Most AI agent frameworks treat the LLM output as ground truth. It comes back, you act on it.

That's the problem.

Axiom is a new Python runtime that changes the contract between agent and LLM. Every belief your agent forms carries:

A confidence score (0.0–1.0) — how sure is the agent?

A provenance chain — where did this belief come from?

An is_actionable flag — should the agent act on this?

And if you're running multiple agents, Axiom lets them verify each other without a central orchestrator .

The problem with current frameworks

LangChain, CrewAI, AutoGen — they all give you tool use and orchestration. Some give you memory. None of them ask: how confident is this agent in what it just said?

This matters because:

Agents hallucinate with full confidence

In multi-agent systems, you're trusting Agent B's output blindly

There's no audit trail of why an agent did something

What Axiom gives you

from axiom import AxiomAgent , BuiltinConstraints import anthropic

client = anthropic .…