| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Detects misspelled words in code by splitting camelCase identifiers into constituent words and matching each against language-specific dictionaries, enabling detection of typos in variable names like 'getUserNme' without false positives on legitimate camelCase patterns. Uses offline dictionary matching rather than ML models, processing the current file in real-time as the developer types.
Unique: Implements camelCase-aware word splitting for identifier spell checking, treating 'getUserNme' as three words ('get', 'User', 'Nme') rather than a single unknown token, enabling detection of typos in naming conventions common to programming languages without flagging legitimate camelCase patterns as errors
vs alternatives: Outperforms generic spell checkers by understanding code-specific naming conventions (camelCase), whereas tools like Grammarly or native OS spell checkers would flag all camelCase identifiers as misspellings
Provides spell checking in 40+ languages through a modular architecture where the core extension includes English (US) by default, and additional language dictionaries are installed as separate VS Code extensions. Each language add-on extends the base spell checker with language-specific dictionaries and rules, allowing developers to switch languages via the `cSpell.language` configuration setting.
Unique: Uses a modular extension architecture where language support is decoupled from the core spell checker, allowing users to install only the languages they need rather than bundling all dictionaries, reducing extension size and improving performance for monolingual projects
vs alternatives: More flexible than monolithic spell checkers that bundle all languages, but requires more manual setup than tools like Grammarly that auto-detect language context
Allows developers to define custom dictionaries at the project, workspace, or user level to whitelist domain-specific terms, acronyms, brand names, and technical jargon that would otherwise be flagged as misspellings. Custom dictionaries are stored in configuration files and merged with the base language dictionaries during spell checking, enabling teams to maintain a shared vocabulary of approved terms.
Unique: Enables project-level vocabulary management through configuration-driven custom dictionaries, allowing teams to version-control approved terminology alongside code rather than relying on individual spell checker settings or external glossaries
vs alternatives: More flexible than fixed dictionaries but less sophisticated than ML-based spell checkers that can infer context and learn domain terminology automatically
Integrates with VS Code's Quick Fix UI (lightbulb icon) to display spelling correction suggestions directly in the editor. When a misspelled word is detected, developers can position their cursor on the underlined word and press Ctrl+. (or Cmd+. on Mac) to open a dropdown menu of suggested corrections, then click to apply the fix with a single action. This integrates into the standard VS Code diagnostics and code action pipeline.
Unique: Leverages VS Code's native Quick Fix and code action infrastructure to provide spell checking corrections as first-class editor actions, integrating seamlessly with other linters and code actions rather than requiring a separate UI panel or command
vs alternatives: More integrated into the editor workflow than external spell checkers, but less powerful than IDE-native spell checkers that can batch-correct multiple errors or provide context-aware suggestions
Continuously monitors the currently open file in VS Code and displays misspelled words as inline squiggly underlines (red wavy lines) in real-time as the developer types. Diagnostics are published to VS Code's diagnostics pipeline and appear in the Problems panel, allowing developers to see all spelling errors in the current file at a glance. Spell checking runs asynchronously to avoid blocking the editor.
Unique: Implements asynchronous real-time spell checking that publishes diagnostics to VS Code's standard diagnostics pipeline, allowing spell checking to coexist with other linters and type checkers without blocking editor responsiveness
vs alternatives: More responsive than batch spell checking tools, but less comprehensive than project-wide spell checkers that can identify errors across multiple files and provide unified reporting
Applies spell checking selectively to different code scopes: code comments (both single-line and multi-line), string literals, and identifiers (variable/function names). The spell checker distinguishes between these scopes and applies appropriate rules — for example, camelCase splitting is applied to identifiers but not to comments. This scope awareness reduces false positives by avoiding spell checking in contexts where misspellings are intentional or irrelevant.
Unique: Implements scope-aware spell checking that treats comments, strings, and identifiers as distinct contexts with different rules (e.g., camelCase splitting for identifiers but not comments), reducing false positives compared to naive spell checkers that treat all text equally
vs alternatives: More sophisticated than simple regex-based spell checkers that flag all unknown words, but less powerful than AST-based approaches that could provide even more precise scope detection
Integrates spell checker configuration into VS Code's standard settings system using the `cSpell.*` configuration namespace. Developers can configure spell checking behavior via VS Code's Settings UI, `settings.json` file, or workspace-level configuration files. Configuration options include language selection, custom dictionaries, and other spell checker parameters, allowing per-user, per-workspace, and per-project customization.
Unique: Leverages VS Code's native settings system and configuration hierarchy (user, workspace, folder) to provide multi-level spell checking configuration, allowing teams to define shared rules in workspace settings while allowing individual developers to override with user settings
vs alternatives: More integrated into VS Code than external spell checkers with separate configuration files, but less powerful than project-specific configuration files (like `.cspellrc.json`) that could be version-controlled and shared
Performs spell checking by comparing words against a pre-built dictionary loaded into memory at extension startup. The dictionary is stored as a compiled data structure (format unknown — likely a trie or hash set for O(1) lookup) and does not require network access. Validation is performed locally on the user's machine, ensuring privacy and fast response times. The extension does not use machine learning models or external APIs; it relies entirely on static dictionary matching.
Unique: Implements pure offline dictionary matching without ML models or external APIs. This is a deliberate design choice prioritizing privacy and performance over adaptive learning. The extension does not track user corrections or learn from usage patterns.
vs alternatives: Faster and more private than cloud-based spell checkers (e.g., Grammarly) because validation happens locally. No API calls or data transmission. Works offline without internet connectivity.
+2 more capabilities
Semgrep-core (OCaml engine) performs AST-based pattern matching against user-defined or curated rules to identify security vulnerabilities, code anti-patterns, and compliance violations. The engine parses source code into language-specific abstract syntax trees using tree-sitter and custom parsers, then matches patterns expressed in Semgrep's domain-specific language (YAML-based rule syntax) against the AST structure. This approach enables structural matching rather than regex-based detection, reducing false positives and enabling cross-language consistency.
Unique: Uses tree-sitter-based AST parsing with language-specific custom parsers for 30+ languages, enabling structural pattern matching that understands code semantics (function scope, variable binding, control flow) rather than relying on regex or token-based matching. The hybrid Python-OCaml architecture delegates computationally intensive matching to OCaml while maintaining a flexible Python CLI for workflow orchestration.
vs alternatives: Faster and more accurate than regex-based tools (Grype, Trivy) because it matches against AST structure; more flexible than signature-based scanners because rules can express complex syntactic patterns; lighter-weight than full symbolic execution tools (Coverity, Fortify) while still catching many real vulnerabilities.
Semgrep's taint analysis engine (available in Pro Engine) tracks data flow across function boundaries to detect vulnerability chains where untrusted input reaches a dangerous sink. The system constructs a dataflow graph by analyzing variable assignments, function parameters, return values, and object field mutations across the codebase. It identifies sources (user input, external data), sinks (SQL queries, command execution, file writes), and sanitizers (validation functions) to determine if tainted data can reach dangerous operations without proper sanitization.
Unique: Implements interprocedural taint analysis by constructing a dataflow graph from AST analysis, tracking variable bindings and function call chains to determine if untrusted data can reach dangerous sinks. The Pro Engine reduces false positives by ~25% and increases true positives by ~250% compared to single-function pattern matching by confirming actual reachability rather than just pattern presence.
Code Spell Checker scores higher at 57/100 vs Semgrep CLI at 57/100.
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vs alternatives: More precise than pattern-only matching (which flags all SQL queries regardless of input source) and faster than full symbolic execution tools because it uses lightweight dataflow analysis rather than constraint solving.
Semgrep includes language-specific parsers (built on tree-sitter and custom OCaml implementations) for 30+ programming languages. Each parser converts source code into an AST that the pattern matching engine can analyze. The system implements graceful error handling where parse errors in individual files do not stop the scan; instead, errors are logged and scanning continues on other files. This enables Semgrep to scan heterogeneous codebases with mixed languages and syntax variations without failing on unparseable code.
Unique: Implements language-specific parsers using tree-sitter (for most languages) and custom OCaml implementations (for performance-critical languages), with graceful error handling that allows scanning to continue even if individual files fail to parse. This architecture enables Semgrep to support 30+ languages without requiring language-specific scanning tools.
vs alternatives: More comprehensive language support than language-specific tools (like Pylint for Python or ESLint for JavaScript) because it handles multiple languages in a single tool; more robust than regex-based tools because it parses code into AST structure.
Semgrep includes an MCP server implementation that exposes scanning capabilities to AI models and LLM-based tools. The MCP server allows AI assistants to invoke Semgrep scans, retrieve findings, and analyze code patterns programmatically. This enables integration with AI-powered code review tools, automated remediation assistants, and LLM-based security analysis workflows. The server implements standard MCP protocols for tool invocation and result streaming.
Unique: Implements an MCP server that exposes Semgrep scanning capabilities to AI models and LLM-based tools, enabling integration with AI-powered code review and remediation workflows. The server implements standard MCP protocols for tool invocation, allowing AI assistants to invoke Semgrep scans and analyze findings programmatically.
vs alternatives: Enables AI-assisted code analysis by exposing Semgrep as an MCP tool; more integrated than separate AI and scanning tools because findings are directly available to AI models for reasoning and remediation.
Semgrep's OCaml engine tracks token positions and source locations during AST parsing and pattern matching, enabling precise reporting of finding locations (file, line, column, character offset). The system maintains a mapping between AST nodes and their source positions, allowing findings to be reported with exact character ranges. This enables IDE integration, inline code comments, and precise highlighting in web interfaces. The position tracking is implemented at the parser level and maintained through the entire analysis pipeline.
Unique: Maintains token and position tracking throughout the OCaml analysis pipeline, enabling precise character-level location reporting for findings. This architecture enables IDE integration, inline code highlighting, and automated remediation by providing exact token ranges rather than just line numbers.
vs alternatives: More precise than tools reporting only line numbers because it provides character offsets; enables better IDE integration and automated fixes because exact token ranges are available.
Semgrep provides a YAML-based domain-specific language (DSL) for expressing code patterns that work across multiple programming languages. Rules are defined in YAML with pattern syntax that abstracts away language-specific details (e.g., a pattern for 'function call' works identically in Python, JavaScript, and Go). The pysemgrep CLI parses rule files, validates syntax, and passes compiled rules to semgrep-core for matching. Users can write custom rules targeting their codebase, organization standards, or specific vulnerability patterns without modifying the core engine.
Unique: Provides a language-agnostic YAML-based DSL that abstracts away language-specific syntax details, allowing a single rule to match equivalent patterns across Python, JavaScript, Go, Java, and 25+ other languages. Rules are compiled to an intermediate representation that semgrep-core interprets, enabling rapid rule iteration without recompiling the core engine.
vs alternatives: More accessible than writing custom checkers in OCaml or C++ (as required by Clang Static Analyzer or Coverity) and more expressive than regex-based tools because rules can reference AST structure and semantic relationships.
The `semgrep ci` command integrates Semgrep into CI/CD workflows by scanning code, uploading findings to semgrep.dev, comparing against baseline scans, and enforcing organization-wide policies. The Python CLI (pysemgrep) orchestrates the workflow: it authenticates to Semgrep App using API tokens, fetches organization-specific rules and policies, runs the OCaml scanning engine, and reports results. The system can block CI builds based on policy rules (e.g., 'fail if critical vulnerabilities detected'), automatically triage findings based on organization rules, and track finding status across commits.
Unique: Implements a hybrid local-remote workflow where the OCaml scanning engine runs locally (fast, no data transmission) but policy enforcement and finding triage happen server-side via semgrep.dev API. This architecture enables organizations to enforce policies without exposing source code to the cloud while maintaining centralized policy management. The system tracks finding status across commits, enabling developers to see remediation progress.
vs alternatives: More flexible than GitHub's native code scanning (which only supports GitHub-native rules) because it supports custom rules and cross-language patterns; more integrated than standalone SAST tools because it provides built-in CI/CD orchestration and finding management.
Semgrep supports incremental scanning mode where it compares current scan results against a baseline (previous commit or main branch) to report only new or changed findings. The Python CLI manages baseline storage and comparison logic: it fetches the previous scan's JSON output, compares rule matches by file path and line number, and reports only findings that are new, moved, or changed in severity. This reduces noise in CI/CD by surfacing only actionable changes rather than all findings in the codebase.
Unique: Implements baseline comparison at the Python CLI layer by storing and comparing JSON scan results, enabling incremental reporting without requiring the OCaml engine to maintain state. This design allows flexible baseline sources (local files, semgrep.dev API, git history) while keeping the core scanning engine stateless.
vs alternatives: Simpler than tools requiring full codebase re-analysis (like some SAST tools) because it compares results rather than re-running analysis; more practical than git-diff-based filtering because it handles line number shifts and can detect moved findings.
+5 more capabilities