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The best AI code editors in 2025

AI coding tools and agentic IDEs that speed up software creation. These assistants edit multi-file projects, suggest code, automate CLI tasks, and help build mobile apps fast.

CursorClaude CodeWindsurfGithub CopilotTraePythagora
AssemblyAI
AssemblyAI Build voice AI apps with a single API

Top reviewed AI code editors

Top reviewed
, , and anchor this space with distinct strengths: Cursor streamlines everyday coding with in-editor generation and refactors; Claude Code excels at large-context reasoning for complex, multi-file changes; Windsurf blends copilot-style autocompletion with agentic task execution and collaboration. Together they span rapid prototyping, deep codebase navigation, and coordinated end-to-end delivery.
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Frequently asked questions about AI Code Editors

  • Yes — but it’s often done via multiple IDE instances, worktrees, or sub‑agents rather than a single in‑editor switch.

    • How it’s commonly implemented: teams spin up multiple IDEs or cloud codespaces and use git worktrees/branches to run parallel agent runs on the same feature (so each agent keeps its own workspace and diffs)
    • Emerging features: some tools let you assign tasks to worktrees, fork conversations mid‑process, and run concurrent models to converge on the best solution
    • Coordination: you can already define rules/system prompts for sub‑agents, but human criteria or a selection task is usually used to pick the final merge

    Expect more built‑in orchestration soon as these platforms evolve.

  • Cursor: yes — it supports full‑file and multi‑file changes and shows diffs inline so you can review and edit agent output before committing.

    Other tools take a slightly different approach:

    • Qoder scans the whole project (dependencies, schemas, app context) so multi‑file refactors are planned with architectural awareness and can auto‑fix common errors or flag duplications.
    • Verdent offers a Plan Mode + DiffLens and a Code Review flow so you can inspect proposed multi‑file edits and choose which changes to accept.

    Note: AI suggestions still need human review on complex code; expect to tweak or rerun agents for tricky refactors.

  • Qoder can generate tests and integrate with run/debug workflows. Qoder’s memory can be taught to always generate unit tests after implementation, so test creation is a built-in part of its automation. Cursor also helps with testing and real‑time debugging inside the editor, making it easy to run tests from the in‑editor terminal.

    What to expect:

    • Generate: AI will scaffold unit/integration tests using project context. (You should still review them.)
    • Execute: Tools integrate with your debugger/terminal so you can run tests from the editor.
    • Limits: Full end‑to‑end automation (generate → run → auto‑verify/fix) varies by product and usually keeps a human in the loop.