GPT says "X", Claude says "Y", Gemini says "Z"
— who do you trust?
ACP achieves consensus between AI models using universal axioms — truths that AI cannot deny because it is built upon them.
"AI cannot lie about what it's built on."
These self-referential facts become the undeniable foundation for consensus.
Axiom Spiral
Each iteration narrows disagreement by the golden ratio (φ ≈ 1.618). After 7 levels, only 3.4% disagreement remains — consensus is mathematically guaranteed.
Understanding D-Score
The divergence metric that quantifies model agreement
0.0 - 0.2
High Confidence
0.2 - 0.4
Moderate
0.4 - 0.6
Low Confidence
0.6 - 1.0
No Consensus
Use Cases
Where multi-model consensus matters most
Code Decisions
"Redis or Memcached?"
Security Review
"Is this SQL injection?"
Fact Check
"When did WWII end?"
IDE Integration
Get AI consensus directly in your code editor. Multi-model code review, real-time suggestions, and D-Score validation.
VS Code
JetBrains
CLI
Join Telegram for updates
Open Source
ACP is free and open source. We believe AI consensus should be accessible to everyone.