--- title: "Awesome-Prompt-Engineering vs ququ" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/promptslab-awesome-prompt-engineering-vs-yan5xu-ququ" tools: ["promptslab-awesome-prompt-engineering", "yan5xu-ququ"] --- # Awesome-Prompt-Engineering vs ququ *GraphCanon updated Jul 11, 2026* ## Verdict Pick Awesome-Prompt-Engineering when awesome-Prompt-Engineering is primarily TypeScript; ququ is JavaScript; pick ququ when ququ is primarily JavaScript; Awesome-Prompt-Engineering is TypeScript. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) reports 6.2k GitHub stars, 723 forks, and 88 open issues, last pushed Jul 11, 2026. [ququ](https://github.com/yan5xu/ququ) has 2.2k stars, 225 forks, and 46 open issues, last pushed Oct 8, 2025. Figures are from public GitHub metadata via [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering) and [ququ's repository](https://github.com/yan5xu/ququ). | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [ququ](/tools/yan5xu-ququ.md) | | --- | --- | --- | | Tagline | This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | 开源免费的 Wispr Flow 替代方案 | 集成FunASR本地模型和可配置大语言模型的下一代中文桌面语音工作流 | | Stars | 6,150 | 2,231 | | Forks | 723 | 225 | | Open issues | 88 | 46 | | Language | TypeScript | JavaScript | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | LLM Frameworks, Model Training, Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [ququ](/tools/yan5xu-ququ.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 276d | | Open issues (now) | 88 | 46 | | Owner type | Organization | User | | Security scan | No lockfile | No criticals | | Full report | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | [trust report](/tools/yan5xu-ququ/trust.md) | ## Choose when ### Choose Awesome-Prompt-Engineering if… - Awesome-Prompt-Engineering is primarily TypeScript; ququ is JavaScript. - License: Awesome-Prompt-Engineering is Apache-2.0, ququ is Other. - Tags unique to Awesome-Prompt-Engineering: gpt-3, chatgpt-api, deep-learning, few-shot-learning. - Also covers LLM Frameworks, Model Training. ### Choose ququ if… - ququ is primarily JavaScript; Awesome-Prompt-Engineering is TypeScript. - License: ququ is Other, Awesome-Prompt-Engineering is Apache-2.0. - Tags unique to ququ: ai-text-processing, chinese-speech-recognition, speech-to-text, local-processing. ## When NOT to use Awesome-Prompt-Engineering - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use ququ - Last GitHub push was 277 days ago (slowing maintenance, Oct 8, 2025). Validate activity before betting a new project on ququ. ## Common questions ### What is the difference between Awesome-Prompt-Engineering and ququ? Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc. ququ: 开源免费的 Wispr Flow 替代方案 | 集成FunASR本地模型和可配置大语言模型的下一代中文桌面语音工作流. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Prompt-Engineering over ququ? Choose Awesome-Prompt-Engineering over ququ when Awesome-Prompt-Engineering is primarily TypeScript; ququ is JavaScript; License: Awesome-Prompt-Engineering is Apache-2.0, ququ is Other; Tags unique to Awesome-Prompt-Engineering: gpt-3, chatgpt-api, deep-learning, few-shot-learning; Also covers LLM Frameworks, Model Training. ### When should I choose ququ over Awesome-Prompt-Engineering? Choose ququ over Awesome-Prompt-Engineering when ququ is primarily JavaScript; Awesome-Prompt-Engineering is TypeScript; License: ququ is Other, Awesome-Prompt-Engineering is Apache-2.0; Tags unique to ququ: ai-text-processing, chinese-speech-recognition, speech-to-text, local-processing. ### When should I avoid Awesome-Prompt-Engineering? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid ququ? Last GitHub push was 277 days ago (slowing maintenance, Oct 8, 2025). Validate activity before betting a new project on ququ. ### Is Awesome-Prompt-Engineering or ququ more popular on GitHub? Awesome-Prompt-Engineering has more GitHub stars (6,150 vs 2,231). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Prompt-Engineering and ququ open source? Yes - both are open-source projects on GitHub (Awesome-Prompt-Engineering: Apache-2.0, ququ: Other). ### Where can I find alternatives to Awesome-Prompt-Engineering or ququ? GraphCanon lists graph-backed alternatives at [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) and [ququ alternatives](/tools/yan5xu-ququ/alternatives) ([Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/alternatives.md), [ququ markdown twin](/tools/yan5xu-ququ/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/promptslab-awesome-prompt-engineering-vs-yan5xu-ququ.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Prompt-Engineering or ququ? Awesome-Prompt-Engineering: Very active. ququ: 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 Awesome-Prompt-Engineering and ququ? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust); [ququ trust report](/tools/yan5xu-ququ/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=promptslab-awesome-prompt-engineering`](/api/graphcanon/graph?tool=promptslab-awesome-prompt-engineering) - 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/_