--- title: "DB-GPT-Hub vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eosphoros-ai-db-gpt-hub-vs-sindresorhus-awesome" tools: ["eosphoros-ai-db-gpt-hub", "sindresorhus-awesome"] --- # DB-GPT-Hub vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick DB-GPT-Hub when license: DB-GPT-Hub is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, DB-GPT-Hub is MIT. [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) reports 2.0k GitHub stars, 250 forks, and 73 open issues, last pushed Jul 2, 2025. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [DB-GPT-Hub's repository](https://github.com/eosphoros-ai/DB-GPT-Hub) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [DB-GPT-Hub](/tools/eosphoros-ai-db-gpt-hub.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL | 😎 Curated list of awesome topics including hardware resources | | Stars | 1,997 | 484,026 | | Forks | 250 | 35,799 | | Open issues | 73 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DB-GPT-Hub](/tools/eosphoros-ai-db-gpt-hub.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 374d | 11d | | Open issues (now) | 73 | 92 | | Owner type | Organization | User | | Full report | [trust report](/tools/eosphoros-ai-db-gpt-hub/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose DB-GPT-Hub if… - License: DB-GPT-Hub is MIT, awesome is CC0-1.0. - Tags unique to DB-GPT-Hub: database, datasets, fine-tuning, gpt. - Leaner open-issue backlog (73). ### Choose awesome if… - License: awesome is CC0-1.0, DB-GPT-Hub is MIT. - Tags unique to awesome: awesome-list, resources. - More GitHub stars (484k vs 2.0k) - visibility, not fit. ## When NOT to use DB-GPT-Hub - Last GitHub push was 375 days ago (dormant maintenance, Jul 2, 2025). Validate activity before betting a new project on DB-GPT-Hub. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between DB-GPT-Hub and awesome? DB-GPT-Hub: A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose DB-GPT-Hub over awesome? Choose DB-GPT-Hub over awesome when License: DB-GPT-Hub is MIT, awesome is CC0-1.0; Tags unique to DB-GPT-Hub: database, datasets, fine-tuning, gpt; Leaner open-issue backlog (73). ### When should I choose awesome over DB-GPT-Hub? Choose awesome over DB-GPT-Hub when License: awesome is CC0-1.0, DB-GPT-Hub is MIT; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 2.0k) - visibility, not fit. ### When should I avoid DB-GPT-Hub? Last GitHub push was 375 days ago (dormant maintenance, Jul 2, 2025). Validate activity before betting a new project on DB-GPT-Hub. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is DB-GPT-Hub or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 1,997). Stars measure visibility, not whether either tool fits your constraints. ### Are DB-GPT-Hub and awesome open source? Yes - both are open-source projects on GitHub (DB-GPT-Hub: MIT, awesome: CC0-1.0). ### Where can I find alternatives to DB-GPT-Hub or awesome? GraphCanon lists graph-backed alternatives at [DB-GPT-Hub alternatives](/tools/eosphoros-ai-db-gpt-hub/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([DB-GPT-Hub markdown twin](/tools/eosphoros-ai-db-gpt-hub/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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/eosphoros-ai-db-gpt-hub-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DB-GPT-Hub or awesome? DB-GPT-Hub: Dormant. awesome: 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 DB-GPT-Hub and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DB-GPT-Hub trust report](/tools/eosphoros-ai-db-gpt-hub/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eosphoros-ai-db-gpt-hub`](/api/graphcanon/graph?tool=eosphoros-ai-db-gpt-hub) - 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/_