--- title: "txtai vs quivr" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/neuml-txtai-vs-quivrhq-quivr" tools: ["neuml-txtai", "quivrhq-quivr"] --- # txtai vs quivr Neutral, constraint-first comparison with live GitHub stats. | | [txtai](/tools/neuml-txtai.md) | [quivr](/tools/quivrhq-quivr.md) | | --- | --- | --- | | Tagline | All-in-one AI framework for semantic search, LLM orchestration and language model workflows | Opiniated RAG for integrating GenAI in your apps | | Stars | 12,712 | 39,190 | | Forks | 841 | 3,719 | | Open issues | 9 | 29 | | Language | Python | Python | | Adopt for | txtai is a comprehensive AI toolkit tailored for semantic search and language model management, offering seamless integration of diverse functionalities into robust workflows. | Quivr is an opinionated RAG framework for integrating Generative AI into apps, emphasizing customizability and compatibility with multiple LLMs and vectorstores. It allows for quick setup and customization to meet varied | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | AI Agents, Data & Retrieval, LLM Frameworks, Model Training, Vector Databases, Inference & Serving, Developer Tools | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [txtai](/tools/neuml-txtai.md) | [quivr](/tools/quivrhq-quivr.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 6d | 363d | | Open issues (now) | 9 | 29 | | Full report | [trust report](/tools/neuml-txtai/trust.md) | [trust report](/tools/quivrhq-quivr/trust.md) | **Typed relationship:** txtai _(alternative)_ quivr Quivr and txtai both offer RAG solutions for semantic search and LLM orchestration, catering to the need of integrating AI efficiently into applications. ## Shared compatibility - **Python**: [txtai](/tools/neuml-txtai.md) - Python runtime; [quivr](/tools/quivrhq-quivr.md) - Python runtime ## Decision facts: txtai - **Adopt for:** txtai is a comprehensive AI toolkit tailored for semantic search and language model management, offering seamless integration of diverse functionalities into robust workflows. ## Decision facts: quivr - **Adopt for:** Quivr is an opinionated RAG framework for integrating Generative AI into apps, emphasizing customizability and compatibility with multiple LLMs and vectorstores. It allows for quick setup and customization to meet varied ## Choose when ### Choose txtai if… - License: txtai is Apache-2.0, quivr is Other. - Quivr and txtai both offer RAG solutions for semantic search and LLM orchestration, catering to the need of integrating AI efficiently into applications. - Tags unique to txtai: multimodal-indexing, embeddings-database, llm-orchestration, semantic-search. - Also covers AI Agents, Model Training, Vector Databases, Inference & Serving, Developer Tools. - - When building applications that require advanced semantic understanding from large language models (LLMs) and need flexible pipelines for prompts like Q&A or summarization tasks. ### Choose quivr if… - License: quivr is Other, txtai is Apache-2.0. - Quivr and txtai both offer RAG solutions for semantic search and LLM orchestration, catering to the need of integrating AI efficiently into applications. - Tags unique to quivr: llm, ai, rag, vector. - You need a customizable RAG solution that supports multiple types of files and can integrate easily with different LLMs. ## When NOT to use txtai - - If your project only needs a basic information retrieval system without requiring sophisticated semantic analysis features provided by LLMs. - - In cases where you prefer using simpler tools for specific tasks rather than an all-in-one solution that integrates diverse AI functionalities. ## When NOT to use quivr - If your application strictly demands a non-opinionated approach to RAG where every detail must be manually configured from scratch. - When you require proprietary or highly restricted licensing terms, as Quivr has a 'Other' license that may not align with these needs. - Your project is limited to only specific LLMs not compatible with Quivr's broad support, such as certain bespoke models not covered by its wide umbrella. ## Common questions ### What is the difference between txtai and quivr? txtai: All-in-one AI framework for semantic search, LLM orchestration and language model workflows. quivr: Opiniated RAG for integrating GenAI in your apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose txtai over quivr? Choose txtai over quivr when License: txtai is Apache-2.0, quivr is Other; Quivr and txtai both offer RAG solutions for semantic search and LLM orchestration, catering to the need of integrating AI efficiently into applications; Tags unique to txtai: multimodal-indexing, embeddings-database, llm-orchestration, semantic-search; Also covers AI Agents, Model Training, Vector Databases, Inference & Serving, Developer Tools; - When building applications that require advanced semantic understanding from large language models (LLMs) and need flexible pipelines for prompts like Q&A or summarization tasks. ### When should I choose quivr over txtai? Choose quivr over txtai when License: quivr is Other, txtai is Apache-2.0; Quivr and txtai both offer RAG solutions for semantic search and LLM orchestration, catering to the need of integrating AI efficiently into applications; Tags unique to quivr: llm, ai, rag, vector; You need a customizable RAG solution that supports multiple types of files and can integrate easily with different LLMs. ### When should I avoid txtai? - If your project only needs a basic information retrieval system without requiring sophisticated semantic analysis features provided by LLMs. - In cases where you prefer using simpler tools for specific tasks rather than an all-in-one solution that integrates diverse AI functionalities. ### When should I avoid quivr? If your application strictly demands a non-opinionated approach to RAG where every detail must be manually configured from scratch. When you require proprietary or highly restricted licensing terms, as Quivr has a 'Other' license that may not align with these needs. Your project is limited to only specific LLMs not compatible with Quivr's broad support, such as certain bespoke models not covered by its wide umbrella. ### Is txtai or quivr more popular on GitHub? quivr has more GitHub stars (39,190 vs 12,712). Stars measure visibility, not whether either tool fits your constraints. ### Are txtai and quivr open source? Yes - both are open-source projects on GitHub (txtai: Apache-2.0, quivr: Other). ### Where can I find alternatives to txtai or quivr? GraphCanon lists graph-backed alternatives at /tools/neuml-txtai/alternatives and /tools/quivrhq-quivr/alternatives (/tools/neuml-txtai/alternatives.md, /tools/quivrhq-quivr/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 /compare/neuml-txtai-vs-quivrhq-quivr.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, txtai or quivr? txtai: Very active. quivr: 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 txtai and quivr? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: txtai: /tools/neuml-txtai/trust; quivr: /tools/quivrhq-quivr/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=neuml-txtai`](/api/graphcanon/graph?tool=neuml-txtai) - 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/_