Give Your Coding Agent ripgrep, Not grep

grep has been a reliable text-search tool for decades, but modern codebases and automation workflows expose its limits. Coding agents, in particular, need fast, recursive, noise-free searches, something traditional grep often struggles with. Tools like ripgrep (rg) solve these problems with smarter defaults and developer-centric behavior. It’s why many AI assistants, including Anthropic’s Claude, explicitly prefer ripgrep and even warn against using grep. Here’s why rg is the better choice for code search.

Grep’s Limitations in Code Search

Grep is extremely powerful, but using it for searching large codebases has a few well-known drawbacks:

1. Recursion Isn’t Automatic 

By default, grep only searches the current file or directory unless you explicitly add -r or -R. Forgetting this flag is common and leads to incomplete results. To make matters worse, the syntax and behavior differ between GNU grep on Linux and BSD grep on macOS, adding unnecessary friction.

2. It Searches Everything, Including Junk

grep doesn’t respect .gitignore, so it happily scans node_modules, vendor/, binaries, build output, cache folders, and other paths that developers never want in results. This creates noise that a coding agent then has to parse and filter manually.

3. Case Sensitivity Causes Missed Matches 

grep defaults to case-sensitive search. Searching for "schema" won’t catch "Schema" or "SCHEMA" unless you remember to add -i. Humans forget this; coding agents forget this even more. The result is inconsistent or incomplete search output.

4. Common Searches Require Overly Complex Commands 

To ignore paths or filter by language, grep often needs long pipelines using find, xargs, or multiple --include and --exclude patterns. This is powerful but brittle—one misplaced quote can break the entire command, and coding agents are especially prone to generating these mistakes.

Example: Searching Markdown Files for “schema” 

A typical grep command looks like this:

$ grep -R -l --ignore-case --include="*.md" "schema" .

It works, but it still returns results from ignored directories like node_modules unless you add even more excludes.

Compare that with ripgrep:

$ rg -l -i -t md "schema"

This single rg command accomplishes the same goal much more cleanly. It searches recursively by default, ignores any file patterns from your .gitignore automatically, treats “schema” case-insensitively (-i), and restricts to Markdown files (-t md). The output will only list relevant project files, not third-party dependencies or ignored directories  . In our example, rg might directly return results like:

docs/data-guidelines.md  

docs/contributing.md  

schemas/compat-data-schema.md  

README.md  

Whereas grep’s output included a lot of noise from node_modules and other ignored folders that ripgrep skips by default. Ripgrep gives us exactly what we want, with far less effort  .

How ripgrep Improves on grep

Ripgrep (rg) was created to address these pain points and optimize code searching. It provides several key advantages that make it ideal for coding agents and developers alike:

Recursive Search by Default

You don’t need to add a -R flag – rg will automatically search all subdirectories. It assumes you want to search the project tree, which is almost always the case for code. This saves time and avoids the “oops, I forgot to use recursive mode” problem .

Respects 

.gitignore and Hidden Files: Out of the box, ripgrep honors your repository’s ignore settings. Files and directories listed in .gitignore (or in .ignore/.rgignore files) won’t appear in results . It also skips hidden files and binary files unless you explicitly ask for them. This means when your agent searches, it will only see relevant code, not dependencies or clutter – with no extra flags needed . (You can override this with -u/--no-ignore if needed, but the default behavior is usually what you want.)

Smart Case & Configurable Defaults

While ripgrep, like grep, is case-sensitive by default, it supports “smart case” searching and easy configuration. The --smart-case option tells rg to do a case-insensitive match when your pattern is all lowercase, but switch to case-sensitive if you include any capital letters. In practice, this means you rarely have to think about adding -i – you just type the search term in lowercase and it “Just Works.” You can even make --smart-case the default by putting it in an ~/.ripgreprc config file . This flexibility helps avoid missed matches due to case, without needing complex regex. (By comparison, grep has no built-in smart-case; you must specify -i every time or use workarounds.)

File Type Filters

Ripgrep lets you search by programming language or file type with a simple flag. For example, rg -tjavascript Promise will search only JavaScript files, and rg -Tsql TODO would exclude SQL files from the search. It knows about many common file extensions and languages out of the box, and you can define your own. Grep can filter by filename pattern (--include="*.js"), but rg’s built-in types are quicker and can be more expressive . This is very handy for agents that might be asked to “find a function in the Python code” or similar – one flag gets the job done.

High Performance

Perhaps ripgrep’s biggest claim to fame is its speed. It’s extremely fast, in many cases an order of magnitude faster than grep, especially on large codebases. Written in Rust, rg is highly optimized and runs searches in parallel across your CPU cores by default. It also has intelligent search algorithms that handle regex efficiently and won’t bog down on large files or binary content. The result is that coding agents using rg can search huge repositories with very low latency. For example, in one benchmark searching the Linux kernel source for a pattern, ripgrep found all matches in ~0.06 seconds, whereas GNU grep took ~0.67 seconds, over 10x slower.

Figure: Search performance comparison on a large codebase (lower is better). 

Similar Syntax & Extra Features

Ripgrep’s command-line usage is deliberately very similar to grep, so most of your knowledge carries over. Common options like -i (ignore case), -n (show line numbers), -v (invert match) work as expected. But rg also adds some quality-of-life features: nicely colored highlights in the output, context line options that mirror grep’s but with easier syntax, and even a built-in replace (-r) functionality to quickly refactor matches. It fully supports Unicode (unlike some old grep versions that could stumble without locale tricks), and can even search compressed files or different encodings if needed. In short, ripgrep can do almost everything grep can, usually faster, and often more conveniently.

Example: Cleaner Search Results with Ripgrep

To concretely see the difference, consider searching a project for the keyword “API_KEY”. Using grep, you might get something like this:

$ grep -R "API_KEY" .

Binary file .envrc matches  

config.py:12:API_KEY = os.environ.get("DEV_API_KEY")  

venv/lib/python3.9/site-packages/thirdparty/config.py:8:    API_KEY = "12345"  

Even with correct flags, grep’s output included a “Binary file ... matches” line (perhaps a compiled file contained the byte pattern) and found matches in a virtual environment folder (venv/...) which is not part of our source code. These are probably not what we wanted. The actual relevant result here is config.py:12:API_KEY = .... We could refine grep with more excludes, but it’s extra work.

Now see what ripgrep would return by default for the same search:

Output from rg "API_KEY" on a project directory. Ripgrep skips over binary files and irrelevant folders, showing only the meaningful matches with filenames and line numbers.

In the ripgrep output above, notice how it cleanly shows the file and line where API_KEY appears, without the noise. The matches are highlighted (in color, if you view it in a terminal), and we don’t see any mention of binary files or irrelevant paths, those were automatically filtered out. This focused output makes it easier for a developer or an AI agent to quickly identify the usage of API_KEY in the code. The agent can parse the filename and line (e.g., config.py:12) directly. In fact, many tools leverage ripgrep’s output format to integrate search results in editors; for example, VS Code’s “Go to Definition” or global search will call rg and then display the results in a clickable list.

Another benefit seen here is performance in practice. 

If .venv/ or other large directories are ignored, ripgrep is scanning far fewer files than grep was. In our hypothetical example, grep searched thousands of files (including heavy binary wheels in .venv), while rg might have searched only dozens of source files. This not only saves time but also reduces CPU usage – important when your agent might be running on a limited cloud instance or a CI runner where efficiency matters.

When to Fall Back to Grep

With all its advantages, you might wonder if there’s ever a reason not to use ripgrep. The truth is, for most development searches ripgrep (and similar tools) are superior, but there are a couple of caveats to be aware of:

Availability

Grep is available on essentially every Unix system out-of-the-box, whereas ripgrep usually needs to be installed separately. If you’re on a system where you cannot install new tools (say, a minimal container or a restricted server), your agent might have no choice but to use grep. In such cases, grep’s ubiquity is its strength. Ensure your agent checks for rg and falls back gracefully if it’s not found. Some programming editors do exactly this: use rg if present, else grep as a last resort.

Compatibility and Edge Cases

As a newer tool, ripgrep doesn’t strictly conform to POSIX grep standards. If you have a very specific use-case relying on grep’s quirks or you need bit-for-bit identical behavior to grep for some reason, rg might not be a drop-in replacement  . For example, if an old script expects the exact grep output format or error codes, using ripgrep could break that. Ripgrep’s author openly acknowledges it’s not meant to replace grep in every single scenario . There are also a few esoteric features that traditional grep or other tools have (like certain obscure regex extensions or the ability to stream input from legacy systems) that ripgrep might not cover. However, these are unlikely to affect typical code searching tasks.

Trust in Results

In extremely rare cases, performance might be comparable or even favor grep for certain patterns on certain data (especially if searching a single large file that fits in disk cache – GNU grep is highly optimized in C for linear scanning). But those scenarios are not common in everyday development work, and ripgrep is continuously optimized to handle even those as much as possible. For practically all searches a developer or AI agent would do, ripgrep finds what you need and does it fast.

In summary, grep isn’t going away – it’s the universal tool that will always be there when you need it. But whenever you do have access to better tools, there’s little reason to stick with vanilla grep for code searching. As one technical writer quipped, these modern grep alternatives “make sense in lots of situations, but not every situation. If you need the ubiquity and compatibility of grep, then accept no substitutes. For everything else, ripgrep and friends are there for you.” 

Conclusion

For developers and AI coding agents alike, using rg (ripgrep) instead of grep for searching code is a smart upgrade. Ripgrep provides sensible defaults (recursive search, respecting ignores), faster performance, and more convenient features, all of which translate to time saved and fewer mistakes. A coding agent that leverages ripgrep will require fewer complex flags in its commands and will get more relevant results with less filtering, allowing it to focus on the core logic rather than parsing out noise. Developers have already embraced rg in their own workflows (it’s hard to go back once you’ve seen how fast and easy it is), and it makes just as much sense to equip our automated tools with that same advantage.

Next time you’re configuring an environment for your AI code assistant or writing a script that scans through code, make sure to give it the power of ripgrep. You’ll likely find that your searches become “fast and effortless” rather than “slow and frustrating” . In a world where codebases are growing and speed matters, choosing the right tool for the job is key, and ripgrep is built to search code like a champion. Happy coding, and happy searching!