--- title: "entaoai vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/akshata29-entaoai-vs-rohitg00-ai-engineering-from-scratch" tools: ["akshata29-entaoai", "rohitg00-ai-engineering-from-scratch"] --- # entaoai vs ai-engineering-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick entaoai when entaoai is primarily TypeScript; ai-engineering-from-scratch is Python; pick ai-engineering-from-scratch when ai-engineering-from-scratch is primarily Python; entaoai is TypeScript. [entaoai](https://github.com/akshata29/entaoai) reports 866 GitHub stars, 245 forks, and 12 open issues, last pushed Jan 2, 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 [entaoai's repository](https://github.com/akshata29/entaoai) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [entaoai](/tools/akshata29-entaoai.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | Chat and Ask on your own data. Accelerator to quickly upload your own enterprise data and use OpenAI services to chat to that uploaded data and ask questions | Learn it. Build it. Ship it for others. | | Stars | 866 | 37,922 | | Forks | 245 | 6,329 | | Open issues | 12 | 96 | | Language | TypeScript | 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 | Vector Databases, LLM Frameworks | LLM Frameworks, AI Agents, Developer Tools, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [entaoai](/tools/akshata29-entaoai.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 554d | 15d | | Open issues (now) | 12 | 96 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/akshata29-entaoai/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 entaoai if… - entaoai is primarily TypeScript; ai-engineering-from-scratch is Python. - Tags unique to entaoai: gpt-3, azureopenai, cognitive-search, azure-webapp. - Also covers Vector Databases. ### Choose ai-engineering-from-scratch if… - ai-engineering-from-scratch is primarily Python; entaoai is TypeScript. - 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 entaoai - Last GitHub push was 555 days ago (dormant maintenance, Jan 2, 2025). Validate activity before betting a new project on entaoai. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - 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-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 entaoai and ai-engineering-from-scratch? entaoai: Chat and Ask on your own data. Accelerator to quickly upload your own enterprise data and use OpenAI services to chat to that uploaded data and ask questions. 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 entaoai over ai-engineering-from-scratch? Choose entaoai over ai-engineering-from-scratch when entaoai is primarily TypeScript; ai-engineering-from-scratch is Python; Tags unique to entaoai: gpt-3, azureopenai, cognitive-search, azure-webapp; Also covers Vector Databases. ### When should I choose ai-engineering-from-scratch over entaoai? Choose ai-engineering-from-scratch over entaoai when ai-engineering-from-scratch is primarily Python; entaoai is TypeScript; 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 entaoai? Last GitHub push was 555 days ago (dormant maintenance, Jan 2, 2025). Validate activity before betting a new project on entaoai. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. 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-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 entaoai or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 866). Stars measure visibility, not whether either tool fits your constraints. ### Are entaoai and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (entaoai: MIT, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to entaoai or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [entaoai alternatives](/tools/akshata29-entaoai/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([entaoai markdown twin](/tools/akshata29-entaoai/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/akshata29-entaoai-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, entaoai or ai-engineering-from-scratch? entaoai: 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 entaoai and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [entaoai trust report](/tools/akshata29-entaoai/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=akshata29-entaoai`](/api/graphcanon/graph?tool=akshata29-entaoai) - 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/_