--- title: "simple-evals vs ai-engineering-hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openai-simple-evals-vs-patchy631-ai-engineering-hub" tools: ["openai-simple-evals", "patchy631-ai-engineering-hub"] --- # simple-evals vs ai-engineering-hub *GraphCanon updated Jul 11, 2026* ## Verdict Pick simple-evals when simple-evals is primarily Python; ai-engineering-hub is Jupyter Notebook; pick ai-engineering-hub when ai-engineering-hub is primarily Jupyter Notebook; simple-evals is Python. [simple-evals](https://github.com/openai/simple-evals) reports 4.6k GitHub stars, 493 forks, and 56 open issues, last pushed Apr 22, 2026. [ai-engineering-hub](https://join.dailydoseofds.com) has 36k stars, 6.0k forks, and 119 open issues, last pushed Jun 8, 2026. Figures are from public GitHub metadata via [simple-evals's repository](https://github.com/openai/simple-evals) and [ai-engineering-hub's repository](https://github.com/patchy631/ai-engineering-hub). | | [simple-evals](/tools/openai-simple-evals.md) | [ai-engineering-hub](/tools/patchy631-ai-engineering-hub.md) | | --- | --- | --- | | Tagline | simple-evals | Tutorials on LLMs, RAGs, and real-world AI agent applications | | Stars | 4,565 | 36,439 | | Forks | 493 | 6,039 | | Open issues | 56 | 119 | | Language | Python | Jupyter Notebook | | Adopt for | - | A collection of in-depth tutorials aiming to cover a wide range from beginner to advanced concepts in AI, including large language models (LLMs), Retrieval-Augmented Generation (RAG) systems and practical applications of | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License | | Categories | Evaluation & Observability, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [simple-evals](/tools/openai-simple-evals.md) | [ai-engineering-hub](/tools/patchy631-ai-engineering-hub.md) | | --- | --- | --- | | Days since push | 79d | 32d | | Open issues (now) | 56 | 119 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/openai-simple-evals/trust.md) | [trust report](/tools/patchy631-ai-engineering-hub/trust.md) | ## Decision facts: ai-engineering-hub - **Requirements:** The tutorials and projects use Jupyter Notebooks which require Python and a compatible local environment or cloud-based Jupyter services. - **Adopt for:** A collection of in-depth tutorials aiming to cover a wide range from beginner to advanced concepts in AI, including large language models (LLMs), Retrieval-Augmented Generation (RAG) systems and practical applications of - **License detail:** MIT License ## Choose when ### Choose simple-evals if… - simple-evals is primarily Python; ai-engineering-hub is Jupyter Notebook. - Tags unique to simple-evals: python. - Also covers Evaluation & Observability. ### Choose ai-engineering-hub if… - ai-engineering-hub is primarily Jupyter Notebook; simple-evals is Python. - Requirements: The tutorials and projects use Jupyter Notebooks which require Python and a compatible local environment or cloud-based Jupyter services.. - Tags unique to ai-engineering-hub: agents, ai, llms, machine-learning. - Also covers AI Agents. - When you are looking for comprehensive learning paths ranging from complete beginners to advanced experts. ## When NOT to use simple-evals - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use ai-engineering-hub - If your team already has significant proficiency in AI engineering and advanced LLM frameworks, as the content starts from zero knowledge up. - When you specifically need industry-standard proprietary tools or heavily specialized niche applications that go beyond foundational learning covered by this hub. - In scenarios where immediate advanced project results are required; ai-engineering-hub focuses on education through step-by-step tutorials rather than providing ready-made solutions with minimal setup ## Common questions ### What is the difference between simple-evals and ai-engineering-hub? simple-evals: simple-evals. ai-engineering-hub: Tutorials on LLMs, RAGs, and real-world AI agent applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose simple-evals over ai-engineering-hub? Choose simple-evals over ai-engineering-hub when simple-evals is primarily Python; ai-engineering-hub is Jupyter Notebook; Tags unique to simple-evals: python; Also covers Evaluation & Observability. ### When should I choose ai-engineering-hub over simple-evals? Choose ai-engineering-hub over simple-evals when ai-engineering-hub is primarily Jupyter Notebook; simple-evals is Python; Requirements: The tutorials and projects use Jupyter Notebooks which require Python and a compatible local environment or cloud-based Jupyter services.; Tags unique to ai-engineering-hub: agents, ai, llms, machine-learning; Also covers AI Agents; When you are looking for comprehensive learning paths ranging from complete beginners to advanced experts. ### When should I avoid simple-evals? Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid ai-engineering-hub? If your team already has significant proficiency in AI engineering and advanced LLM frameworks, as the content starts from zero knowledge up. When you specifically need industry-standard proprietary tools or heavily specialized niche applications that go beyond foundational learning covered by this hub. In scenarios where immediate advanced project results are required; ai-engineering-hub focuses on education through step-by-step tutorials rather than providing ready-made solutions with minimal setup ### Is simple-evals or ai-engineering-hub more popular on GitHub? ai-engineering-hub has more GitHub stars (36,439 vs 4,565). Stars measure visibility, not whether either tool fits your constraints. ### Are simple-evals and ai-engineering-hub open source? Yes - both are open-source projects on GitHub (simple-evals: MIT, ai-engineering-hub: MIT). ### Where can I find alternatives to simple-evals or ai-engineering-hub? GraphCanon lists graph-backed alternatives at [simple-evals alternatives](/tools/openai-simple-evals/alternatives) and [ai-engineering-hub alternatives](/tools/patchy631-ai-engineering-hub/alternatives) ([simple-evals markdown twin](/tools/openai-simple-evals/alternatives.md), [ai-engineering-hub markdown twin](/tools/patchy631-ai-engineering-hub/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/openai-simple-evals-vs-patchy631-ai-engineering-hub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, simple-evals or ai-engineering-hub? simple-evals: Steady. ai-engineering-hub: Steady. 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 simple-evals and ai-engineering-hub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [simple-evals trust report](/tools/openai-simple-evals/trust); [ai-engineering-hub trust report](/tools/patchy631-ai-engineering-hub/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openai-simple-evals`](/api/graphcanon/graph?tool=openai-simple-evals) - 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/_