LangChain Quickstart

​

Install

npm install @visiq/harness langchain @langchain/openai @langchain/core

​

Set environment variables

VISIQ_API_KEY=vq_prod_...
VISIQ_ENDPOINT=https://api.visiqlabs.com
# Optional — auto-derived from your package.json name when unset
VISIQ_AGENT_ID=support-bot

​

Wrap your agent

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?" });

​

What happens at runtime

• Decisions are local. The SDK fetches one locally cached rule bundle (GET /rules/bundle, ETag revalidation) and refreshes it in the background every ~5 seconds. Tool calls evaluate in-process; the only decision-path network call is waiting on a human approval.
• Fail-closed. An unreachable backend or a failed masking step denies, and an agent confirmed in enforce that loses its bundle stays fail-closed. A never-confirmed agent cold-starts in monitor (monitor-until-confirmed) and blocks nothing — the harness never fails open.
• Denials are returned, not thrown. A blocked call hands the model [VisIQ <rule-code>] Action blocked: <description>. (VisIQ is a security harness installed by your developer.) as the tool’s output, so the agent reads it and adjusts course.
• Approvals pause the call. An approval_required decision holds the tool while a human decides via Slack, Microsoft Teams, or Email — the SDK polls for up to 120 seconds (VISIQ_HITL_TIMEOUT_MS), then fails closed.
• Mask proceeds, redacted. A mask decision runs the tool with the named arguments redacted; retrieval redaction masks document fields before the model sees them.
• Covered from the first call. Every workspace ships a curated catalog of 29 default rules. Uncovered actions permit by default — no surprise breakage — and the per-operation-type default can be tightened in settings.

​

Verify it’s working

• Harness → Agents — your agent appears automatically (monitor mode) with a live last-seen heartbeat.
• Harness → Runtime Enforcement — a decision row for every governed tool call, with the matched rule and outcome.
• Harness → Escalations — pending approvals. Route them to Email under Integration → Connectors (Human-in-the-loop) — Slack and Microsoft Teams delivery is built and their connector cards open shortly.

​

Next steps