@langinsight/langchain

LangInsight callback handler for LangChain.

Usage

Basic Usage

import { ChatOllama } from "@langchain/ollama";
import { LangInsight } from "@langinsight/langchain";

const model = new ChatOllama({ model: "gpt-oss:20b" });

const { LANGINSIGHT_ENDPOINT, LANGINSIGHT_API_KEY } = process.env;

// Specify callback handler when invoking LLM
await model.invoke("hi!", {
  callbacks: [
    new LangInsight.CallbackHandler({
      metadata: { userId: "admin", sessionId: "admin" },
      endpoint: LANGINSIGHT_ENDPOINT,
      apiKey: LANGINSIGHT_API_KEY,
    }),
  ],
});

Usage with LangGraph

import { Annotation, END, START, StateGraph } from "@langchain/langgraph";
import { ChatOllama } from "@langchain/ollama";
import { LangInsight } from "@langinsight/langchain";

const model = new ChatOllama({ model: "gpt-oss:20b" });

// Configure LangInsight callback handler
const callbacks = [
  new LangInsight.CallbackHandler({
    metadata: { userId: "admin", sessionId: "langgraph-example" },
    endpoint: process.env.LANGINSIGHT_ENDPOINT,
    apiKey: process.env.LANGINSIGHT_API_KEY,
  }),
];

// Define graph state
const GraphState = Annotation.Root({
  input: Annotation<string>,
  response: Annotation<string>,
});

// Define node
async function processInput(state: typeof GraphState.State) {
  const response = await model.invoke(state.input, { callbacks });
  return {
    response: response.content as string,
  };
}

// Build graph
const workflow = new StateGraph(GraphState)
  .addNode("process", processInput)
  .addEdge(START, "process")
  .addEdge("process", END);

const app = workflow.compile();

// Execute graph
const result = await app.invoke(
  { input: "What is LangGraph?" },
  { callbacks }
);

Options

• apiKey: LangInsight API key (required)
• endpoint: LangInsight API endpoint (required)
• metadata: Metadata to attach to trace information (required)
  • userId: User ID
  • sessionId: Session ID
  • Any other key-value pairs