CleanSlop vs Code Review Graph: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CleanSlop and Code Review Graph — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CleanSlop
CleanSlop
Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.
Key features
- Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
- System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
- Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
- Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
- Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
- Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
- GitHub App Integration: Scan only the repositories you select, read-only access.
- Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.
Best for
- A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
- A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
- A side-project builder verifies that one user's data cannot be read by another through database policies.
- A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
- A team lead gets a short prioritized list instead of hundreds of linter warnings.
- An indie hacker books an expert triage call to confirm an ambiguous finding.
C
Code Review Graph
tirth8205
Code Review Graph is a local-first code intelligence graph for MCP and CLI that cuts AI coding tool context by mapping only what matters.
Key features
- Persistent Repo Graph: Builds and maintains a graph of the codebase's symbols, references, and structure so lookups are instant on subsequent runs.
- MCP Server Integration: Exposes the graph as an MCP server so Claude Code, Cursor, and other MCP-compatible agents can query it directly.
- CLI Access: A first-class command-line interface lets developers query the graph without an agent in the loop.
- Task-Scoped Context Slices: Instead of loading whole files, returns only the pieces of code an AI tool needs — with benchmarked context reductions.
- Local-First Privacy: All indexing and serving runs on the developer's machine, so source code never leaves the environment.
- PyPI Distribution: Installs with a single `pip install code-review-graph` and works on any Python 3.10+ setup.
Best for
- Cheaper AI Code Reviews: Feed only the relevant slices of a change to a review agent so token spend on large PRs stays low.
- Large Monorepo Workflows: Give coding agents targeted context in repos too big to fit into any model's window.
- MCP-Compatible Agent Enhancement: Plug into Claude Code or Cursor as an MCP server to add repo-aware retrieval.
- Local Refactor Planning: Use the CLI to explore dependencies and impact before making cross-cutting changes.
- Air-Gapped Codebases: Keep proprietary source local while still using AI tools that consume the graph rather than raw files.
- Onboarding Assistance: Help new engineers navigate a large codebase by querying the graph for related symbols and callers.