| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
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.
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
Implements configurable access control for file reading through a path allowlist/blocklist security model that validates all requested paths against configured boundaries before exposing file contents. Uses TypeScript SDK's tool registration pattern to expose read_file and list_directory tools with automatic path normalization and symbolic link resolution, preventing directory traversal attacks while maintaining transparent access semantics for LLM clients.
Unique: Uses MCP's native tool registration with declarative path allowlisting rather than OS-level permissions, enabling fine-grained LLM-specific access control that survives across different execution contexts and doesn't require filesystem-level changes
vs alternatives: More granular than OS-level file permissions and easier to configure per-client than containerization, while remaining simpler than full capability-based security models
Provides write_file tool that creates or overwrites files with optional safety checks for preventing accidental data loss. Implements atomic write semantics by writing to a temporary file first, then renaming to target path, ensuring partial writes don't corrupt existing files. Respects the same path validation layer as read operations, preventing writes outside configured boundaries.
Unique: Combines MCP tool semantics with filesystem-level atomic writes (temp-then-rename pattern) to guarantee consistency even if the MCP client crashes mid-operation, unlike simple write implementations that may leave partial files
vs alternatives: More reliable than direct file writes because atomic semantics prevent corruption, while remaining simpler than full transactional filesystems or version control integration
Filesystem MCP Server scores higher at 60/100 vs Semgrep CLI at 58/100.
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Implements list_directory tool that recursively enumerates directory trees with configurable depth limits and file type filtering. Returns structured metadata (size, modification time, permissions) for each entry without loading file contents, enabling efficient directory analysis. Uses TypeScript's fs.promises API with concurrent operations to traverse large directory structures while respecting path validation boundaries.
Unique: Exposes directory metadata through MCP tools with configurable recursion depth and filtering, allowing LLM clients to make informed decisions about which files to read next without requiring multiple round-trips or loading entire directory contents upfront
vs alternatives: More efficient than having LLMs read entire files to understand structure, and more flexible than simple ls-style listings because it includes metadata and supports filtering
Provides move_file tool that relocates files or directories within the sandboxed filesystem with configurable behavior for handling destination conflicts (fail, overwrite, or rename). Validates both source and destination paths against the same security boundaries, ensuring moves cannot escape the allowed directory tree. Implements atomic move semantics using OS-level rename operations when possible.
Unique: Integrates move operations into the MCP tool model with path validation on both source and destination, preventing LLM agents from accidentally moving files outside sandboxed boundaries while maintaining atomic semantics through OS-level rename when possible
vs alternatives: Safer than exposing raw filesystem operations because it validates both paths, and more flexible than read-only filesystem access because it enables file organization workflows
Implements search_files tool that finds files matching patterns (regex, glob, or literal strings) across the allowed filesystem tree. Returns matching file paths with optional context snippets showing where matches occur. Uses efficient pattern matching libraries (e.g., minimatch for globs) to avoid loading entire files into memory, supporting large codebases with thousands of files.
Unique: Exposes pattern-based file search through MCP tools with support for multiple pattern syntaxes (regex, glob, literal), allowing LLM clients to locate files efficiently without requiring full directory enumeration or file content loading upfront
vs alternatives: More efficient than having LLMs read entire directories to find files, and more flexible than simple filename matching because it supports content-based and pattern-based search
Implements the MCP server-side tool registration pattern using TypeScript SDK, exposing filesystem operations as callable tools through JSON-RPC 2.0 protocol. Handles tool schema definition (input parameters, return types), request routing, and error serialization automatically. Supports multiple transport mechanisms (stdio, HTTP, WebSocket) through MCP's transport abstraction layer, allowing the same server to work with different client configurations without code changes.
Unique: Leverages MCP's native tool registration abstraction to decouple tool implementation from transport mechanism, enabling the same filesystem server to work with stdio, HTTP, or WebSocket clients without modification through MCP's transport-agnostic design
vs alternatives: More standardized than custom REST APIs because it uses MCP's protocol, and more flexible than direct function calls because it supports multiple transport mechanisms and automatic schema validation
Implements a declarative security model where filesystem access is controlled through configuration files specifying allowed_directories (allowlist) or denied_paths (blocklist). Configuration is loaded at server startup and applied to all subsequent requests without requiring code changes. Supports glob patterns and environment variable expansion in configuration paths, enabling flexible deployment across different environments (dev, staging, production).
Unique: Provides declarative, configuration-driven access control that is loaded at server startup and applied uniformly to all requests, enabling environment-specific security policies without code changes or recompilation
vs alternatives: More flexible than hardcoded access rules because it supports configuration files, and simpler than role-based access control because it uses straightforward allowlist/blocklist semantics
Implements comprehensive error handling for filesystem operations with detailed diagnostic messages that help LLM clients understand why operations failed (e.g., 'Path /etc/passwd is outside allowed directories' vs generic 'Access denied'). Validates all inputs (paths, patterns, parameters) before execution and returns structured error responses through JSON-RPC error protocol, enabling clients to implement retry logic or fallback strategies.
Unique: Provides structured error responses with detailed diagnostic messages that distinguish between different failure modes (path validation, permissions, filesystem errors), enabling LLM clients to implement intelligent error handling without exposing sensitive system information
vs alternatives: More informative than generic error messages because it explains the specific reason for failure, while remaining secure by avoiding stack traces and sensitive path information
+2 more capabilities