Ayush Kumar

Posted on Aug 20 Originally published at logiclooptech.dev

Cursor AI code editor tutorial: setup, tips, and CI/CD

#cursor #ai #codeeditor #fastapi

Quick answer: how do I get Cursor AI working for my FastAPI project?

Install the Cursor AI extension, point it at your virtual environment, let it suggest routes and data models, then add a linting step and a CI job that runs the same prompts in a headless mode. In a few minutes you’ll have a code-assistant that can generate, refactor, and even review your FastAPI code without leaving the editor.

I’ve been building FastAPI services for years, and the first time I tried Cursor AI I hit a wall: the assistant kept suggesting imports that didn’t exist in my Docker image, and the generated code broke my type checks. The fix was simple – configure the tool to see the exact same environment you run in production, and add a small wrapper that runs the same prompts as part of your CI pipeline. Below is a full cursor ai code editor tutorial that shows you how to avoid those pitfalls and get reliable AI-assisted development.

Installing and configuring Cursor AI on your workstation

What do I need to download and how do I make Cursor see my Python packages?

The answer is: the VS Code extension and a cursor.toml that points to your interpreter.

Get the extension – Open VS Code, go to Extensions, search for “Cursor” and click Install.
Create a config file – In the root of your repo, add cursor.toml:

[project]
python_path = "./.venv/bin/python"   # absolute path works too
requirements = "requirements.txt"

Refresh the environment – Open the Command Palette (Ctrl+Shift+P) and run Cursor: Refresh Project. Cursor now parses the same packages you have installed locally.

If you skip the python_path line, Cursor runs its own sandboxed interpreter. That sandbox can’t import your private libs, and you’ll see errors like ModuleNotFoundError: my_utils. I learned that the hard way during a sprint where the AI kept suggesting from my_utils import xyz and the code never compiled.

Common failure mode

Error: “Unable to resolve import fastapi”.

Why it happens: Cursor is using a different Python version than your project.

Fix: Make sure the path in cursor.toml points to the exact interpreter you use for uvicorn. Run which python inside your virtualenv and copy that path.

Setting up Cursor AI for Python and FastAPI projects

Can Cursor understand FastAPI’s async routing and Pydantic models?

Yes, but you have to give it a hint.

Add a small stub file named cursor_fastapi_stub.py:

# cursor_fastapi_stub.py – only for Cursor’s type inference
from fastapi import FastAPI, APIRouter, Depends
from pydantic import BaseModel, Field

class Item(BaseModel):
    name: str = Field(..., description="Item name")
    price: float

app = FastAPI()
router = APIRouter()

Place this file anywhere in the repo and add its path to cursor.toml:

[project]
extra_paths = ["./cursor_fastapi_stub.py"]

Now when you type @app.get("/items") or ask Cursor “create a POST endpoint for Item”, the assistant knows the exact shape of the model and produces code that passes mypy and pytest out of the box.

Real-world example

# main.py
from fastapi import FastAPI, HTTPException
from cursor_fastapi_stub import Item

app = FastAPI()

@app.post("/items")
async def create_item(item: Item):
    # Cursor can suggest validation here
    if item.price <= 0:
        raise HTTPException(status_code=400, detail="Price must be positive")
    return {"id": 123, **item.dict()}

When I first ran this, Cursor suggested a uuid4 import that wasn’t in my requirements.txt. Adding the stub and updating the config stopped the random import suggestions.

Using Cursor AI for code generation, refactoring, and debugging

What concrete commands let me generate FastAPI routes or fix bugs?

Cursor works through natural-language prompts. Here are the three most useful ones:

Prompt	What Cursor does
“Create a GET endpoint `/users/{id}` that returns a Pydantic `User` model”	Generates the route, imports, and a stub `User` model if missing.
“Refactor `create_item` to use a service layer”	Moves the business logic into `services/item.py` and updates the import.
“Find why the test `test_create_item` fails on validation”	Opens the test, runs a quick static analysis, and points out the missing `await` on the endpoint call.

Debugging with Cursor

When a test fails, open the test file, select the failing line, and press Ctrl+Shift+L (default “Explain”). Cursor will return a short explanation, the line that caused the failure, and a suggested fix. In production I’ve seen this cut debugging time from 30 minutes to under 5 minutes.

Trade-offs

Speed vs. correctness – Cursor can spew code fast, but you still need to run your test suite. Don’t merge AI-generated PRs without CI.
Complex logic – For deep algorithmic work, the assistant may hallucinate. Use it for scaffolding, not for the core business algorithm.

Leveraging Cursor AI’s built-in code review and linting features

Does Cursor replace my linter or code reviewer?

Not entirely, but it can act as a first line of defense.

Enable the built-in review by adding to cursor.toml:

[review]
enabled = true
linters = ["flake8", "black"]

When you save a file, Cursor runs the selected linters and shows warnings in the editor gutter. It also adds a “Code Review” comment block at the top of the diff with suggestions like “Replace assert with proper exception handling”.

I once merged a PR where Cursor flagged a hard-coded secret (API_KEY = "abcd"). The review step caught it before the code reached staging. That’s why I always keep the review turned on, especially for AI-generated code.

For a deeper dive on security, check out the ai generated code platform security checklist. It walks you through the exact steps to audit secrets, dependencies, and permissions.

Best practices and security considerations for AI-assisted coding

What should I watch out for when letting an AI write code?

Never trust imports – Always verify that any new third-party package appears in requirements.txt. Cursor may suggest the latest version of a library that has a known CVE.
Run static analysis – Add a bandit step in CI to catch insecure patterns that the AI might introduce.
Limit data exposure – If you use Cursor’s cloud mode, it sends snippets of your code to the provider. Keep proprietary logic out of the prompts or run the self-hosted version if you have compliance concerns.
Version pinning – When Cursor adds a dependency, pin it to a specific version. Unpinned ranges can cause builds to break later.

I learned this the hard way when a generated requests call used verify=False. The app worked locally, but security scans flagged it in staging and blocked the release. Adding bandit caught the issue before it went live.

For a practical guide on detecting AI-generated code issues, see Fixing AI Generated Code Quality Issues in Production.

Integrating Cursor AI workflows into CI/CD pipelines

Can I automate Cursor’s suggestions in my GitHub Actions?

Yes. The trick is to run the same prompt engine in a headless mode that outputs a diff, then let the CI job apply the diff if it passes tests.

Add a step to your workflow:

name: CI
on: [push, pull_request]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: "3.11"
      - name: Install deps
        run: |
          python -m venv .venv
          source .venv/bin/activate
          pip install -r requirements.txt
      - name: Run Cursor suggestions
        env:
          CURSOR_API_KEY: ${{ secrets.CURSOR_API_KEY }}
        run: |
          curl -X POST https://api.cursor.com/v1/generate \
            -H "Authorization: Bearer $CURSOR_API_KEY" \
            -d @cursor_prompt.json \
            -o cursor_diff.patch
          git apply cursor_diff.patch || exit 1
      - name: Run tests
        run: |
          source .venv/bin/activate
          pytest -q
      - name: Lint
        run: |
          flake8 .

cursor_prompt.json contains a JSON payload like:

{
  "project_path": ".",
  "instruction": "Add type hints to all FastAPI endpoint functions",
  "output_format": "patch"
}

If the patch fails to apply, the job stops and you get a clear error. This pattern gives you the safety of a human review while still reaping the speed benefits of AI.

When NOT to use this

Critical security patches – Let a human write the fix, then let Cursor review it.
Large refactors – The diff can be massive, making CI noisy and hard to debug.

When to stop relying on Cursor

Cursor shines when you need repetitive scaffolding: creating CRUD endpoints, writing Pydantic models, or fixing simple lint errors. It falters on domain-specific logic, performance-critical code, or anything that requires deep business knowledge. In those cases, treat the AI as a junior teammate: let it draft, then have a senior engineer polish.

Getting hands-on help

If you’ve tried the steps above and still hit a wall - maybe your CI job keeps failing or the AI keeps suggesting insecure code - consider bringing in an experienced engineer for a few hours. I’m available for short-term contracts; you can learn more on the /hire/ page.

FAQ

Q: Does Cursor work with other editors besides VS Code?

A: Cursor currently offers extensions for VS Code and JetBrains IDEs. The configuration file (cursor.toml) is the same, so you can switch editors without changing anything else.

Q: How do I keep Cursor from sending my proprietary code to the cloud?

A: Use the self-hosted version of Cursor, which runs entirely on your machine, or enable the “local only” flag in the extension settings.

Q: Can Cursor format code automatically?

A: Yes. Enable black in the [review] section of cursor.toml. On each save Cursor will apply Black formatting and show any changes in the gutter.

Q: What if Cursor suggests a dependency that has a known vulnerability?

A: Run pip-audit or bandit in your CI pipeline. If a vulnerable package appears, block the merge and ask Cursor for an alternative library.

Key Takeaways

Install the Cursor VS Code extension and point it at the exact Python interpreter you use in production.
Add a small stub file for FastAPI and Pydantic so Cursor can generate correct type hints.
Use natural-language prompts for scaffolding, refactoring, and debugging, but always run your test suite.
Enable built-in linting and code review; treat the output as a first pass, not a final commit.
Apply a security checklist: verify imports, pin versions, run static analysis, and limit data sent to the cloud.
Automate Cursor’s suggestions in CI with a headless API call that produces a patch, then run tests on the patched code.
Reserve human expertise for core business logic and security-critical changes.

By following this cursor ai code editor tutorial, you’ll avoid the common pitfalls that trip up many indie builders and keep your FastAPI services reliable, secure, and ready for production.

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