Comparison

sgrep fits into the ecosystem of code search tools. This page compares it to popular alternatives and explains when to use each.

Feature Comparison Matrix

Feature	sgrep	grep	ripgrep	ag	ast-grep	CodeQL
Search Type	Semantic	Text	Text	Text	AST	Semantic
Offline	Yes	Yes	Yes	Yes	Yes	Partial
Zero Setup	Yes	Yes	Yes	Yes	No	No
Binary Size	~8MB	~300KB	~1MB	~500KB	~10MB	SDK
Speed	Fast	Fast	Fastest	Fast	Medium	Slow
Language Agnostic	Yes	Yes	Yes	Yes	No*	No
Cross-language	Yes	Yes	Yes	Yes	Yes	No
Understanding Meaning	Yes	No	No	No	Partial	Yes
JSON Output	Yes	No	No	No	Yes	Yes
API Calls	None	None	None	None	None	Optional

*ast-grep supports many languages but requires language-specific grammars.

Tool Deep Dives

grep

The original Unix text search tool.

Strengths:

Ubiquitous (installed everywhere)
Predictable exact matching
Regex support
Zero learning curve

Weaknesses:

No semantic understanding
Slow on large codebases
Limited to literal text patterns

Use when: You need exact text matching, working on unfamiliar systems, or searching non-code files.

# Find exact string
grep "TODO" src/

# grep can't do this
sgrep "future improvements needed"  # Finds "TODO", "FIXME", etc.

ripgrep (rg)

Modern, Rust-based grep replacement.

Strengths:

Extremely fast (parallel search)
Smart filtering (respects .gitignore)
Great defaults
PCRE regex support

Weaknesses:

Still text-based (no semantics)
Can’t understand synonyms or related concepts

Use when: You need fast literal search with great UX.

# ripgrep is faster for exact text
rg "Error" src/

# But misses related concepts
sgrep "error handling"  # Finds "Error", "Exception", "fail", etc.

the_silver_searcher (ag)

Early contender in fast grep tools.

Strengths:

Faster than grep
Respects .gitignore
Clean output format

Weaknesses:

Less actively maintained than ripgrep
No semantic search

Use when: You have existing ag scripts or prefer its output format.

ast-grep

AST-based code search with structural matching.

Strengths:

Understands code structure
Pattern matching on AST nodes
Language-specific queries
Refactoring support

Weaknesses:

Requires language parsers
Not truly semantic (matches structure, not meaning)
Heavier setup

Use when: You need structural code matching or automated refactoring.

# ast-grep matches structure
sg -p 'console.log($_)' src/

# sgrep matches meaning
sgrep "logging statement"  # Finds console.log, logger.info, print, etc.

CodeQL

Powerful semantic analysis framework from GitHub.

Strengths:

Deep semantic understanding
Taint analysis
Vulnerability detection
Extensive query library
Data flow analysis

Weaknesses:

Heavy setup (database generation)
Slow (not for quick search)
Language-specific databases
Not ideal for ad-hoc queries

Use when: You need deep security analysis or complex vulnerability scanning.

# CodeQL for security analysis
codeql database analyze --run=security-queries

# sgrep for quick semantic search
sgrep "security vulnerability" src/

When to Use sgrep

sgrep excels when:

You don’t know exact terms

# Find database code without knowing ORM name
sgrep "database query" src/

Cross-language search

# Find auth code across Python, Rust, and TypeScript
sgrep "authentication" src/

Exploring unfamiliar codebases

# Understand what modules exist
sgrep "data processing" src/

Code review automation

# Check for similar patterns
sgrep --json "potential bug" src/ | jq .

Quick semantic queries

# Faster than setting up CodeQL
sgrep "memory leak pattern" src/

When to use other tools:

Task	Use This Tool
Exact string search	grep / ripgrep
Regex matching	ripgrep
Structural refactoring	ast-grep
Security audit	CodeQL
Performance profiling	ripgrep (faster)
Semantic exploration	sgrep

Hybrid Workflow

The best approach often combines tools:

# Fast pre-filter with ripgrep, then semantic
rg "error" src/ -l | xargs sgrep "error handling"

# Use sgrep to find patterns, CodeQL for deep analysis
sgrep "sql injection" src/  # quick scan
codeql analyze src/  # thorough audit

Key Differentiators

Meaning Over Text

Traditional tools search for what you type. sgrep searches for what you mean.

Query: "database connection"

grep finds:
- "database connection" (exact match only)

sgrep finds:
- "db.connect()"
- "open_database()"
- "ConnectionPool::new()"
- "psql://localhost"
- "MongoClient()"

Language Independence

Learn one query language for all programming languages:

# Works for Python, Rust, Go, JavaScript...
sgrep "async function" src/

Offline First

No API calls, no rate limits, no data leaving your machine:

Model: ~7.5MB (downloaded once, cached locally)
Inference: Runs on your CPU
Privacy: Code never leaves your computer

Developer Experience

# Familiar grep-like interface
sgrep "pattern" path/

# JSON for pipelines
sgrep --json "pattern" | jq .

# Hybrid mode (BM25 + semantic)
sgrep --hybrid "pattern" path/

Benchmarks

On a codebase of 100K lines of code:

Operation	sgrep	ripgrep	CodeQL
First run	15s	0.5s	5min
Subsequent (cached)	10ms	0.5s	30s
”find auth code”	Works	Misses many	Works
”function_name”	Works	Works	Works

sgrep’s strength is in semantic queries, not raw text search speed.

Summary

grep: The universal baseline
ripgrep: Fastest literal search
ast-grep: Structural code matching
CodeQL: Deep security analysis
sgrep: Fast semantic search

Use sgrep when you need to search by meaning, not just text. Combine with other tools for the best workflow.