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Install JetBrains AI Assistant in PyCharm or IntelliJ, open a tiny Python lab, run one Agents-mode task, and review every change—built for after-work evenings.
JetBrains AI Assistant brings chat and agent workflows into IntelliJ IDEA, PyCharm, and other JetBrains IDEs. You ask in plain language; the assistant can explain code, generate snippets, and—in Agents mode—plan multi-step edits you review before keeping. Product home: jetbrains.com/ai. Install guide: AI Assistant installation. Chat and Agents overview: AI Chat.
This guide is for after-work Python practice. You keep your day-job IDE habits if you already use PyCharm, or you try Community / free tiers for evening labs. You do not need to migrate a whole company stack tonight—only a tiny practice folder and one supervised agent run.
Working professionals who already open JetBrains tools (or want one editor for Python and data notebooks) and want AI help without abandoning the JetBrains keymap. Analysts, ops, finance, and career-switchers who review every change before it sticks.
If you are comparing evening-friendly agent tools, see Top 10 AI agent tools for after-work Python.
The plugin is not bundled by default. In your IDE open Settings → Plugins → Marketplace, search AI Assistant, and install. You can also start from the AI Chat tool window or the JetBrains AI widget when the IDE offers Install Plugin. Prefer JetBrains Marketplace over unofficial jars. After install, activate with your JetBrains Account when prompted—see official docs for license and provider options.
Create evening-python/jetbrains-lab/ with one file:
# greet.py
def greet(name: str) -> str:
return f"Hello, {name}"
if __name__ == "__main__":
print(greet("world"))
Open that folder (or mark it as the project root). Agents and chat behave better when the workspace is the lab you care about—not your entire home directory or a confidential work repo.
Open AI Chat from the right toolbar. Use Chat mode for questions and explanations (you apply code yourself). Switch to Agents when you want multi-step edits: the agent can change files and report progress; you keep or roll back results.
Give a bounded task, for example:
Add
greet_many(names: list[str]) -> list[str]togreet.py, keep type hints, print joined results in__main__. Do not add new files or dependencies.
Read every suggested hunk. Reject noisy renames and surprise libraries. In the terminal:
python greet.py
Confirm output matches the prompt. The skill you are building is supervision—especially after a long workday.
Write NOTES.md: what worked, which model you used, what you rejected. Next session should start in under two minutes.
Ask the agent (or Chat + apply) for a pure function normalize_name(s: str) -> str that strips whitespace and title-cases, then wire it into greet. Run the script. If the agent pulls a packaging library for string casing, reject that hunk and insist on the standard library.
JetBrains AI Assistant meets you inside an IDE you may already trust for Python debugging and refactoring. A single evening is enough to install the plugin, open a lab folder, and complete one Agents-mode edit with a full review. You are not migrating teams, wiring CI, or benchmarking every model—just proving the loop: prompt → diff → python greet.py → note for tomorrow. That loop is what transfers when you later try bigger agent tools.
When JetBrains AI feels like a junior pair instead of magic, strengthen the Python underneath. SoloQuant’s Intro to Python Programming is built for after-work short lessons—so you can tell the assistant what good code looks like, not only hope it guesses.