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.
Top 20 AI Code Review tools
Every tool in the directory documented as supporting AI Code Review, 19 fully, 1 partially, with what its own documentation says.
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.
- 1
AI Reviewer (GitHub) combines static analysis with Gemini contextual review using PR, Jira, history context.
- 2
Deep Review performs AI contextual code review of business logic and attack paths
- 3
Review and scanning are agentic Codex runs; Codex Security defaults to gpt-5.6-sol at xhigh reasoning effort.
- 4Checkmarx OneCode Security Platform
AI Security Champion explains and fixes scanner findings; AI Triage analyses new pull-request vulnerabilities.
- 5Claude CodeAgent Coding Tool
Entire product is agent-based: parallel reviewer agents on Anthropic infrastructure with independent verification.
- 6CodeAnt AIPR Review
AI-assisted agentic code review across PR, IDE and CLI, grounded in full codebase context
- 7
LLM-based reasoning engine with 57 static analysis tools layered on top
- 8
AI-powered PR analysis plus CodeScene ACE refactoring with deterministic Code Health fact-checking
- 9CorgeaCode Security Platform
BLAST combines LLM reasoning with static/AST analysis
- 10
Bugbot, Security Agents, Agent Review and PR Routing are all LLM agents on Cursor's cloud.
- 11DeepSourceQuality Platform
AI Review agent runs hybrid with static analysis, governed by a team-level AI & Agents policy.
- 12
PR reviews, chat and completions all run on Gemini foundation models with automatic model selection.
- 13GitHub Advanced SecurityCode Security PlatformAdd-on: Code Security (source marks its plan gating 'implied'); Copilot licence for Code Quality AI findings
AI-powered security detections run at PR review for languages CodeQL misses; Code Quality adds LLM findings
- 14GreptilePR Review
Codebase-graph-grounded AI review posting summary, confidence score and inline comments
- 15
LLM powers opt-in AI autofixes and AI issue explanations alongside deterministic linter analysis.
- 16QodoPR Review
Multi-agent LLM review engine is the core product; agents per quality dimension
- 17
Semgrep Multimodal AI layer for detection, guidance, autotriage, tagging; unavailable on Free (no AI credits).
- 18SnykCode Security Platform
DeepCode AI semantic engine powers Snyk Code; LLMs used for breakability, Agent Fix, Snyk Assist.
- 19
AI CodeFix is LLM-powered on Team and Enterprise; Gitar AI PR review sold as a separate product.
- 20
Veracode Fix uses ML plus RAG for remediation only; no AI reviewer that finds issues itself
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 labelled separately from scanner findings
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 support AI Code Review?
20 of the 20 tools tracked in this directory support AI Code Review, 19 fully and 1 partially, including Codacy, Aikido, ChatGPT / Codex, Checkmarx One, Claude Code. 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 look for in AI Code Review?
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 labelled separately from scanner findings.