--- title: "txtai vs EnterpriseRAG-Bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/neuml-txtai-vs-onyx-dot-app-enterpriserag-bench" tools: ["neuml-txtai", "onyx-dot-app-enterpriserag-bench"] --- # txtai vs EnterpriseRAG-Bench *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick EnterpriseRAG-Bench if enterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. [txtai](https://neuml.github.io/txtai) reports 13k GitHub stars, 891 forks, and 10 open issues, last pushed Sep 15, 2026. [EnterpriseRAG-Bench](https://www.onyx.app/) has 562 stars, 62 forks, and 13 open issues, last pushed Sep 3, 2026. Figures are from public GitHub metadata via [txtai's repository](https://github.com/neuml/txtai) and [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench). | | [txtai](/tools/neuml-txtai.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Tagline | All-in-one AI framework for semantic search, LLM orchestration and language model workflows | Dataset and benchmark for RAG on company internal documents | | Stars | 12,959 | 562 | | Forks | 891 | 62 | | Open issues | 10 | 13 | | Language | Python | - | | 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. | EnterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT license allows free usage and modification with attribution. | | Categories | AI Agents, Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training, Vector Databases | Data & Retrieval, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [txtai](/tools/neuml-txtai.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 2d | 16d | | Open issues (now) | 10 | 13 | | Stars delta | +69 (30d) | +73 (30d) | | Open issues delta | 0 (30d) | +4 (30d) | | Full report | [trust report](/tools/neuml-txtai/trust.md) | [trust report](/tools/onyx-dot-app-enterpriserag-bench/trust.md) | ## 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. ## Decision facts: EnterpriseRAG-Bench - **Adopt for:** EnterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. - **License detail:** MIT license allows free usage and modification with attribution. ## Choose when ### Choose txtai if… - License: txtai is Apache-2.0, EnterpriseRAG-Bench is MIT. - Tags unique to txtai: agents, ai, ai-agents, embeddings. - Also covers AI Agents, Inference & Serving, Model Training, Vector Databases. - When you need a comprehensive framework that integrates semantic search, LLM orchestration, and language model workflows in a single package. ### Choose EnterpriseRAG-Bench if… - License: EnterpriseRAG-Bench is MIT, txtai is Apache-2.0. - Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation. - When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation ## 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. ## When NOT to use EnterpriseRAG-Bench - Avoid if your focus is on general web or public-domain document benchmarking, as EnterpriseRAG-Bench is tuned exclusively for company internal documents - Do not use if you require a solution that supports languages other than those implied by the existing dataset without further customization ## Common questions ### What is the difference between txtai and EnterpriseRAG-Bench? txtai: All-in-one AI framework for semantic search, LLM orchestration and language model workflows. EnterpriseRAG-Bench: Dataset and benchmark for RAG on company internal documents. See the comparison table for live GitHub stats and shared categories. ### When should I choose txtai over EnterpriseRAG-Bench? Choose txtai over EnterpriseRAG-Bench when License: txtai is Apache-2.0, EnterpriseRAG-Bench is MIT; Tags unique to txtai: agents, ai, ai-agents, embeddings; Also covers AI Agents, Inference & Serving, Model Training, Vector Databases; When you need a comprehensive framework that integrates semantic search, LLM orchestration, and language model workflows in a single package. ### When should I choose EnterpriseRAG-Bench over txtai? Choose EnterpriseRAG-Bench over txtai when License: EnterpriseRAG-Bench is MIT, txtai is Apache-2.0; Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation; When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation. ### 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. ### When should I avoid EnterpriseRAG-Bench? Avoid if your focus is on general web or public-domain document benchmarking, as EnterpriseRAG-Bench is tuned exclusively for company internal documents Do not use if you require a solution that supports languages other than those implied by the existing dataset without further customization ### Is txtai or EnterpriseRAG-Bench more popular on GitHub? txtai has more GitHub stars (12,959 vs 562). Stars measure visibility, not whether either tool fits your constraints. ### Are txtai and EnterpriseRAG-Bench open source? Yes - both are open-source projects on GitHub (txtai: Apache-2.0, EnterpriseRAG-Bench: MIT). ### Where can I find alternatives to txtai or EnterpriseRAG-Bench? GraphCanon lists graph-backed alternatives at [txtai alternatives](/tools/neuml-txtai/alternatives) and [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) ([txtai markdown twin](/tools/neuml-txtai/alternatives.md), [EnterpriseRAG-Bench markdown twin](/tools/onyx-dot-app-enterpriserag-bench/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/neuml-txtai-vs-onyx-dot-app-enterpriserag-bench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, txtai or EnterpriseRAG-Bench? txtai: Very active. EnterpriseRAG-Bench: 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 txtai and EnterpriseRAG-Bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [txtai trust report](/tools/neuml-txtai/trust); [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/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/_