grep, ripgrep, and AI-Powered Text Search

TL;DR

Tool	Use Case	Speed	AI Agent Compatibility
grep	Minimal environments, simple pipelines	Baseline	Low (noisy output)
ripgrep (rg)	Daily development, large codebases	10x faster	High (all agents use it)
ast-grep (sg)	Structural code search, refactoring	Fast	MCP integration available
mgrep	Semantic search, natural language queries	Moderate	2x less token consumption

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I will be discussing a command that holds a significant place in your toolkit and command-line operations: grep. The first version of this article was published in 2019. Since then, the developer tools ecosystem, particularly AI-assisted development workflows, has undergone significant changes. In this update, I will cover everything from grep fundamentals to modern alternatives like ripgrep, how AI coding agents use these tools, and next-generation semantic search solutions.

grep Fundamentals

grep, the Global Regular Expression Printer, allows you to select and mark lines from a text corpus based on a specified pattern. The provided pattern is processed within the specified path, and matching results are listed. It can be used independently or combined with pipes (|) to enhance its capabilities.

Its most basic usage:

grep '[search-text]' [file-path]

Commonly Used Parameters

Parameter	Description	Example
-i	Case-insensitive search	grep -i 'error' log.txt
-r	Recursive search in subdirectories	grep -r 'TODO' src/
-n	Show line numbers	grep -n 'function' app.js
-v	Show non-matching lines (exclude)	grep -v 'debug' log.txt
-l	List only file names	grep -l 'import' *.ts
-c	Show match count	grep -c 'error' log.txt
-w	Whole word matching	grep -w 'return true' *.php
-o	Show only matching part	grep -o 'v[0-9]\+' changelog
-A N	N lines after match	grep -A 3 'error' log.txt
-B N	N lines before match	grep -B 2 'error' log.txt
-C N	N lines around match	grep -C 5 'error' log.txt
-E	Extended regex	grep -E 'err(or|eur)' log.txt
-F	Fixed string (no regex)	grep -F '$variable' code.sh

Pipeline Usage

grep’s power emerges when chained with other commands:

# Search for nginx in running processes
ps aux | grep 'nginx'

# View 500 errors in log file page by page
grep '500' /var/log/access.log | more

# Check file permissions for specific extensions
ls -l ~/var/www/html/*.jpg | grep rwxrwxrwx

To search for multiple words:

grep -i 'spam\|hashes' access_log.txt      # method 1
grep -iE 'spam|hashes' access_log.txt       # method 2
grep -i -e 'spam' -e 'hashes' access_log.txt # method 3

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Modern Alternatives: Why New Tools Were Needed

grep has served as one of the cornerstones of Unix philosophy for decades. However, today’s massive codebases, multi-core processors, and AI-assisted development workflows have brought new requirements:

• Performance: grep uses a single core. This becomes slow in projects with hundreds of thousands of files.
• Smart defaults: Directories like node_modules, dist, .git need to be manually excluded.
• Unicode and modern regex: Multi-language support and advanced regex requirements in modern codebases are increasing.

ripgrep (rg)

ripgrep is a search tool developed in Rust by Andrew Gallant, positioned as a modern alternative to grep¹. With over 59,700 stars on GitHub, ripgrep continues its active development with the latest version 15.1.0 (October 2025).

Key features that set ripgrep apart:

• Multi-core parallel search: Search operations are automatically distributed across CPU cores
• Automatic .gitignore support: Reads .gitignore and .ignore files to skip directories like node_modules and build by default
• Advanced regex engine: Finite automaton-based, SIMD-optimized Rust regex engine
• Unicode support: Full Unicode character class support
• Jujutsu VCS recognition: Jujutsu version control system repository recognition support since version 15.0.0

Performance Comparison

Benchmark on Linux kernel source code (4,640 directories, 178 .gitignore files)²:

Operation	GNU grep	ripgrep	Difference
Simple pattern search	~0.67s	~0.06s	11x faster
Line-numbered search (-n)	9.48s	1.66s	5.7x faster

Basic Usage

ripgrep’s command-line interface feels familiar to grep users:

# Simple search (recursive and .gitignore-aware by default)
rg 'TODO'

# File type filter
rg --type ts 'interface'
rg --glob '*.tsx' 'useState'

# Fixed string search (no regex, faster)
rg -F '$variable'

# Search with context
rg -C 3 'error'

# File names only
rg -l 'import.*lodash'

# Multiple patterns
rg -e 'TODO' -e 'FIXME' -e 'HACK'

# JSON output format (for programmatic use)
rg --json 'pattern'

Other Alternatives

Tool	Language	Latest Version	GitHub Stars	Status
ripgrep (rg)	Rust	15.1.0 (October 2025)	59,700+	Active
ack	Perl	3.9.0 (May 2025)	799	Active
ag (Silver Searcher)	C	2.2.0 (August 2018)	27,200+	Unmaintained
ugrep	C++	7.5 (2025)	3,000+	Active
GNU grep	C	3.12 (April 2025)	N/A	Active (slow pace)

ack, a Perl-based search tool, continues to be actively developed. With version 3.9.0, it offers Boolean search operators such as --and, --or, --not; a feature not directly available in grep or ripgrep³.

ag (The Silver Searcher) was an important stepping stone from grep to ripgrep. However, no new version has been released since 2018, and it is unmaintained.

ugrep stands out as an alternative fully compatible with GNU grep. It offers unique features such as an interactive TUI interface, searching within compressed files (gz, bz2, xz, zstd) and archives (zip, 7z, tar), and searching in PDF and Word documents⁴.

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AI Agents and Text Search

The proliferation of AI-powered coding tools has created a new layer in the text search ecosystem. The first step for an AI agent to “understand” a codebase is finding the relevant files and code snippets. Text search tools are vital for answering questions like “Where is this function defined?” or “Which file uses this API key?”

Which Tool Do Agents Use?

All major AI coding agents use ripgrep as their internal search engine:

Agent	Search Tool	Source
Claude Code	ripgrep (Grep tool)	Confirmed in GitHub issue #735
GitHub Copilot CLI	ripgrep (included November 2025)	GitHub Blog6
OpenAI Codex	ripgrep (primary), grep (fallback)	GitHub repository7
Aider	grep-ast (tree-sitter powered)	GitHub repository8

Claude Code’s Grep tool uses ripgrep in three different output modes: content (matching lines), files_with_matches (file paths), and count (match counts). These modes control the output volume based on the agent’s needs.

Challenges AI Agents Face

1. Noisy Results

# Problematic: Searching the entire project
grep -r 'config' .
# Thousands of irrelevant results in node_modules, dist, .next

This approach pollutes the agent’s context and rapidly consumes token limits. ripgrep’s .gitignore support largely solves this problem, though there are still cases where it falls short.

2. Lack of Context

grep only returns the matching line. Even with surrounding lines via the -C parameter, it may not be sufficient to understand the entire function or class. Aider’s grep-ast tool uses the tree-sitter parser to show the matching line along with the function, class, or method it belongs to⁸.

3. Regex Errors

AI agents can sometimes generate incorrect regex patterns. Inconsistencies are particularly observed with escaping special characters like ., *, (, ). Therefore, the -F (fixed string) parameter should be preferred when exact string matching is needed:

# Fixed string instead of potentially incorrect regex
rg -F 'interface{}' --type go

4. Token Consumption

As noted in discussions in the OpenAI Codex repository, “grep or filename heuristics fall short in multilingual repositories, with renamed identifiers, or when concepts are expressed differently from the query”⁷. This drives the need for semantic search tools.

Solutions and Best Practices

Narrowing the search: Constraining to specific directories and file types instead of searching the entire project:

# Instead of searching the entire project
rg 'handleSubmit' src/components/ --glob '*.tsx'

Fixed string search: Using -F when regex is not required:

rg -F 'process.env.DATABASE_URL'

Choosing output mode: Proceeding in two stages, first file list, then content search:

# First, which files contain it?
rg -l 'useAuth'
# Then search in detail within those files
rg -C 3 'useAuth' src/hooks/useAuth.ts

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Next-Generation Tools: Beyond Text Search

In the 2025-2026 period, the text search ecosystem is evolving into a three-layer structure:

Layer 1: Exact Text Matching (grep, ripgrep)

Fast, reliable classical text search that produces no false positives. Still the best choice for searching a known string or regex pattern.

Layer 2: Structural Code Search (ast-grep)

ast-grep (sg) performs structural search on the Abstract Syntax Tree instead of text-based search⁹. Using the tree-sitter parser to understand code structure, it can run queries beyond text matching:

# Find console.log calls (only function calls, excluding those in strings)
sg -p 'console.log($$$)' --lang typescript

# Find async functions without try-catch blocks
sg -p 'async function $NAME($$$) { $$$ }' --lang javascript

ast-grep also provides an MCP (Model Context Protocol) server for AI agent integration. This enables tools like Claude Code or Cursor to perform structural code searches.

Layer 3: Semantic Search (mgrep, grepai)

mgrep is a semantic search tool developed by Mixedbread AI that works with AI embeddings¹⁰. It can search code, text, and even PDF files using natural language queries:

# Natural language search
mgrep "user authentication flow"

# Auto-index git repository
mgrep watch

In benchmarks comparing mgrep with Claude Code integration, mgrep-based workflows reportedly consume approximately 2x fewer tokens than grep-based workflows¹¹.

grepai is a fully local semantic code search tool that uses vector embeddings. It offers features like natural language queries, conceptual similarity search, and call graph tracing. It provides AI agent integration through its built-in MCP server¹².

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Practical Guide: Which Tool for Which Scenario?

Scenario	Recommended Tool	Why
Minimal server, Docker image	grep	No additional installation required
Simple pipeline filtering	grep	ps aux | grep nginx
Daily development search	rg	Speed, .gitignore support
Large codebase	rg	Parallel search, smart filtering
AI agent command	rg	All agents support it
Code structure search	ast-grep	AST-based structural queries
Refactoring	ast-grep	Structural find-and-replace
Conceptual search	mgrep / grepai	Natural language queries
Compressed file search	ugrep	zip, gz, PDF support
Boolean combinations	ack	--and, --or, --not

Installation

# ripgrep
brew install ripgrep        # macOS
apt install ripgrep          # Debian/Ubuntu
choco install ripgrep        # Windows

# ast-grep
npm install -g @ast-grep/cli
brew install ast-grep

# mgrep
pip install mgrep

# ugrep
brew install ugrep

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Conclusion

grep maintains its value as one of the cornerstones of Unix philosophy. However, in modern development workflows, ripgrep’s speed and smart defaults make it a better choice in nearly every scenario. With the rise of AI coding agents, the efficient use of text search tools has become a critical skill for both humans and AI agents alike.

The text search ecosystem is evolving into a three-layer structure: exact matching (ripgrep), structural search (ast-grep), and semantic search (mgrep). Each of these layers addresses a different need and complements one another.

While researching this article, I took a closer look at the problems AI agents face during search operations: false negatives in built-in grep tools, noisy results that burn through the context window, hallucinated file paths. Each of these issues is frustrating on its own, but combined they create a domino effect that directly impacts the quality of code an agent produces. In the next article, I covered these problems in detail and shared a local semantic code search MCP server I built as a solution.

Footnotes

1. ripgrep GitHub Repository ↩
2. ripgrep is faster than grep, ag, git grep, ucg, pt, sift ↩
3. ack: Beyond grep ↩
4. ugrep: Ultra fast grep ↩
5. Claude Code - ripgrep confirmation ↩
6. GitHub Copilot CLI Changelog, November 2025 ↩
7. OpenAI Codex - Semantic Search Proposal ↩ ↩²
8. Aider grep-ast ↩ ↩²
9. ast-grep: Structural Code Search ↩
10. mgrep: Semantic grep by Mixedbread AI ↩
11. Boosting Claude: Faster Code Analysis with mgrep ↩
12. grepai: Local Semantic Code Search ↩