Govern a Python agent

# Install from PyPI.
pip install metamynd-client

# Offline first: this checks the three signing details below without
# calling us at all, so a failure here is your environment, not the gate.
python -m metamynd_client --selftest

# Then live, against a real agent.
export METAMYND_API=https://metamynd.ai/api/v1
export AGENT_DID=did:hedera:testnet:...
export AGENT_KEY=302e020100...          # exactly as issued
python -m metamynd_client

from langgraph.prebuilt import ToolNode
from metamynd_client import MetaMyndClient, guard_agent_tool

client = MetaMyndClient.from_env()

# Your tool, unchanged and unaware it is governed.
def book_flight(airline: str, amount: float, route: str) -> dict:
    """Book a flight and charge the corporate card."""
    ...

# The governed one. Give THIS to your ToolNode. A refusal comes back as
# the call's result (a GovernanceRefusal the model reads), never a crash.
governed_book_flight = guard_agent_tool(
    client, "flight-purchase", book_flight,
    lambda airline, amount, route: {"amount": amount, "merchant": airline,
                                    "context": {"riskLevel": "high" if amount >= 1000 else "low"}},  # derived from the call, never hard-coded
)

# An ordinary LangGraph graph. Nothing about the wiring changes.
tools = ToolNode([governed_book_flight])

from langchain.agents import create_agent
from langchain_core.tools import tool
from metamynd_client import MetaMyndClient, guard_agent_tool

client = MetaMyndClient.from_env()

# Your tool, unchanged and unaware it is governed.
def book_flight(airline: str, amount: float, route: str) -> dict:
    """Book a flight and charge the corporate card."""
    ...

# Guard on the INSIDE, wrap with tool() on the OUTSIDE.
governed_book_flight = guard_agent_tool(
    client, "flight-purchase", book_flight,
    lambda airline, amount, route: {"amount": amount, "merchant": airline,
                                   "context": {"riskLevel": "high" if amount >= 1000 else "low"}},  # derived from the call, never hard-coded
)

# An ordinary create_agent graph — LangChain 1.x's only agent constructor
# now that AgentExecutor is gone. Nothing about the wiring changes.
agent = create_agent(model="openai:gpt-4o-mini", tools=[
    tool(governed_book_flight),
])

from pydantic_ai import Agent
from metamynd_client import MetaMyndClient, guard_agent_tool

client = MetaMyndClient.from_env()

# Your tool, unchanged. async def is fine: the guard keeps it async and
# keeps its signature, so PydanticAI builds exactly the same schema.
async def book_flight(airline: str, amount: float, route: str) -> dict:
    """Book a flight and charge the corporate card."""
    ...

governed_book_flight = guard_agent_tool(
    client, "flight-purchase", book_flight,
    lambda airline, amount, route: {"amount": amount, "merchant": airline,
                                   "context": {"riskLevel": "high" if amount >= 1000 else "low"}},  # derived from the call, never hard-coded
)

agent = Agent("openai:gpt-4o-mini",
              instructions="You book flights. If a booking is refused, explain why.")
agent.tool_plain(governed_book_flight)