Signet: Cryptographic Enforcement of LLM Transitions

Cryptographic State Enforcement for AI Agents.

Problem: LLMs are probabilistic. They hallucinate. Solution: Signet is deterministic. It forces your agent to adhere to a strict Cryptographic State Machine.

If your agent tries to output a state that violates your business logic (e.g., refunding > $100) or your process graph (e.g., skipping "Approval"), Signet blocks it and auto-corrects the LLM.

• Graph Enforcement: Define valid transitions (START -> ANALYZING -> APPROVED). Signet blocks illegal jumps.
• Self-Healing Middleware: Automatically retries LLM calls with error feedback when hallucinations occur.
• Tamper-Proof Logs: Every state transition is cryptographically signed (Ed25519).
• Visual Debugger: Includes a local Streamlit dashboard to visualize your agent's logic.

pip install signet-ai
python
from signet import AgentState, StateMachineGuard, StateStatus
from pydantic import Field
from typing import Dict

class RefundState(AgentState):
    data: Dict[str, int] = Field(..., description="Must contain 'amount'")

REFUND_GRAPH = {
    "START": ["ANALYZING"],
    "ANALYZING": ["APPROVED", "DENIED"],
    "APPROVED": [],
    "DENIED": []
}
python
from signet.middleware import SignetClient
import openai

guard = StateMachineGuard(RefundState, REFUND_GRAPH)
client = SignetClient(openai.Client(), guard)

final_state = client.run(
    messages=[{"role": "user", "content": "Process a refund for $50"}],
    model="gpt-4"
)

print(f"Signed: {final_state.signature}")

Signet comes with a built-in dashboard to test your graph logic.

1. Create a file viz.py (see examples folder).
2. Run:

streamlit run viz.py

Existing frameworks (LangGraph, AutoGen) manage memory. Signet manages trust. Use Signet when you need to ensure that your agent followed the rules.