A.I.G (AI Infra Guard) vs Code Review Graph: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of A.I.G (AI Infra Guard) and Code Review Graph — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
A.I.G (AI Infra Guard)
Tencent Zhuque Lab
Tencent's open-source AI red teaming platform for scanning agents, agent skills, MCP servers and AI infrastructure, plus LLM jailbreak evaluation.
Key features
- Agent Skills Scan: Audits agent skill packages against a nine-category risk taxonomy aligned with the public SkillTrustBench T01-T09 classification, including detection of .pyc bytecode bypasses and charset smuggling.
- MCP Server Scan: Inspects MCP servers for threats such as tool poisoning, credential exfiltration and command injection, with tool whitelisting to prevent remote code execution during dynamic scanning.
- AI Infrastructure Vulnerability Scanning: Checks deployed AI components against a library that has grown to roughly 130 components and over 2,000 CVE rules, covering frameworks such as llama.cpp.
- Jailbreak Evaluation: Runs single-turn jailbreak operators plus multi-turn attack techniques including Many-Shot, PAIR, GOAT and ActorAttack to measure a model's resistance.
- Agent Scan with OWASP Coverage: Assesses running agents using OWASP-derived skills and web exfiltration detection, with a dedicated agent red team skill for comprehensive assessment.
- Standalone Scanner CLIs: skill-scan, mcp-scan and agent-scan each install as an independent command-line tool so scans can be wired directly into enterprise CI/CD pipelines.
- Docker Deployment with Web UI: Deploy the full platform with Docker on 4GB+ RAM and reach the web interface at localhost:8088, or use a one-click install script or a source build.
- AI Security Skill Market: A catalog of official security scanning skills, with the frontend fully open-sourced and integration available from OpenClaw chat via the aig-scanner skill.
Best for
- Pre-Deployment Agent Audit: Scan an internally built agent and its skill bundle for prompt injection, exfiltration and privilege risks before it is released to staff.
- MCP Supply Chain Review: Vet third-party MCP servers for tool poisoning and credential exfiltration before connecting them to production assistants.
- CI/CD Security Gate: Run skill-scan as a standalone CLI on every pull request so risky agent skills fail the build rather than shipping.
- Model Safety Benchmarking: Measure how a deployed LLM holds up against single and multi-turn jailbreak techniques before and after guardrail changes.
- AI Infrastructure Patch Triage: Inventory AI serving components and match them against the CVE rule library to prioritise which hosts need patching.
- Security Research and Reporting: Use the open scan engines and SkillTrustBench alignment as a reproducible basis for internal or published AI security research.
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.