LangChain Quickstart

npm install @visiq/harness "langchain@^0.3" "@langchain/openai@^0.3" "@langchain/core@^0.3" "zod@^3"

import { visiq } from "@visiq/harness";
import { AgentExecutor, createOpenAIToolsAgent } from "langchain/agents";
import { createRetrieverTool } from "langchain/tools/retriever";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { DynamicTool } from "@langchain/core/tools";

// A small in-memory knowledge base so this snippet runs as pasted.
const vectorStore = await MemoryVectorStore.fromTexts(
  ["Q3 revenue was $4.2M.", "Refunds over $500 require manager approval."],
  [{ classification: "internal" }, { classification: "public" }],
  new OpenAIEmbeddings(),
);
const knowledgeRetriever = vectorStore.asRetriever();

// Your tools — unchanged.
const tools = [
  new DynamicTool({
    name: "issue_refund",
    description: "Issue a refund to a customer",
    func: async (input: string) => {
      const { customerId, amount } = JSON.parse(input);
      return `Refunded $${amount} to ${customerId}`;
    },
  }),
  createRetrieverTool(knowledgeRetriever, {
    name: "search_knowledge",
    description: "Search the company knowledge base",
  }),
];

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant."],
  ["human", "{input}"],
  ["placeholder", "{agent_scratchpad}"],
]);
const llm = new ChatOpenAI({ model: "gpt-4o", temperature: 0 });
const agent = await createOpenAIToolsAgent({ llm, tools, prompt });

// ── One call — governance + audit trail activate here ──
const executor = visiq(new AgentExecutor({ agent, tools }), { agentId: "support-bot" });

const result = await executor.invoke({ input: "What was Q3 revenue?" });

pip install -U "visiq>=0.5.0" "langchain>=1.0" langchain-openai

from langchain.agents import create_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 company 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_agent(
    "openai:gpt-4o",
    tools=[issue_refund, search_knowledge],
    system_prompt="You are a helpful assistant.",
)

# One call: every tool on this agent 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 langchain.agents import create_agent
from langchain_aws import ChatBedrockConverse  # pip install langchain-aws
from visiq import govern  # pip install visiq

agent = create_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"})