axiomcodegraph: Turn any codebase into a knowledge graph grounded in formal methods

by AxiomCodeAI · MCP Server · ★ 482

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install axiomcodegraph

git clone https://github.com/AxiomCodeAI/axiomcodegraph.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsai-skillsantigravityastchange-impact-analysisclaude-codecode-analysiscode-searchcodegraphcodex

Quick Facts

Stars482
Forks91
LanguageTypeScript
CategoryMCP Server
Quality Score66/100
Open Issues78
Last Updated2026-11-05
Created2026-08-22
Platformsclaude-code, codex, gemini, mcp, node
Est. Tokens~20k

About axiomcodegraph

AxiomCode Graph A god's-eye view of your codebase for AI agents. Stop grepping. Ensure correctness and completeness for any task your AI agent performs. &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp; <

axiomcodegraph alternative? Top 6 similar tools

Looking for a axiomcodegraph alternative? If you're comparing axiomcodegraph with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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  • memorix by AVIDS2 · ⭐ 898

    Open-source cross-agent memory layer for coding agents via MCP. Compatible with Claude Code, Codex, Cursor, Wi

  • codegraph-rust by Jakedismo · ⭐ 888

    100% Rust implementation of code graphRAG with blazing fast AST+FastML parsing, surrealDB backend and advanced

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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