> ## Documentation Index
> Fetch the complete documentation index at: https://chainlit-5-wd-prompt.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Langchain

The Langchain integration enables to monitor your Langchain agents and chains with a single line of code.

<Note>You should create a new instance of the callback handler for each invocation.</Note>

<CodeGroup>
  ```python Python
  import os
  from literalai import LiteralClient

  from langchain_openai import ChatOpenAI
  from langchain.schema.runnable.config import RunnableConfig
  from langchain.schema import StrOutputParser
  from langchain.prompts import ChatPromptTemplate

  literal_client = LiteralClient(api_key=os.getenv("LITERAL_API_KEY"))

  cb = client.langchain_callback()

  prompt = ChatPromptTemplate.from_messages(
      ['human', 'Tell me a short joke about {topic}']
  )

  model = ChatOpenAI(streaming=True)
  runnable = prompt | model | StrOutputParser()

  res = runnable.invoke(
      {"topic": "ice cream"},
      config=RunnableConfig(callbacks=[cb], run_name="joke")
      )
  ```

  ```typescript TypeScript
  import { LiteralClient } from '@literalai/client';

  import { StringOutputParser } from '@langchain/core/output_parsers';
  import { ChatPromptTemplate } from '@langchain/core/prompts';
  import { ChatOpenAI } from '@langchain/openai';

  const client = new LiteralClient(process.env['LITERAL_API_KEY']); // This is the default and can be omitted

  const cb = client.instrumentation.langchain.literalCallback();

  // Example of using the callback
  const prompt = ChatPromptTemplate.fromMessages([
      ['human', 'Tell me a short joke about {topic}']
    ]);

  const model = new ChatOpenAI({});
  const outputParser = new StringOutputParser();

  const chain = prompt.pipe(model).pipe(outputParser);

  const response = await chain.invoke(
  {
      topic: 'ice cream'
  },
  {
      runName: 'joke',
      callbacks: [cb]
  }
  );
  ```
</CodeGroup>

## Multiple langchain calls in a single thread

You can combine the Langchain callback handler with the concept of [Thread](/concepts/thread) to monitor multiple langchain calls in a single thread.

<CodeGroup>
  ```python Python
  import os
  from literalai import LiteralClient

  literal_client = LiteralClient(api_key=os.getenv("LITERAL_API_KEY"))

  with literal_client.thread(name="Langchain example") as thread:
      cb = client.langchain_callback()
      # Call your Langchain agent here
  ```

  ```typescript TypeScript
  import { LiteralClient } from '@literalai/client';

  const client = new LiteralClient(process.env['LITERAL_API_KEY']); // This is the default and can be omitted

  const thread = await client.thread({ name: "Langchain Example" }).upsert();
  const cb = client.instrumentation.langchain.literalCallback(thread.id);

  // Call your Langchain agent here
  ```
</CodeGroup>