Cursor vs Windsurf: Which AI Coding Workflow Fits Monorepo Refactoring Reviews Better?

A refactor that touches forty packages rarely fails because the agent could not write the patch. It fails in review: the diff is too large to read, the local build passes while the workspace build does not, three owners are pinged at once, and nobody can say which of the changed files was actually required by the change in behaviour. The tool that helps most is not the one that types fastest; it is the one whose plan, checkpoints, and command history make the review tractable after the fact.

That is the lens for comparing Cursor and Windsurf. Cursor packages its agent work around plans, checkpoints, and a locally built search index. Windsurf’s agent, Cascade, is now documented and shipped by Cognition under the Devin Desktop name after the acquisition, and its docs live at a new domain — the agent still runs on the plan-and-todo model, with four terminal auto-execution levels and rule files that now read from .devin/ first and .windsurf/ as a fallback (last verified against Cognition’s documentation, October 19, 2026). Both can drive a monorepo refactor. They produce very different review artifacts.

Short answer: choose Cursor when the refactor needs a coordinator that can split work across agents, a rollback that is independent of Git history, and an AI review pass on the resulting diff. Choose Cascade when the bottleneck is plan visibility inside a single long session, when your safety model depends on terminal allow and deny lists that an admin can enforce, or when you want several agents isolated in Git worktrees at once. Neither tool can prove a refactor preserved behaviour; the test suite and the human reviewer still own that claim.

Define what the review has to produce

Before comparing features, write down the deliverable. A monorepo refactoring review needs four things that a normal pull request does not.

• A bounded blast radius. The set of packages the change is allowed to touch, and the set it must not touch. Shared build tooling, generated clients, and lockfiles are the usual leaks.
• Evidence that the whole workspace still builds. A green test run in one package means little if the dependency graph resolves differently at the root.
• A readable diff order. Mechanical renames separated from behavioural edits, so a reviewer can skip the noise and read the risk.
• Named owners per package. Monorepo review fails when the tool decides who reviews, instead of the ownership file.

Everything else — model choice, completion speed, prompt ergonomics — is secondary to those four. A tool that produces a beautiful 6,000-line diff with no scoping is worse than a tool that produces a boring 300-line diff with a plan attached.

Where Cursor fits a monorepo refactor

Cursor’s agent is documented as three parts: instructions (system prompt plus project rules), tools (file editing, codebase search, terminal execution, browser), and the model. Cursor states there is no limit on the number of tool calls an agent can make during a task, which matters in a monorepo where a rename has to be traced across many packages rather than inferred from one open file. For work larger than a single session, Cursor offers Projects, where a coordinator agent plans the work and delegates it to other agents — the closest documented analogue to splitting a refactor into per-package tracks (last verified against Cursor’s agent documentation, October 19, 2026).

The review-relevant feature is checkpoints. Cursor saves snapshots of the codebase during a session, creating them automatically before significant changes and capturing the state of all modified files. Clicking a checkpoint previews files at that point; restoring reverts files to that state, and the documented behaviour is explicit that restoring affects files only, not the conversation. Checkpoints are stored locally and kept separate from Git. That is a real safety net for exploratory refactoring, but it is not a substitute for commits: a locally stored snapshot does not travel to a reviewer, and it will not survive a machine change.

For code egress, Cursor documents that its search layer, Instant Grep, builds and queries its index on your machine, and that Cursor does not upload file paths or code to build a search index and does not store embeddings of your codebase for search. That is a narrower claim than “nothing leaves your laptop”: the documentation also says that when the agent opens a match, that file’s content can be included in the model request.

Command execution is the other half of the review story. Cursor’s stated default is that terminal commands need approval, with Run Modes ranging from a simple allowlist to an Auto-review classifier, and the documentation is candid that these are best-effort guardrails rather than a hard security boundary. Agent edits to workspace files save immediately, while configuration files require approval first. Network requests are documented as limited to GitHub, direct link retrieval, and web search providers under default settings, and .cursorignore blocks agent access to specific files. Bugbot, Cursor’s AI code review, is included from the Premium tier upward.

Cursor fits when the refactor is genuinely parallel — split it into per-package tracks, run them under a coordinator plan, and let checkpoints absorb the wrong turns. The cost is that you must still create the review artifacts yourself: the branch order, the split between mechanical and behavioural commits, and the owner map.

Where Windsurf, now Devin Desktop, fits

Cascade’s documented strength is planning visibility inside one long session. Cognition describes a specialised planning agent that refines the long-term plan in the background while the selected model takes short-term actions, plus a todo list inside the conversation that you can edit by asking for changes. That is a planning artifact a reviewer can read before the diff exists — useful when a monorepo refactor spans several days and several sessions.

Two documented limits deserve attention before you standardise on it. First, Cascade makes up to 40 tool invocations per prompt by default; when it hits that ceiling, pressing continue resumes it, but each continue counts as a new prompt credit, and the Auto-Continue setting (devin.autoContinue) trades that limit for credit consumption. Second, reverts are documented as currently irreversible: you can revert to a named checkpoint or to the state at a previous step, and the documentation warns to be careful because that action cannot itself be undone. For a refactor, that asymmetry argues for committing early and often rather than relying on the agent’s own history.

Terminal control is Cascade’s clearest governance advantage. Four auto-execution levels are documented — Disabled (every command requires approval), Allowlist Only, Auto (model judgement, risky commands still need approval, premium models only), and Turbo (everything auto-runs except the deny list). Allow and deny lists match exact commands or prefixes ending in *, and the deny list takes precedence over the allow list when a command matches both. Teams and Enterprise admins can set a maximum auto-execution level and push team-wide lists that merge with each user’s own; users can then choose any level up to the admin cap. For a monorepo, a team-wide deny list covering force-pushes, history rewrites, and package publishing is a control you can point at during a change review.

Context control has also moved. Rules can be global (one file, capped at 6,000 characters) or workspace-level (one file per rule, capped at 12,000 characters each), with four activation modes — always on, model decision, glob, and manual — plus AGENTS.md files that are always on at the repository root and glob-scoped in subdirectories. The preferred directory is .devin/rules, with .windsurf/rules kept as a fallback, and rules are discovered up the tree to the Git root. Memories are a different mechanism: auto-generated, stored locally under ~/.codeium/windsurf/memories/, scoped to the workspace, never committed, and the documentation recommends writing durable knowledge as a rule or in AGENTS.md rather than relying on memories. Files can be excluded from the agent entirely through .devinignore or the legacy .codeiumignore.

Parallelism is handled through worktrees, which Cognition recommends when several Cascades edit similar files, and the documentation is honest about the failure mode: two Cascades editing the same file at the same time can race, and the second edit can fail. In a monorepo that is not a corner case; it is Tuesday. Worktree isolation is therefore not an optimisation but a requirement if you run more than one agent.

Side by side on the decisions that matter

The workflow that survives review

1. Write the change contract first. Name the packages in scope, the packages explicitly out of scope, the test command that must pass at the repository root, and the owners who must approve. This file goes in the pull request description, not in a chat.
2. Create a clean baseline. Start from a branch with a green root build, then let the agent work in a worktree or a dedicated checkout. If the baseline is red, the agent will “fix” unrelated failures and inflate the diff.
3. Split mechanical from behavioural. Do renames, import moves, and path rewrites as one pass and verify the build; do behaviour changes in a second pass. Reviewers can then read one commit instead of squinting at everything.
4. Checkpoint before every risky step. In Cursor, that is the automatic pre-change snapshot plus a deliberate restore point before touching shared tooling. In Cascade, create a named checkpoint before a multi-file edit, and commit to Git as well, because reverts are documented as irreversible.
5. Run the tests the reviewer will run. Root build, affected-package tests, and the lint or type check that gates the merge. Keep the command output in the pull request; the agent’s summary of the test run is not evidence.
6. Review by package, not by file. For each changed package, ask what behaviour changed and which owner signs off. Use the tool’s AI review as a second pass for missed call sites, never as the approval.
7. Record the rollback. Know which commit reverts cleanly and which migration cannot be reverted without a data step. Write that down before merging, not after.

What leaves the repository, and what it costs

Cursor documents Privacy Mode as available to any user, including free accounts, and states that with it enabled customer data is not used for training, with zero data retention agreements in place with model providers. Without Privacy Mode, the documentation says Cursor may use and store codebase data, prompts, and editor actions to improve features and train models, and that some inference providers may temporarily store inputs and outputs. It also notes that file contents are cached temporarily on its servers, encrypted with client-generated keys that exist only for the duration of a request.

Cognition’s documented default runs the other way: by default data may be used for model training, and paid plans can opt out in Data Controls, after which data is not used for training and zero data retention is enabled with model providers; on Teams only an administrator can exercise the opt-out, and Enterprise customers are never trained on without prior written consent. Retention is stated as the duration of the customer relationship unless agreed otherwise. Cognition also holds SOC 2 Type II, and Cursor lists AIUC-1, ISO/IEC 27001:2022, ISO/IEC 42001:2023, and a SOC 2 Type II attestation.

On price shape, Cursor currently sells a Free tier and paid individual tiers from $8 to $200 per month, with per-seat Teams tiers at $30, $100, or $200 per seat per month and Enterprise custom; third-party models are billed at API list price from prepaid credit on higher tiers, and Teams and Enterprise add a token rate on those requests. Cognition lists Free, Pro at $20 per month, Max at $200 per month, Teams at $80 per month plus $40 per full developer seat for up to 200 users, and Enterprise custom. Both vendors changed plan names and entitlements repeatedly in 2026, and Cursor still supports legacy plans for existing customers, so price the seats you will actually buy rather than the ones on a comparison page (last verified October 19, 2026).

What not to automate

Do not let an agent decide the review scope: the packages in scope come from the change contract and the ownership file. Do not accept a refactor because the agent reports the tests passed — read the command and the exit code. Do not rely on a checkpoint as your only rollback, because checkpoints are local, tied to one session, and, in Cascade’s case, documented as irreversible once used. Do not let a mechanical rename and a behavioural change share a commit. Do not point any agent at a monorepo containing credentials, customer data, or regulated identifiers without excluding those paths through ignore files first. And do not treat an AI review pass as the approver; it is a second reader, not an owner.

Recommendation

Choose Cursor when the refactor is large enough to split across coordinated agents, when you want rollback snapshots that do not depend on Git history, and when you want an AI review pass on the final diff. Choose Cascade when plan visibility within a long session is the bottleneck, when you need admin-enforced terminal allow and deny lists, or when several agents must run in isolated worktrees. If your monorepo has no ownership map and no green root build, fix those first: no agent will produce a reviewable refactor on top of a repository nobody can verify.

Last verified: October 19, 2026. Product naming, documentation locations, rule directories, plan names, prices, retention defaults, and terminal control features change frequently — Windsurf’s Cascade documentation now lives under Cognition’s Devin Desktop domain, which is itself evidence of how fast this surface moves. Confirm every claim against the official documentation for the plan and tier your team actually uses before standardising. Neither product is currently an affiliate partner of this site; the links below are plain official links and nothing in this article is paid placement.

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