Claude Code vs Cursor: How Each One Thinks

Claude Code and Cursor both call themselves AI coding tools, but they work in different ways. Claude Code is an agent harness, born in the terminal, that breaks a task into tool-using loops and keeps going until the task is done. Cursor builds its AI into a fork of the VS Code editor and keeps the human close to every change, with fast autocomplete, quick edits and an agent that works inside the editor.

Control lives in different places. Claude Code behaves like a delegated engineer who works through the job and asks for approval according to the permission mode you chose, so you trust its plan and review the result. Cursor behaves like a fast pair programmer who shows you every move, trading more frequent interaction for deep integration with what is open in your editor. Both have grown toward each other, and both now run agents in the cloud too, but their defaults still reflect those two ideas.

Two philosophies, one workweek

Think of an engineer, Alice, adopting both tools over five days. On Monday she builds a new endpoint. She opens Cursor, gets inline completions for the boilerplate, and uses Agent mode to add validation logic. She stays in flow, reviewing each diff as it appears. On Wednesday she faces a cross-module refactor: renaming a core domain concept across fifty files. She opens her terminal, describes the full task to Claude Code, and lets it plan and execute while she gets a coffee. When she returns, the changes are made, the tests pass, and a commit message is ready.

These two experiences are not just different interfaces. They reflect two theories of how an AI should work on code. Let's break them down.

How Claude Code builds and uses a plan

Claude Code's process is a loop that its documentation, How Claude Code works, describes in three phases: gather context, take action, verify results. The layer around the model that provides the tools and manages what the model sees is what Anthropic calls the agentic harness.

When you give it a task, the harness loads its system instructions, the tool descriptions, your CLAUDE.md project memory and any auto memory it has saved. The model then explores the repository with search tools and reads the files that look relevant. At each step it can reply in plain text or ask for tools.

When the model chooses a tool, for example to read a file, run a shell command or search the web, the harness executes it, adds the result to the conversation and calls the model again. This loop repeats until the model stops asking for tools and returns its final answer. There is no separate planner: the planning lives in the model's reasoning and its choice of tools.

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To stay on track over long runs, the context window matters most. As it fills, Claude Code compacts automatically, summarizing older parts of the conversation, and you can steer what survives with /compact or a section in CLAUDE.md. It can also keep a written task list, though as of October 2026 the task tools are on by default only for older models; newer models track multi-step work without one unless you opt in.

Subagents add a way to split work. Claude Code can hand a side task, such as searching a large codebase or investigating a bug, to a subagent that runs in its own context window and returns only a summary, which keeps the main conversation clean. Subagents and Parallel Work covers them in depth.

Permission modes set how much the agent may do without asking. Since version 2.1.283, interactive terminal and VS Code sessions start in auto mode, where a second model reviews actions instead of you; in Manual mode Claude Code asks before most edits, shell commands and network access; acceptEdits lets file edits through; and plan mode lets it read and plan but not change anything until you approve, as the permission modes guide explains. Shift+Tab cycles between them. Plan Mode and the Art of Not Letting the Agent Run Wild shows plan mode at work.

How Cursor finds context, then and now

Cursor starts as a fork of VS Code with its AI built in, so its agent can see your open files, your selection and your editor state. For most of its life, Cursor also kept a semantic index of your codebase. It split files into chunks, turned each chunk into an embedding (a list of numbers that captures meaning), and used those embeddings to find code related to your question.

Keeping that index current for a big repository was a neat piece of engineering, which Cursor described in Securely indexing large codebases in January 2026. The editor computed a Merkle tree: a cryptographic hash, a short fingerprint, for every file, and a hash for every folder built from the hashes inside it. Comparing its tree with the server's showed exactly which files had changed, so only those were re-embedded, and embeddings were cached by chunk content. Git stores your project as the same kind of tree of hashes, which is why it can compare two commits so quickly.

In July 2026 Cursor phased that index out. Its staff explained on the Cursor forum that models had become good enough at searching that the semantic path no longer helped much, and that Cursor no longer computes or stores embeddings of your code for search. Today the agent searches with Instant Grep, a fast index that, according to Cursor's search documentation, is built and queried on your machine, and it can send an Explore subagent to run many searches in its own context and report back. In other words, both tools now find code the same basic way: by searching for it.

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Cursor's agent works much like Claude Code's harness. A model receives instructions and tools (file read and edit, search, terminal, web, a browser) and can make tool call after tool call; Cursor's agent documentation says there is no limit on how many it makes in a task. You can pick from many models, including Cursor's own Composer models, which are tuned for tool use, file edits and terminal work inside Cursor; the current one, Composer 2.5, runs in a fast variant by default.

The difference from Claude Code is where the work happens: Cursor's agent runs inside the IDE (integrated development environment). As its agent guide describes, edits are applied as the agent works and appear in a diff view where you can reject anything you don't want, or roll every file back to an earlier checkpoint. Its run modes decide which shell commands, MCP tools and web fetches may run without asking. Cursor can also run many agents in parallel from its Agents Window, locally in isolated Git worktrees or as Cloud Agents, but you still orchestrate them from the editor.

Why each system wins different tasks

Return to Alice's week. Monday's feature work is edit-heavy, and she wants fast feedback and tight coupling to the code. Cursor supplies inline completions and short agent bursts. She stays in the driver's seat, and the agent's search gives the model enough project awareness for relevant suggestions without a long planning phase.

Wednesday's refactor is different. Fifty files need consistent changes, imports must update across the tree, and the whole thing has to compile and pass tests. Claude Code is at home here because it can plan the whole change, apply it file by file, run the test suite and keep going until everything passes. Alice delegates the mechanics and reviews the finished work.

That maps onto a broader trade-off: one long delegated run versus many short, supervised exchanges. Neither is cheaper in every case. A delegated run saves your attention but can wander if the task is vague; short exchanges keep you in control but cost more of your time. We have not found an independent, published benchmark that settles which uses fewer tokens for the same work, so treat any such number you see with care.

Long-lived project knowledge works the same way in both tools now. Neither keeps a semantic memory of your code; each searches when it needs something and reads standing instructions you write down: CLAUDE.md for Claude Code, and rules files (.cursor/rules) or an AGENTS.md file for Cursor, as its rules documentation describes. Within a long session, both rely on the conversation, and Claude Code compacts it as it grows.

Quick Reference

Property	Claude Code	Cursor
Agent loop style	Works through a task until done, within its permission mode	Agent inside the editor; you review each edit as a diff
Finding code	Search tools on demand, plus CLAUDE.md and auto memory	Instant Grep on a local index and an Explore subagent; the semantic index was phased out in July 2026
Long sessions	Automatic compaction, /compact	Conversation plus rules and AGENTS.md
Main interface	Interactive terminal, plus IDE extensions, a desktop app and the web; headless with -p	The Cursor editor (a VS Code fork), plus Cloud Agents
Default permissions	Auto mode in terminal and VS Code since v2.1.283; Manual, acceptEdits and plan mode available	Edits applied and shown in a diff view; run modes (Auto-review, Allowlist, Run Everything) gate commands, MCP tools and fetches
Splitting work	Subagents in their own context windows; background sessions	Subagents; parallel agents in worktrees or the cloud

Test yourself

Alice needs to debug a flaky integration test that only fails in CI. The test touches a service that calls three other microservices. She has log output but must trace the call chain through five repositories' source code and configuration files, then repeatedly run the test with small changes to isolate the race condition.

Answer: This suits Claude Code well. The investigation means reading logs, searching across five repositories, editing configs and rerunning the test many times, which is exactly the long, delegated loop Claude Code is built for. It can send subagents to explore different parts of the call chain, each in its own context window so the main conversation stays focused, and compact the history as it grows. Cursor's agent could do the same work too, and a Cloud Agent could run it unattended, but its strength is supervised editing in the IDE, and this task is mostly searching and rerunning, not editing. Either way, Alice should give the agent the logs, a precise way to reproduce the failure and permission to run the test command, and review the fix as a normal diff.

Frequently Asked Questions

Q: Can I run Claude Code and Cursor on the same project at the same time?

Yes, as long as they do not edit the same files at once; Claude Code even runs inside Cursor through its extension. Claude Code + Your IDE: A Workflow That Compounds shows how to set that up and split the work.

Q: Why does Claude Code sometimes loop without finishing?

The model can misread tool output or lose sight of the goal, especially after compaction. It might keep editing and rerunning tests, thinking the requirement is not met. A nudge in the terminal that states the expected outcome, such as "stop when these three tests pass", usually breaks the cycle.

Q: Does Cursor still index my codebase?

Not with embeddings, as of October 2026. Cursor phased out its semantic index in July 2026. Its agent searches with Instant Grep, whose index is built on your machine, and Cursor says it does not upload file paths or code to build a search index. The files the agent reads are still sent to the model in requests.

Q: Does Claude Code respect my .gitignore?

Partly. The @ file picker leaves out files that match your .gitignore by default (the respectGitignore setting), but .gitignore is not a security boundary: Claude can still read an ignored file if it decides to. To keep it away from a file such as .env, add a deny rule like Read(./.env) in .claude/settings.json, as the permissions documentation shows. CLAUDE.md is for project context and coding conventions; permission rules go in settings files.

Q: When should I use Cursor's parallel agents instead of Claude Code's subagents?

They solve different problems. Cursor's parallel agents are separate runs, each in its own Git worktree or cloud machine, which suits trying two approaches side by side or splitting independent tasks such as frontend and backend work. Claude Code's subagents work inside one session: each gets a fresh context window, does a piece of research or review, and hands a short summary back to the main conversation, which keeps it focused on one coherent change. Claude Code can also run separate sessions in parallel when you need that.

Next: Trusting AI-Written Code: Tests, Reviews, Guardrails. It covers how to check what an agent wrote before it reaches production: tests first, static analysis, reviews and guardrails.

If you want this kind of breakdown every week, how real systems actually work under the hood, subscribe to Internals Decoded at internalsdecoded.com.

Sources

• How Claude Code works
• Claude Code: Tools reference
• Claude Code: Subagents
• Claude Code: Choose a permission mode
• Claude Code: Configure permissions
• Securely indexing large codebases
• Cursor forum: removing the codebase indexing settings
• Cursor: Search (Instant Grep)
• Cursor: Agent overview
• Cursor: Agent (help)
• Cursor: Composer 2.5
• Cursor: Agents Window
• Cursor: Rules