Action guide
Setting Up AI Projects in Python
Your AI repo outgrew the demo script. Env files, pyproject, src layout, and a clean split between HTTP surfaces and agent code.
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Why subscribe
The repo that worked Saturday dies by Wednesday. You need a folder tree you can explain at 3 a.m. Env files. Packaging. Obvious homes for HTTP code versus agent code so nobody asks where the bodies are buried.
For: Engineers building Python AI services who still own the repo after the demo glow fades. Structure beats pedigree.
- A repo layout that scales past the first happy path
- Clear seams between API routes, domain logic, and agent code
- A setup that makes iteration and handoff less bruising
- Full project walkthrough with a concrete tree
- Config and environment patterns that do not rot on M1 and Linux
- A template you can clone for the next product
- Optimizes for the problems that show up in review, not tutorial minimalism
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What you’ll learn
How to organize a repo so it stays shippable: .env vs .env.example, what belongs in pyproject.toml, a src/ importable package, and why api/ (FastAPI app, routers, health) stays separate from agents/ (graphs, executors) so HTTP concerns do not tangle with model logic.
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