Cursor

Cursor: Context Window Too Large Error — How to Fix It

The 'context window too large' error in Cursor appears when the total tokens sent to the underlying AI model exceed its maximum limit. This commonly happens in Composer when you reference multiple large files or work inside a monorepo with deeply nested dependencies. Developers at all experience levels encounter this, especially on projects with large generated files or bundled assets.

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Why does this error happen?

Cursor's Composer and Chat features pass file contents, conversation history, and system instructions together as a single token payload to the AI model. Every model has a hard upper limit on how many tokens it can process in one request — for example, GPT-4 supports up to 128k tokens and Claude models vary by version. When the combined size of all referenced files, open tabs, and prior conversation turns exceeds that limit, the API rejects the request entirely and Cursor surfaces the context window error. Large files like lockfiles, compiled bundles, auto-generated code, or node_modules accidentally pulled into context are the most frequent culprits.

How to fix it

1

Reference Only Specific Files with @file

Instead of letting Cursor auto-include open tabs or entire folders, use the @file mention to pin only the exact files relevant to your current task. Type @file followed by the file path directly in the Composer prompt to give the model focused, minimal context. This alone can reduce token usage by 60–80% on large projects.

2

Break Large Files into Smaller Modules

If a single file is causing the overflow, split it into logically separate modules — for example, separate utility functions, constants, and types into their own files. Smaller, purpose-scoped files let you reference only the slice of code the AI actually needs. This also improves long-term maintainability and makes future Cursor sessions faster.

3

Exclude Irrelevant Files with .cursorignore

Create a .cursorignore file in your project root to block directories and file patterns from ever being indexed or auto-included by Cursor. Add entries for node_modules, build outputs, lockfiles, and any large generated assets. This prevents those files from silently inflating context even when you use broad references like @codebase.

4

Manually Summarize Context Before Prompting

When you need to discuss a large file or a long conversation history, write a brief plain-language summary of the relevant state and paste it at the top of your prompt instead of referencing the full file. For example, describe the current function signatures and data shapes rather than including hundreds of lines of implementation. This gives the model the information it needs at a fraction of the token cost.

Code example

// .cursorignore example
node_modules/
.next/
dist/
*.lock

Pro tip

Audit your Cursor context regularly by checking which files are highlighted in the Composer sidebar before submitting a prompt — deselect any file not directly relevant to the task to stay well under the token limit.

Frequently asked questions

Does upgrading to Cursor Pro increase the context window size?
Cursor Pro unlocks access to models with larger context windows, such as Claude 3.5 Sonnet and GPT-4o, which can handle significantly more tokens per request. However, even with Pro, extremely large codebases will still require good file hygiene and selective referencing to avoid hitting limits.
Will Cursor automatically trim context if it gets too large?
Cursor does not silently truncate context — it surfaces an error rather than sending a partial or degraded payload to the model. This means you need to manually reduce context size before retrying the prompt.
Does .cursorignore work the same way as .gitignore?
Yes, .cursorignore follows the same glob pattern syntax as .gitignore, making it familiar and easy to configure. Patterns added there tell Cursor's indexer to skip those files entirely, so they never appear in @codebase results or automatic context inclusion.
Can I see how many tokens my current context is using?
Cursor does not currently display a live token counter in the UI, so you need to estimate based on file sizes and conversation length. A rough rule of thumb is that 1 token equals approximately 4 characters of English text or code.

Upgrade to Cursor Pro for larger context window models and faster completions.

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