Question 1

How to install fff.nvim with lazy.nvim?

Accepted Answer

Add the plugin to your lazy.nvim config with a build function to download or build the binary, and set keymaps like 'ff' for find_files. The README provides a complete example with opts for debugging and layout.

Question 2

FFF.nvim vs telescope.nvim: which is better?

Accepted Answer

FFF focuses on raw speed and memory-augmented search for AI agents and large repos, while telescope.nvim offers a wider plugin ecosystem and more general-purpose picking. Choose FFF if performance and AI integration are critical.

Question 3

Does fff.nvim work with AI agents like Claude Code?

Accepted Answer

Yes, it includes an MCP integration with a bash script for installation, and charts show it reduces token usage by providing memory-enhanced file search, as noted in the README's AI section.

Question 4

How to filter search results by git status in fff.nvim?

Accepted Answer

Use constraints like 'git:modified' in the search query, or enable git status text coloring in setup. The plugin highlights status in the sign column by default and allows negation with '!'.

Question 5

Can fff.nvim handle very large repositories?

Accepted Answer

Yes, it's optimized for large codebases with features like time-budgeted grep and lazy syncing, demonstrated on the Linux kernel with 100k files, though it may require tuning max_threads and file size limits.

Question 6

What is the memory-augmented search in fff.nvim?

Accepted Answer

It stores file open frecency and query combos in databases to boost scores for frequently accessed files, helping both AI agents and users find relevant results faster based on past behavior.

fff.nvim

What is fff.nvim?

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