AI capabilities
What is AI code review?
Using a language model to review changes contextually, rather than matching them against fixed rules.
A model reads the diff along with surrounding code and comments on what it finds: naming, missed edge cases, logic that contradicts the stated intent of the change. It covers ground no ruleset describes.
It complements rather than replaces deterministic analysis. Static rules are repeatable and auditable; model review is broader but variable. Teams that get value from it typically run both and keep the outputs visually distinct.
What to look for
- Which model is used, and whether that is disclosed at all
- Whether review context includes linked issues and repository history
- Comment precision measured on your own pull requests
- Whether AI findings are labeled separately from scanner findings
Best 20 AI code review tools
Every tool in the directory whose documentation covers this, 19 fully and 1 partially. Full support is shown first. Shown in a random order that changes on every visit, so no tool gets an advantage from its position.
Support is not the same as parity, since some implementations are narrower in scope, gated to a higher plan tier, or maintained only for existing customers. The note under each tool is what its own documentation describes.
Code Security Platform
Deep Review performs AI contextual code review of business logic and attack paths
AI Coding Agent
Review and scanning are agentic Codex runs; Codex Security defaults to gpt-5.6-sol at xhigh reasoning effort.
Code Security Platform
AI Security Champion explains and fixes scanner findings; AI Triage analyzes new pull-request vulnerabilities.
AI Coding Agent
Entire product is agent-based: parallel reviewer agents on Anthropic infrastructure with independent verification.
Code Quality Platform
AI Reviewer (GitHub) combines static analysis with Gemini contextual review using PR, Jira, history context.
PR Review Tool
AI-assisted agentic code review across PR, IDE and CLI, grounded in full codebase context
PR Review Tool
LLM-based reasoning engine with 57 static analysis tools layered on top
Code Quality Platform
AI-powered PR analysis plus CodeScene ACE refactoring with deterministic Code Health fact-checking
Code Security Platform
BLAST combines LLM reasoning with static/AST analysis
PR Review Tool
Bugbot, Security Agents, Agent Review and PR Routing are all LLM agents on Cursor's cloud.
Code Quality Platform
AI Review agent runs hybrid with static analysis, governed by a team-level AI & Agents policy.
AI Coding Agent
PR reviews, chat and completions all run on Gemini foundation models with automatic model selection.
Code Security Platform
AI-powered security detections run at PR review for languages CodeQL misses; Code Quality adds LLM findings
PR Review Tool
Codebase-graph-grounded AI review posting summary, confidence score and inline comments
Code Quality Platform
LLM powers opt-in AI autofixes and AI issue explanations alongside deterministic linter analysis.
PR Review Tool
Multi-agent LLM review engine is the core product; agents per quality dimension
Code Security Platform
Semgrep Multimodal AI layer for detection, guidance, autotriage, tagging; unavailable on Free (no AI credits).
Code Security Platform
DeepCode AI semantic engine powers Snyk Code; LLMs used for breakability, Agent Fix, Snyk Assist.
Code Quality Platform
AI CodeFix is LLM-powered on Team and Enterprise; Gitar AI PR review sold as a separate product.
Code Security Platform
Veracode Fix uses ML plus RAG for remediation only; no AI reviewer that finds issues itself
Related terms
FAQ
What is AI code review?
Using a language model to review changes contextually, rather than matching them against fixed rules.
How many tools in the directory offer AI code review?
All 20 tools in this directory offer AI code review, 19 fully and 1 partially:
Support is not the same as parity, since some implementations are narrower in scope, gated to a higher plan tier, or maintained only for existing customers. The note under each tool is what its own documentation describes.
What should you check when comparing tools with AI code review?
Four things:
- Which model is used, and whether that is disclosed at all
- Whether review context includes linked issues and repository history
- Comment precision measured on your own pull requests
- Whether AI findings are labeled separately from scanner findings