--- title: "rag_api vs txtai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/danny-avila-rag-api-vs-neuml-txtai" tools: ["danny-avila-rag-api", "neuml-txtai"] --- # rag_api vs txtai *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 txtai if txtai is an all-in-one AI framework that supports semantic search, LLM orchestration, and language model workflows, making it suitable for projects that require comprehensive AI capabilities in Python. [rag_api](https://librechat.ai/) reports 901 GitHub stars, 404 forks, and 45 open issues, last pushed Aug 15, 2026. [txtai](https://neuml.github.io/txtai) has 13k stars, 891 forks, and 10 open issues, last pushed Sep 15, 2026. Figures are from public GitHub metadata via [rag_api's repository](https://github.com/danny-avila/rag_api) and [txtai's repository](https://github.com/neuml/txtai). | | [rag_api](/tools/danny-avila-rag-api.md) | [txtai](/tools/neuml-txtai.md) | | --- | --- | --- | | Tagline | ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector | All-in-one AI framework for semantic search, LLM orchestration and language model workflows | | Stars | 901 | 12,959 | | Forks | 404 | 891 | | Open issues | 45 | 10 | | Language | Python | Python | | Adopt for | Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration | txtai is an all-in-one AI framework that supports semantic search, LLM orchestration, and language model workflows, making it suitable for projects that require comprehensive AI capabilities in Python. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Data & Retrieval, Vector Databases | AI Agents, Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rag_api](/tools/danny-avila-rag-api.md) | [txtai](/tools/neuml-txtai.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 35d | 2d | | Open issues (now) | 45 | 10 | | Stars delta | +35 (30d) | +69 (30d) | | Open issues delta | -2 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/danny-avila-rag-api/trust.md) | [trust report](/tools/neuml-txtai/trust.md) | ## Shared compatibility - **Python**: [rag_api](/tools/danny-avila-rag-api.md) - Python runtime; [txtai](/tools/neuml-txtai.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: txtai - **Adopt for:** txtai is an all-in-one AI framework that supports semantic search, LLM orchestration, and language model workflows, making it suitable for projects that require comprehensive AI capabilities in Python. ## Choose when ### Choose rag_api if… - License: rag_api is MIT, txtai is Apache-2.0. - Tags unique to rag_api: api, api-rest, fastapi, langchain. - 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 txtai if… - License: txtai is Apache-2.0, rag_api is MIT. - Tags unique to txtai: agents, ai, ai-agents, information retrieval. - Also covers AI Agents, Evaluation & Observability, Inference & Serving, Model Training. - When you need a comprehensive framework that integrates semantic search, LLM orchestration, and language model workflows in a single package. ## 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 txtai - If your project strictly requires a framework that is not Python-based, as txtai is specifically designed for Python environments. - When you need a tool that focuses solely on a specific aspect of AI, such as only semantic search or only LLM orchestration, as txtai's all-in-one approach might introduce unnecessary complexity. - If your project cannot accommodate the Apache-2.0 license, as txtai is distributed under this license and may not be suitable for projects with different licensing requirements. ## Common questions ### What is the difference between rag_api and txtai? rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. txtai: All-in-one AI framework for semantic search, LLM orchestration and language model workflows. See the comparison table for live GitHub stats and shared categories. ### When should I choose rag_api over txtai? Choose rag_api over txtai when License: rag_api is MIT, txtai is Apache-2.0; Tags unique to rag_api: api, api-rest, fastapi, langchain; 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 txtai over rag_api? Choose txtai over rag_api when License: txtai is Apache-2.0, rag_api is MIT; Tags unique to txtai: agents, ai, ai-agents, information retrieval; Also covers AI Agents, Evaluation & Observability, Inference & Serving, Model Training; When you need a comprehensive framework that integrates semantic search, LLM orchestration, and language model workflows in a single package. ### 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 txtai? If your project strictly requires a framework that is not Python-based, as txtai is specifically designed for Python environments. When you need a tool that focuses solely on a specific aspect of AI, such as only semantic search or only LLM orchestration, as txtai's all-in-one approach might introduce unnecessary complexity. If your project cannot accommodate the Apache-2.0 license, as txtai is distributed under this license and may not be suitable for projects with different licensing requirements. ### Is rag_api or txtai more popular on GitHub? txtai has more GitHub stars (12,959 vs 901). Stars measure visibility, not whether either tool fits your constraints. ### Are rag_api and txtai open source? Yes - both are open-source projects on GitHub (rag_api: MIT, txtai: Apache-2.0). ### Where can I find alternatives to rag_api or txtai? GraphCanon lists graph-backed alternatives at [rag_api alternatives](/tools/danny-avila-rag-api/alternatives) and [txtai alternatives](/tools/neuml-txtai/alternatives) ([rag_api markdown twin](/tools/danny-avila-rag-api/alternatives.md), [txtai markdown twin](/tools/neuml-txtai/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-neuml-txtai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rag_api or txtai? rag_api: Steady. txtai: 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 txtai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rag_api trust report](/tools/danny-avila-rag-api/trust); [txtai trust report](/tools/neuml-txtai/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/_