How to pass callbacks into a module constructor

Prerequisites

This guide assumes familiarity with the following concepts:

• Callbacks

Most LangChain modules allow you to pass callbacks directly into the constructor. In this case, the callbacks will only be called for that instance (and any nested runs).

Here’s an example using LangChain’s built-in ConsoleCallbackHandler:

import { ConsoleCallbackHandler } from "@langchain/core/tracers/console";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatAnthropic } from "@langchain/anthropic";

const handler = new ConsoleCallbackHandler();

const prompt = ChatPromptTemplate.fromTemplate(`What is 1 + {number}?`);
const model = new ChatAnthropic({
  model: "claude-3-sonnet-20240229",
  callbacks: [handler],
});

const chain = prompt.pipe(model);

await chain.invoke({ number: "2" });

[llm/start] [1:llm:ChatAnthropic] Entering LLM run with input: {
  "messages": [
    [
      {
        "lc": 1,
        "type": "constructor",
        "id": [
          "langchain_core",
          "messages",
          "HumanMessage"
        ],
        "kwargs": {
          "content": "What is 1 + 2?",
          "additional_kwargs": {},
          "response_metadata": {}
        }
      }
    ]
  ]
}
[llm/end] [1:llm:ChatAnthropic] [1.00s] Exiting LLM run with output: {
  "generations": [
    [
      {
        "text": "1 + 2 = 3",
        "message": {
          "lc": 1,
          "type": "constructor",
          "id": [
            "langchain_core",
            "messages",
            "AIMessage"
          ],
          "kwargs": {
            "content": "1 + 2 = 3",
            "tool_calls": [],
            "invalid_tool_calls": [],
            "additional_kwargs": {
              "id": "msg_011Z1cgi3gyNGxT55wnRNkXq",
              "type": "message",
              "role": "assistant",
              "model": "claude-3-sonnet-20240229",
              "stop_sequence": null,
              "usage": {
                "input_tokens": 16,
                "output_tokens": 13
              },
              "stop_reason": "end_turn"
            },
            "response_metadata": {
              "id": "msg_011Z1cgi3gyNGxT55wnRNkXq",
              "model": "claude-3-sonnet-20240229",
              "stop_sequence": null,
              "usage": {
                "input_tokens": 16,
                "output_tokens": 13
              },
              "stop_reason": "end_turn"
            }
          }
        }
      }
    ]
  ],
  "llmOutput": {
    "id": "msg_011Z1cgi3gyNGxT55wnRNkXq",
    "model": "claude-3-sonnet-20240229",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 16,
      "output_tokens": 13
    },
    "stop_reason": "end_turn"
  }
}

AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: "1 + 2 = 3",
    tool_calls: [],
    invalid_tool_calls: [],
    additional_kwargs: {
      id: "msg_011Z1cgi3gyNGxT55wnRNkXq",
      type: "message",
      role: "assistant",
      model: "claude-3-sonnet-20240229",
      stop_sequence: null,
      usage: { input_tokens: 16, output_tokens: 13 },
      stop_reason: "end_turn"
    },
    response_metadata: {}
  },
  lc_namespace: [ "langchain_core", "messages" ],
  content: "1 + 2 = 3",
  name: undefined,
  additional_kwargs: {
    id: "msg_011Z1cgi3gyNGxT55wnRNkXq",
    type: "message",
    role: "assistant",
    model: "claude-3-sonnet-20240229",
    stop_sequence: null,
    usage: { input_tokens: 16, output_tokens: 13 },
    stop_reason: "end_turn"
  },
  response_metadata: {
    id: "msg_011Z1cgi3gyNGxT55wnRNkXq",
    model: "claude-3-sonnet-20240229",
    stop_sequence: null,
    usage: { input_tokens: 16, output_tokens: 13 },
    stop_reason: "end_turn"
  },
  tool_calls: [],
  invalid_tool_calls: []
}

You can see that we only see events from the chat model run - none from the prompt or broader chain.

Next steps

You’ve now learned how to pass callbacks into a constructor.

Next, check out the other how-to guides in this section, such as how to create your own custom callback handlers.