How to merge consecutive messages of the same type

The mergeMessageRuns function is available in @langchain/core version 0.2.8 and above.

Certain models do not support passing in consecutive messages of the same type (a.k.a. “runs” of the same message type).

The mergeMessageRuns utility makes it easy to merge consecutive messages of the same type.

Basic usage

import {
  HumanMessage,
  SystemMessage,
  AIMessage,
  mergeMessageRuns,
} from "@langchain/core/messages";

const messages = [
  new SystemMessage("you're a good assistant."),
  new SystemMessage("you always respond with a joke."),
  new HumanMessage({
    content: [{ type: "text", text: "i wonder why it's called langchain" }],
  }),
  new HumanMessage("and who is harrison chasing anyways"),
  new AIMessage(
    'Well, I guess they thought "WordRope" and "SentenceString" just didn\'t have the same ring to it!'
  ),
  new AIMessage(
    "Why, he's probably chasing after the last cup of coffee in the office!"
  ),
];

const merged = mergeMessageRuns(messages);
console.log(
  merged
    .map((x) =>
      JSON.stringify(
        {
          role: x._getType(),
          content: x.content,
        },
        null,
        2
      )
    )
    .join("\n\n")
);

{
  "role": "system",
  "content": "you're a good assistant.\nyou always respond with a joke."
}

{
  "role": "human",
  "content": [
    {
      "type": "text",
      "text": "i wonder why it's called langchain"
    },
    {
      "type": "text",
      "text": "and who is harrison chasing anyways"
    }
  ]
}

{
  "role": "ai",
  "content": "Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn't have the same ring to it!\nWhy, he's probably chasing after the last cup of coffee in the office!"
}

Notice that if the contents of one of the messages to merge is a list of content blocks then the merged message will have a list of content blocks. And if both messages to merge have string contents then those are concatenated with a newline character.

Chaining

mergeMessageRuns can be used in an imperatively (like above) or declaratively, making it easy to compose with other components in a chain:

import { ChatAnthropic } from "@langchain/anthropic";
import { mergeMessageRuns } from "@langchain/core/messages";

const llm = new ChatAnthropic({
  model: "claude-3-sonnet-20240229",
  temperature: 0,
});
// Notice we don't pass in messages. This creates
// a RunnableLambda that takes messages as input
const merger = mergeMessageRuns();
const chain = merger.pipe(llm);
await chain.invoke(messages);

AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: [],
    additional_kwargs: {
      id: 'msg_01LsdS4bjQ3EznH7Tj4xujV1',
      type: 'message',
      role: 'assistant',
      model: 'claude-3-sonnet-20240229',
      stop_reason: 'end_turn',
      stop_sequence: null,
      usage: [Object]
    },
    tool_calls: [],
    usage_metadata: { input_tokens: 84, output_tokens: 3, total_tokens: 87 },
    invalid_tool_calls: [],
    response_metadata: {}
  },
  lc_namespace: [ 'langchain_core', 'messages' ],
  content: [],
  name: undefined,
  additional_kwargs: {
    id: 'msg_01LsdS4bjQ3EznH7Tj4xujV1',
    type: 'message',
    role: 'assistant',
    model: 'claude-3-sonnet-20240229',
    stop_reason: 'end_turn',
    stop_sequence: null,
    usage: { input_tokens: 84, output_tokens: 3 }
  },
  response_metadata: {
    id: 'msg_01LsdS4bjQ3EznH7Tj4xujV1',
    model: 'claude-3-sonnet-20240229',
    stop_reason: 'end_turn',
    stop_sequence: null,
    usage: { input_tokens: 84, output_tokens: 3 }
  },
  id: undefined,
  tool_calls: [],
  invalid_tool_calls: [],
  usage_metadata: { input_tokens: 84, output_tokens: 3, total_tokens: 87 }
}

Looking at the LangSmith trace we can see that before the messages are passed to the model they are merged.

Looking at just the merger, we can see that it’s a Runnable object that can be invoked like all Runnables:

await merger.invoke(messages);

[
  SystemMessage {
    lc_serializable: true,
    lc_kwargs: {
      content: "you're a good assistant.\nyou always respond with a joke.",
      name: undefined,
      additional_kwargs: {},
      response_metadata: {},
      id: undefined
    },
    lc_namespace: [ 'langchain_core', 'messages' ],
    content: "you're a good assistant.\nyou always respond with a joke.",
    name: undefined,
    additional_kwargs: {},
    response_metadata: {},
    id: undefined
  },
  HumanMessage {
    lc_serializable: true,
    lc_kwargs: {
      content: [Array],
      name: undefined,
      additional_kwargs: {},
      response_metadata: {},
      id: undefined
    },
    lc_namespace: [ 'langchain_core', 'messages' ],
    content: [ [Object], [Object] ],
    name: undefined,
    additional_kwargs: {},
    response_metadata: {},
    id: undefined
  },
  AIMessage {
    lc_serializable: true,
    lc_kwargs: {
      content: `Well, I guess they thought "WordRope" and "SentenceString" just didn't have the same ring to it!\n` +
        "Why, he's probably chasing after the last cup of coffee in the office!",
      name: undefined,
      additional_kwargs: {},
      response_metadata: {},
      id: undefined,
      tool_calls: [],
      invalid_tool_calls: [],
      usage_metadata: undefined
    },
    lc_namespace: [ 'langchain_core', 'messages' ],
    content: `Well, I guess they thought "WordRope" and "SentenceString" just didn't have the same ring to it!\n` +
      "Why, he's probably chasing after the last cup of coffee in the office!",
    name: undefined,
    additional_kwargs: {},
    response_metadata: {},
    id: undefined,
    tool_calls: [],
    invalid_tool_calls: [],
    usage_metadata: undefined
  }
]

API reference

For a complete description of all arguments head to the API reference.