--- title: "LLM-Adapters vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agi-edgerunners-llm-adapters-vs-rohitg00-ai-engineering-from-scratch" tools: ["agi-edgerunners-llm-adapters", "rohitg00-ai-engineering-from-scratch"] --- # LLM-Adapters vs ai-engineering-from-scratch *GraphCanon updated Jul 12, 2026* ## Verdict Pick LLM-Adapters when license: LLM-Adapters is Apache-2.0, ai-engineering-from-scratch is MIT; pick ai-engineering-from-scratch when license: ai-engineering-from-scratch is MIT, LLM-Adapters is Apache-2.0. [LLM-Adapters](https://arxiv.org/abs/2304.01933) reports 1.2k GitHub stars, 119 forks, and 55 open issues, last pushed Mar 10, 2024. [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 [LLM-Adapters's repository](https://github.com/AGI-Edgerunners/LLM-Adapters) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning using Adapters | Learn it. Build it. Ship it for others. | | Stars | 1,233 | 37,922 | | Forks | 119 | 6,329 | | Open issues | 55 | 96 | | Language | Python | 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 | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training | LLM Frameworks, AI Agents, Developer Tools, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 853d | 15d | | Open issues (now) | 55 | 96 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/agi-edgerunners-llm-adapters/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 LLM-Adapters if… - License: LLM-Adapters is Apache-2.0, ai-engineering-from-scratch is MIT. - Tags unique to LLM-Adapters: fine-tuning, adapters, large-language-models, parameter-efficient. - Also covers Model Training. ### Choose ai-engineering-from-scratch if… - License: ai-engineering-from-scratch is MIT, LLM-Adapters is Apache-2.0. - 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 LLM-Adapters - Last GitHub push was 854 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on LLM-Adapters. - 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 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 LLM-Adapters and ai-engineering-from-scratch? LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning using Adapters. 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 LLM-Adapters over ai-engineering-from-scratch? Choose LLM-Adapters over ai-engineering-from-scratch when License: LLM-Adapters is Apache-2.0, ai-engineering-from-scratch is MIT; Tags unique to LLM-Adapters: fine-tuning, adapters, large-language-models, parameter-efficient; Also covers Model Training. ### When should I choose ai-engineering-from-scratch over LLM-Adapters? Choose ai-engineering-from-scratch over LLM-Adapters when License: ai-engineering-from-scratch is MIT, LLM-Adapters is Apache-2.0; 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 LLM-Adapters? Last GitHub push was 854 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on LLM-Adapters. 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 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 LLM-Adapters or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 1,233). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM-Adapters and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to LLM-Adapters or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [LLM-Adapters alternatives](/tools/agi-edgerunners-llm-adapters/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([LLM-Adapters markdown twin](/tools/agi-edgerunners-llm-adapters/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/agi-edgerunners-llm-adapters-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, LLM-Adapters or ai-engineering-from-scratch? LLM-Adapters: Dormant. 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 LLM-Adapters and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Adapters trust report](/tools/agi-edgerunners-llm-adapters/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters`](/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters) - 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/_