LangChain and LangGraph

Instrument LangChain agents and LangGraph nodes with Apie callback handlers.

You run LangChain agents or LangGraph workflows. You want tool calls and graph node executions tracked inside Apie runs without wrapping every tool manually.

Callback handler (recommended)

Pass ApieCallbackHandler to your agent’s callbacks inside a run:

TypeScript

import { ApieCallbackHandler } from "@apie-sh/sdk/integrations";
import apie from "./apie.config";

const handler = new ApieCallbackHandler(apie, {
  defaultEnvironment: "production",
});

await apie.withRun({ inputSummary: "LangChain agent run" }, async () => {
  const result = await agent.invoke(
    { input: "Summarize the incident" },
    { callbacks: [handler] },
  );
});

The handler tracks tool start/end events and infers action/resource metadata from tool names.

LangGraph node wrapper

Wrap individual graph nodes for step-level telemetry:

TypeScript

import { withLangGraphNode } from "@apie-sh/sdk/integrations";

await withLangGraphNode(
  apie,
  {
    runId: run.id,
    nodeName: "triage",
    stepKey: "triage-node",
    stepIndex: 1,
  },
  async () => triageNode(state),
);

LangChain tool step

For individual tool steps with explicit metadata:

TypeScript

import { withLangChainToolStep } from "@apie-sh/sdk/integrations";

await withLangChainToolStep(
  apie,
  {
    runId: run.id,
    toolName: "search",
    actionType: "read",
    resourceType: "knowledge_base",
  },
  async () => searchTool.invoke("query"),
);

What you’ll see

Tool calls and workflow steps in the run timeline. In monitor mode, guard evaluations appear for tools with inferred or explicit metadata.

Example

See the LangChain example in the SDK repos:

• javascript-sdk/examples/langchain-instrumented-agent.ts
• python-sdk/examples/langchain_instrumented_agent.py

Next steps

Choose how to instrument

Framework plugin tier overview.

Monitor mode

Observe guard evaluations first.