--- title: "Awesome-LLM-Eval vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/onejune2018-awesome-llm-eval-vs-rohitg00-ai-engineering-from-scratch" tools: ["onejune2018-awesome-llm-eval", "rohitg00-ai-engineering-from-scratch"] --- # Awesome-LLM-Eval vs ai-engineering-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick Awesome-LLM-Eval when tags unique to Awesome-LLM-Eval: bert, evaluation, dataset, benchmark; pick ai-engineering-from-scratch when pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) reports 648 GitHub stars, 78 forks, and 38 open issues, last pushed Nov 24, 2025. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 38k stars, 6.3k forks, and 96 open issues, last pushed Jun 25, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界. | Learn it. Build it. Ship it for others. | | Stars | 648 | 37,922 | | Forks | 78 | 6,329 | | Open issues | 38 | 96 | | Language | - | Python | | Adopt for | - | Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Evaluation & Observability | LLM Frameworks, AI Agents, Developer Tools, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 229d | 15d | | Open issues (now) | 38 | 96 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Decision facts: ai-engineering-from-scratch - **Pricing:** freemium - The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up - **Adopt for:** Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. ## Choose when ### Choose Awesome-LLM-Eval if… - Tags unique to Awesome-LLM-Eval: bert, evaluation, dataset, benchmark. - Also covers Evaluation & Observability. - Leaner open-issue backlog (38). ### Choose ai-engineering-from-scratch if… - Pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up. - Tags unique to ai-engineering-from-scratch: deep-learning, ai-engineering, agents, llm. - Also covers AI Agents, Developer Tools, Computer Vision. - When you want to start with foundational knowledge and learn the intricacies behind AI systems. ## When NOT to use Awesome-LLM-Eval - Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on Awesome-LLM-Eval. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use ai-engineering-from-scratch - If you are looking for a quick setup or ready-to-go solution without diving into the foundational understanding. - When your project requires immediate practical application with less emphasis on self-implemented solutions from scratch. ## Common questions ### What is the difference between Awesome-LLM-Eval and ai-engineering-from-scratch? Awesome-LLM-Eval: Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界.. ai-engineering-from-scratch: Learn it. Build it. Ship it for others.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Eval over ai-engineering-from-scratch? Choose Awesome-LLM-Eval over ai-engineering-from-scratch when Tags unique to Awesome-LLM-Eval: bert, evaluation, dataset, benchmark; Also covers Evaluation & Observability; Leaner open-issue backlog (38). ### When should I choose ai-engineering-from-scratch over Awesome-LLM-Eval? Choose ai-engineering-from-scratch over Awesome-LLM-Eval when Pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up; Tags unique to ai-engineering-from-scratch: deep-learning, ai-engineering, agents, llm; Also covers AI Agents, Developer Tools, Computer Vision; When you want to start with foundational knowledge and learn the intricacies behind AI systems. ### When should I avoid Awesome-LLM-Eval? Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on Awesome-LLM-Eval. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid ai-engineering-from-scratch? If you are looking for a quick setup or ready-to-go solution without diving into the foundational understanding. When your project requires immediate practical application with less emphasis on self-implemented solutions from scratch. ### Is Awesome-LLM-Eval or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 648). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Eval and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Eval: MIT, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to Awesome-LLM-Eval or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/alternatives.md), [ai-engineering-from-scratch markdown twin](/tools/rohitg00-ai-engineering-from-scratch/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/onejune2018-awesome-llm-eval-vs-rohitg00-ai-engineering-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLM-Eval or ai-engineering-from-scratch? Awesome-LLM-Eval: Slowing. ai-engineering-from-scratch: 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 Awesome-LLM-Eval and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=onejune2018-awesome-llm-eval`](/api/graphcanon/graph?tool=onejune2018-awesome-llm-eval) - 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/_