Axiom

The first agent runtime that knows what it doesn't know.

LangChain, AutoGen, CrewAI — they all let agents act. None of them let agents reason about their own confidence before acting. Axiom does.

Every belief an Axiom agent holds carries an explicit confidence score and provenance chain. A Guardian layer blocks actions when confidence is too low. Agents verify each other directly without a central orchestrator. And cryptographic identity persists across sessions so you can detect drift.

Why this matters

# Every other framework:
answer = agent.run("Is this deployment safe?")
deploy(answer)  # hope for the best

# Axiom:
belief = agent.think("Is this deployment safe?")
print(belief.confidence)   # 0.43 — Guardian blocks the action
print(belief.provenance)   # "reasoning:incomplete_data"
agent.act("deploy", deploy_fn)  # raises ConfidenceTooLow

vs other frameworks

Install

pip install axiom-agent

Quick start

import anthropic
from axiom import AxiomAgent, BuiltinConstraints

client = anthropic.Anthropic()

def my_llm(prompt: str) -> str:
    return client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        messages=[{"role": "user", "content": prompt}],
    ).content[0].text

agent = AxiomAgent(
    name="researcher-01",
    llm=my_llm,
    constraints=[BuiltinConstraints.min_confidence(0.6)],
)

belief = agent.think("What are the risks of deploying untested ML models?")
print(belief.confidence)      # 0.82
print(belief.provenance_str)  # "reasoning:risk_analysis, memory:prior_context"
print(belief.is_actionable)   # True  (confidence >= 0.6)

# Snapshot identity and measure drift from baseline
snap = agent.snapshot()
print(snap["divergence_from_baseline"])  # 0.0 on first run
print(snap["public_key"])                # cryptographic fingerprint

Multi-agent trust

No other framework lets Agent A independently verify Agent B before acting on its output — without a central authority.

researcher = AxiomAgent("researcher-01", llm=my_llm)
validator  = AxiomAgent("validator-01",  llm=my_llm)

belief = researcher.think("Current state of quantum error correction?")
snap   = researcher.snapshot()

# Validator verifies researcher independently
trust = validator.verify_peer("researcher-01", peer_snapshot=snap)
print(trust.verdict)      # "trusted"
print(trust.trust_score)  # 0.91

if trust.is_trusted:
    validator.act("publish", publish_fn, belief.content)

Constraints

from axiom import BuiltinConstraints

BuiltinConstraints.min_confidence(0.7)        # block if agent < 70% confident
BuiltinConstraints.deny(["send_email"])        # always block named actions
BuiltinConstraints.require_peer_trust(0.6)    # warn if acting on low-trust peer output
BuiltinConstraints.rate_limit("publish", 5)   # max 5 publish calls per minute

Architecture

axiom/
├── agent.py              ← AxiomAgent — the main interface
├── core/
│   ├── identity.py       ← RSA-2048 cryptographic identity, persists to disk
│   ├── memory.py         ← SQLite-backed beliefs with confidence + provenance
│   └── drift.py          ← hash-based drift monitoring across snapshots
├── epistemic/
│   └── belief.py         ← Belief dataclass: content + confidence + provenance
├── guardian/
│   └── constraint.py     ← action gating + tamper-evident audit trail
└── trust/
    └── peer.py           ← agent-to-agent verification (local or Cathedral-backed)

Roadmap

• Consensus mechanism: N agents must agree before high-stakes action fires
• Gossip protocol: agents share verified high-confidence beliefs across a network
• Cathedral sync: optional cloud backup of identity + drift timeline
• MCP server: Axiom as a tool any Claude session can call (axiom_mcp.py)
• PyPI release — pip install axiom-agent (v0.2.0)

Built on

• Cathedral — persistent identity + drift detection
• AgentGuard — runtime safety constraints
• Veritas — epistemic confidence engine

Support

BCH: bitcoincash:qr3f60yk6yc0vut3hukhuch8dylwjnq8qvv0q5pnxv

Starring the repo and sharing it helps just as much.

License

MIT