--- title: "rag_api vs autoflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/danny-avila-rag-api-vs-pingcap-autoflow" tools: ["danny-avila-rag-api", "pingcap-autoflow"] --- # rag_api vs autoflow *GraphCanon updated Aug 21, 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 autoflow if pingcap/autoflow leverages Graph RAG technology and TiDB Serverless Vector Storage, making it a specialized choice for building conversational knowledge bases in TypeScript. [rag_api](https://librechat.ai/) reports 885 GitHub stars, 387 forks, and 44 open issues, last pushed Aug 15, 2026. [autoflow](https://tidb.ai) has 3.0k stars, 194 forks, and 74 open issues, last pushed Apr 27, 2026. Figures are from public GitHub metadata via [rag_api's repository](https://github.com/danny-avila/rag_api) and [autoflow's repository](https://github.com/pingcap/autoflow). | | [rag_api](/tools/danny-avila-rag-api.md) | [autoflow](/tools/pingcap-autoflow.md) | | --- | --- | --- | | Tagline | ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector | Graph RAG based conversational knowledge base tool using TiDB Serverless Vector Storage | | Stars | 885 | 2,971 | | Forks | 387 | 194 | | Open issues | 44 | 74 | | Language | Python | TypeScript | | Adopt for | Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration | pingcap/autoflow leverages Graph RAG technology and TiDB Serverless Vector Storage, making it a specialized choice for building conversational knowledge bases in TypeScript. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | 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) | [autoflow](/tools/pingcap-autoflow.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 6d | 115d | | Open issues (now) | 44 | 74 | | Stars delta | +19 (30d) | +15 (30d) | | Open issues delta | -3 (30d) | -1 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/danny-avila-rag-api/trust.md) | [trust report](/tools/pingcap-autoflow/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: autoflow - **Adopt for:** pingcap/autoflow leverages Graph RAG technology and TiDB Serverless Vector Storage, making it a specialized choice for building conversational knowledge bases in TypeScript. ## Choose when ### Choose rag_api if… - rag_api is primarily Python; autoflow is TypeScript. - License: rag_api is MIT, autoflow is Apache-2.0. - 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. ### Choose autoflow if… - autoflow is primarily TypeScript; rag_api is Python. - License: autoflow is Apache-2.0, rag_api is MIT. - Tags unique to autoflow: chatbot, cot, graphrag, knowledge-graph. - - When you need to create a conversational interface that can leverage both graph-based data structures and retrieval-augmented generation techniques for context-aware responses. ## 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 autoflow - - When your application does not require a conversational knowledge base or cannot benefit from retrieval-augmented generation (RAG) techniques. - - If you are aiming for broad compatibility across different SQL-based databases, as autoflow specifically integrates with TiDB and might offer less flexibility when compared to tools that support a ## Common questions ### What is the difference between rag_api and autoflow? rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. autoflow: Graph RAG based conversational knowledge base tool using TiDB Serverless Vector Storage. See the comparison table for live GitHub stats and shared categories. ### When should I choose rag_api over autoflow? Choose rag_api over autoflow when rag_api is primarily Python; autoflow is TypeScript; License: rag_api is MIT, autoflow is Apache-2.0; 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. ### When should I choose autoflow over rag_api? Choose autoflow over rag_api when autoflow is primarily TypeScript; rag_api is Python; License: autoflow is Apache-2.0, rag_api is MIT; Tags unique to autoflow: chatbot, cot, graphrag, knowledge-graph; - When you need to create a conversational interface that can leverage both graph-based data structures and retrieval-augmented generation techniques for context-aware responses. ### 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 autoflow? - When your application does not require a conversational knowledge base or cannot benefit from retrieval-augmented generation (RAG) techniques. - If you are aiming for broad compatibility across different SQL-based databases, as autoflow specifically integrates with TiDB and might offer less flexibility when compared to tools that support a ### Is rag_api or autoflow more popular on GitHub? autoflow has more GitHub stars (2,971 vs 885). Stars measure visibility, not whether either tool fits your constraints. ### Are rag_api and autoflow open source? Yes - both are open-source projects on GitHub (rag_api: MIT, autoflow: Apache-2.0). ### Where can I find alternatives to rag_api or autoflow? GraphCanon lists graph-backed alternatives at [rag_api alternatives](/tools/danny-avila-rag-api/alternatives) and [autoflow alternatives](/tools/pingcap-autoflow/alternatives) ([rag_api markdown twin](/tools/danny-avila-rag-api/alternatives.md), [autoflow markdown twin](/tools/pingcap-autoflow/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-pingcap-autoflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rag_api or autoflow? rag_api: Very active. autoflow: Slowing. 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 autoflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rag_api trust report](/tools/danny-avila-rag-api/trust); [autoflow trust report](/tools/pingcap-autoflow/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/_