liuhaotian2024-prog/K9Audit
Engineering-grade causal audit infrastructure for AI agents
What's novel
Engineering-grade causal audit infrastructure for AI agents
Code Analysis
9 files read · 3 roundsA Python library that records LLM agent actions into a SHA256 hash-chained CIEU ledger (context, action, constraints, outcome, assessment), provides causal chain tracing to find root causes of agent misbehavior, and offers real-time constraint violation alerts via multiple channels.
Strengths
Genuinely well-designed hash-chain integrity system with proper canonicalization, thread safety, and log rotation; the CIEU five-tuple model is a coherent and practical framework for agent auditing that goes beyond simple logging. The constraint system is extensible (custom registry, multi-source loading, AST-based magic suggestions) and the causal analyzer provides real value for debugging multi-step agent failures.
Weaknesses
The 'causal' analysis is heuristic string-matching rather than true causal inference, which overstates the mathematical rigor claimed in the README; test coverage is thin (only replay scripts and a concurrency test at the root level, no proper unit test suite), and there are code quality issues like duplicate __all__ definitions, awkward __import__ usage, and excessive try/except swallowing that could mask real failures.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
86
Contributors
1
Files
64
Active weeks
2
Repository
Language
Python
Stars
3
Forks
1
License
NOASSERTION