quarkus-langchain4j-workshop vs ai-engineering-from-scratch

AI engineering learning projects compared

Both projects teach practical AI development through runnable code and structured learning material. quarkus-langchain4j-workshop focuses on building Java applications with Quarkus and LangChain4j, while ai-engineering-from-scratch covers a broader curriculum across AI foundations, LLMs, agents, and production topics.

quarkus-langchain4j-workshopai-engineering-from-scratch
LanguageJavaPython
LicenseApache-2.0MIT
Stars1062.9k
Forks910.8k
Last analyzedOct 15, 2026Oct 3, 2026

Key differences

  • quarkus-langchain4j-workshop is a step-based workshop centered on Quarkus and LangChain4j; ai-engineering-from-scratch is a 20-phase curriculum spanning AI foundations through production.
  • quarkus-langchain4j-workshop provides Java applications for each step; ai-engineering-from-scratch offers lessons in Python, TypeScript, Rust, and Julia, depending on the lesson.
  • quarkus-langchain4j-workshop uses the Apache-2.0 license; ai-engineering-from-scratch uses the MIT license.
  • quarkus-langchain4j-workshop is aimed at learning Quarkus-based AI application development; ai-engineering-from-scratch supports both broad study paths and focused routes such as agent engineering and MCP.
  • quarkus-langchain4j-workshop includes a Maven wrapper and a local endpoint at http://localhost:8080 when an application is running; ai-engineering-from-scratch provides a website, repository lessons, AI tutor skills, and downloadable book editions.

Choose quarkus-langchain4j-workshop if you…

  • want a guided Java workshop using Quarkus and LangChain4j.
  • prefer step-by-step instructions with runnable final states to compare your progress.
  • are teaching or joining a focused exercise in building AI-infused applications.
Read the quarkus-langchain4j-workshop analysis →

Choose ai-engineering-from-scratch if you…

  • want a broad route from AI foundations to LLMs, agents, and production topics.
  • need lessons or focused paths covering areas such as RAG, evaluation, agent engineering, or MCP.
  • prefer practical examples and artifacts across multiple programming languages.
Read the ai-engineering-from-scratch analysis →

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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.