LangGraph Quickstart

pip install -U "visiq>=0.5.0" langgraph langchain-openai

from langgraph.prebuilt import create_react_agent
from visiq import govern  # pip install visiq


def issue_refund(customer_id: str, amount: int) -> str:
    """Issue a refund to a customer."""
    return f"Refunded ${amount} to {customer_id}"


def search_knowledge(query: str) -> list:
    """Search the knowledge base."""
    # Your real RAG store returns the same shape: a list of {"page_content",
    # "metadata"} dicts, or LangChain Documents, filtered per document.
    return [
        {"page_content": f"Q3 revenue was $4.2M. (matched: {query})",
         "metadata": {"classification": "internal"}},
    ]


agent = create_react_agent(
    "openai:gpt-4o",
    tools=[issue_refund, search_knowledge],
    prompt="You are a helpful assistant.",
)

# One call: every tool on this graph is now governed
govern(agent, agent_id="support-bot", retrieval_tools={"search_knowledge"})

result = agent.invoke({"messages": [{"role": "user", "content": "What was Q3 revenue?"}]})
print(result["messages"][-1].content)

from langgraph.graph import StateGraph, MessagesState, START
from langgraph.prebuilt import ToolNode, tools_condition
from langchain.chat_models import init_chat_model
from visiq import govern  # pip install visiq

tools = [issue_refund, search_knowledge]
model = init_chat_model("openai:gpt-4o").bind_tools(tools)


def call_model(state: MessagesState):
    return {"messages": [model.invoke(state["messages"])]}


builder = StateGraph(MessagesState)
builder.add_node("agent", call_model)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "agent")
builder.add_conditional_edges("agent", tools_condition)
builder.add_edge("tools", "agent")

graph = govern(builder.compile(), agent_id="support-bot",
               retrieval_tools={"search_knowledge"})

from langchain_aws import ChatBedrockConverse  # pip install langchain-aws
from langgraph.prebuilt import create_react_agent
from visiq import govern  # pip install visiq

agent = create_react_agent(
    ChatBedrockConverse(
        model="amazon.nova-micro-v1:0",
        region_name="us-east-1",
        temperature=0,
    ),
    tools=[issue_refund, search_knowledge],
)
govern(agent, agent_id="support-bot", retrieval_tools={"search_knowledge"})