Best AI Coding Tool for Python

For Python, we rank Cursor first for service work, as an editor built around its agent. Copilot ranks second as the low-change option, and its agent mode also runs commands. Both publish a Python SDK, and notebook work needs its own test, covered below.

A ranked list hides the variable that decides the choice: switching cost. A tool that wins on features but makes your team change editors does not automatically beat one that scores a little lower and already lives where people work. That trade looks different for four people than for two hundred with an established review process, so no single order is right for both.

Rank tools on how they behave in real work instead of in demos. The criteria below change how a team's week goes.

• Agent reliability on real code, not demo tasks.
• Review load after the agent writes code.
• Cost predictability for the team.
• Security controls, audit path and admin fit.
• Fit with the team's editor, repo and CI workflow.

Review load is the criterion most comparisons skip. If changes arrive twice as fast but each one takes a senior engineer three times as long to read, the work has not gone away. It has moved to the people with the least room for it, so ask what happens after the code is written as well as how fast it arrived.

Cost predictability deserves the same scrutiny. You can plan around per-seat pricing. Usage-based pricing is harder to plan once agents run longer tasks and people learn they can leave them running, and its failure mode is a bill that arrives after the money is spent. Whichever model you pick, know which number finance will ask about at quarter end, and make sure you can see it first.

Cursor ships often, so advice about it can go out of date. The table summarizes Cursor's recent releases, so you can check this page against the current product. For each release's date and sources, see What's new in Cursor 2026.

No, because of a limit, not a preference. With the Jupyter extension installed, Cursor opens notebooks with cell execution, and Tab and Agent work in them as in any other file. Running the cells still falls to you: in a Cursor data-science walkthrough, the presenter ran every cell by hand because the agent couldn't run them reliably. For notebook work, you are judging context quality and how fast the loop feels, because you close the loop yourself.

Service code works the other way round, because there the agent can run the command itself. The terminal tool runs shell commands directly in your terminal, and your Run Mode setting decides whether a command runs on its own, waits for your approval or runs in the sandbox.

So one ranking across "Python" isn't much use. A team shipping services and a team working in notebooks are buying different things. We expect the notebook team to be let down more often, because tool comparisons tend to assume the agent closes its own loop.

Install the Jupyter extension published by ms-toolsai, and check the publisher before you install any extension with a similar name.

The import that exists and still fails. A package installed in your system Python but missing from the virtual environment the notebook uses produces errors like "pandas could not be resolved" or "matplotlib not installed".

In the same walkthrough, this was the case Cursor handled well. With the failing cell added to chat in Agent modeCursor's full-capability mode: the AI can read the codebase, write and edit files, move them and run terminal commands. Contrast with Ask mode, which is read-only. Press Enter for the full definition., the agent found the mismatch between the venv and the system Python, installed the package into the active venv and checked the import again.

Our guess is that models do well here because Python is so heavily represented in their training data. Either way, pip, version and venv problems are the easy part of the job.

That doesn't make tool choice matter less for Python. It makes the visible failures cheap, which moves the real difference somewhere a two-week trial struggles to see. Check whether the agent respects the boundary between a service and the scripts around it, and whether its diff is small enough that someone reads all of it.

Pick the environment on purpose at setup. In the walkthrough, Cursor asked whether to install into a virtual environment or the project, and choosing the venv keeps the work reproducible later.

Yes. The Python package is cursor-sdk on PyPI, and it needs Python 3.10 or later. Until release 1.0.24, the Python SDK trailed the TypeScript one. From 1.0.24 on, both ship from the same release and share a version number.

For cost attribution, check two versions. Python runs have emitted token usage since 1.0.23, matching TypeScript from 1.0.22, but token counts say nothing about cost. Billed cost arrived in 1.0.25, when agent.get_usage() began returning token usage, billed cost and a per-run breakdown for cloud agents, and local agents followed in 1.0.27, once usage access is enabled for the account. SDK runs follow the same pricing, request pools and Privacy ModeCursor's setting that guarantees code data is not used for training by Cursor or its model providers, and that an admin can enforce org-wide; data-retention terms are a separate, contractual layer. Press Enter for the full definition. rules as runs from the IDE, and the spend appears in the team usage dashboard under the SDK tag.

If you are choosing for a Python team, run this check before the agent comparison, and run it on both tools. GitHub publishes a Python SDK for Copilot's agent too, github-copilot-sdk, which needs Python 3.11 or later. A tool your data people call from a script is a different purchase from an editor they open, and the pricing and review questions change with it.

Cursor publishes role-specific cookbooks. The data-science one covers notebook development and two ways to connect a database: an MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. server, with Postgres as its example, or extensions for PostgreSQL, BigQuery, SQLite and Snowflake. If your Python work is mostly querying and reshaping data, start there instead of with a general tool ranking.

The mode habit from that world carries over. Ask modeA read-only mode for asking questions about a codebase without changing files; the safe way to explore unfamiliar or legacy code. Press Enter for the full definition. answers questions and never edits, so an analyst or a manager can open an unfamiliar notebook, ask what it does and what is missing, and know that nothing changed. Stay in Ask while you are still working out what the code does, and switch to Agent when you want a change made.