twin](/tools/danny-avila-rag-api/alternatives.md), [recipes markdown twin](/tools/weaviate-recipes/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-weaviate-recipes.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rag_api or recipes? rag_api: Steady. 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 rag_api and recipes? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rag_api trust report](/tools/danny-avila-rag-api/trust); [recipes trust report](/tools/weaviate-recipes/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/_