# How to Run Example Code in ai-engineering-from-scratch: A Complete Execution Guide > Learn to run example code in ai-engineering-from-scratch. Clone the repo, install dependencies, and execute lessons easily with our execution guide. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: how-to-guide - Published: 2026-06-06 --- **Clone the repository, install dependencies via `pip install -r requirements.txt`, and execute any lesson's [`main.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/main.py) directly or use `python3 scripts/lesson_run.py --execute` to run the entire curriculum.** The **ai-engineering-from-scratch** repository by rohitg00 provides a self-contained curriculum with over 500 runnable lessons on AI engineering. Each lesson follows a **Build-It / Use-It** pattern, storing reference implementations under `phases/-/-/code/` that you can execute immediately to validate your understanding. ## Quick Start: Running a Single Lesson Every lesson in the curriculum includes a runnable entry point that demonstrates the concept from first principles. ### Clone the Repository and Install Dependencies Begin by cloning the repository and installing Python dependencies required for the lessons: ```bash git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch python -m pip install -r requirements.txt ``` ### Run Python Lessons Navigate to any lesson directory and execute the [`main.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/main.py) file directly. For example, to run the agent loop lesson in Phase 14: ```bash python phases/14-agent-engineering/01-the-agent-loop/code/main.py ``` Each [`main.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/main.py) contains a header comment referencing its corresponding documentation in `phases///docs/en.md`. Upon successful execution, the script outputs a status line ending with `exit 0`. ### Run TypeScript Lessons For TypeScript implementations, install dev-dependencies first, then execute with `ts-node`. For example, the Constitutional Rules Engine in Phase 19: ```bash cd phases/19-capstone-projects/86-constitutional-rules-engine/code/ts npm install npx ts-node main.ts ``` ## Batch Execution with lesson_run.py The repository includes a generic runner at [`scripts/lesson_run.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/lesson_run.py) that discovers all lessons, compiles them for syntax validation, and optionally executes each `main.*` file. ### Syntax-Only Validation Perform a fast compile check across all 503 lessons without executing them: ```bash python3 scripts/lesson_run.py ``` This walks the `phases/` tree, compiles each `*.py` file to bytecode, and returns a non-zero exit code if any lesson contains syntax errors—ideal for CI pipelines. ### Execute Specific Phases Target a single phase using the `--phase` flag: ```bash python3 scripts/lesson_run.py --phase 14 ``` This restricts execution to lessons within `phases/14-agent-engineering/`, isolating your validation to specific curriculum sections. ### Full Curriculum Execution Run all lessons with a 10-second timeout per lesson using the `--execute` flag: ```bash python3 scripts/lesson_run.py --execute ``` Additional flags provide granular control: - **`--strict`**: Exit with status 1 if any lesson fails (default is tolerant). - **`--json`**: Emit a JSON report summarizing successes and failures. - **`--phase N`**: Restrict execution to a specific phase number. ## Install Generated Skills and Agents Many lessons generate reusable artifacts under `phases///outputs/`, including prompts, skills, agents, and MCP servers. Make these globally available using the skill installer: ```bash python3 scripts/install_skills.py ``` This copies all generated artifacts to `~/.aifromscratch/` for easy reuse across projects. Use `--dry-run` to preview changes without installing, or `--force` to overwrite existing files. ## Practical Walkthrough: Running the Agent Loop Lesson Follow this complete example to execute the Phase 14, Lesson 01 agent loop implementation: ```bash # Clone and enter the repository git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch # Install dependencies (stdlib-only for this lesson, but recommended for consistency) python -m pip install -r requirements.txt # Execute the specific lesson python phases/14-agent-engineering/01-the-agent-loop/code/main.py ``` Expected output: ``` [INFO] Running agent loop with MAX_STEPS=5 [INFO] Step 0 – LLM called, no tool usage [INFO] Step 1 – LLM called, no tool usage … [INFO] Finished after 5 steps – exit 0 ``` Alternatively, use the batch runner to validate this specific phase: ```bash python3 scripts/lesson_run.py --phase 14 --execute ``` ## Summary - **Lesson structure**: Each lesson stores its implementation in `phases///code/main.py` (or [`main.ts`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/main.ts)). - **Direct execution**: Run individual lessons with `python /main.py` after installing [`requirements.txt`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/requirements.txt). - **Batch validation**: Use `python3 scripts/lesson_run.py` for syntax checking or add `--execute` to run the full curriculum. - **Artifact installation**: Run `python3 scripts/install_skills.py` to copy generated skills, prompts, and agents to `~/.aifromscratch/`. - **TypeScript workflow**: For TypeScript lessons, use `npm install` and `npx ts-node main.ts` within the lesson's `code/ts/` directory. ## Frequently Asked Questions ### How do I know if a lesson requires Python or TypeScript? Check the file extension of the `main` file in the lesson's `code/` directory. Python lessons contain [`main.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/main.py), while TypeScript lessons include a `ts/` subdirectory with [`main.ts`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/main.ts) and a [`package.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/package.json). The README in each phase directory also specifies the primary language for that section of the curriculum. ### What should I do if a lesson execution times out? The batch runner ([`scripts/lesson_run.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/lesson_run.py)) enforces a 10-second timeout per lesson. If a specific lesson hangs, run it individually to debug: `python phases///code/main.py`. Lessons involving external API calls may require environment variables or network connectivity not provided in the base repository. ### Can I run the example code without installing dependencies? Some lessons use only Python standard library modules and will run without `pip install -r requirements.txt`. However, most advanced lessons in later phases require external packages such as `requests`, `openai`, or `numpy`. Always install dependencies first to ensure consistent execution across all 503 lessons. ### Where are the generated skills and prompts stored after running a lesson? Reusable artifacts appear in the `phases///outputs/` directory within each lesson folder. To make these available system-wide, run `python3 scripts/install_skills.py`, which copies them to `~/.aifromscratch/` for integration with external AI engineering tools.