How to use callbacks in async environments

Prerequisites

This guide assumes familiarity with the following concepts:

• Callbacks
• Custom callback handlers

If you are planning to use the async APIs, it is recommended to use and extend AsyncCallbackHandler to avoid blocking the event.

danger

If you use a sync CallbackHandler while using an async method to run your LLM / Chain / Tool / Agent, it will still work. However, under the hood, it will be called with run_in_executor which can cause issues if your CallbackHandler is not thread-safe.

danger

If you're on python<=3.10, you need to remember to propagate config or callbacks when invoking other runnable from within a RunnableLambda, RunnableGenerator or @tool. If you do not do this, the callbacks will not be propagated to the child runnables being invoked.

import asyncio
from typing import Any, Dict, List

from langchain_anthropic import ChatAnthropic
from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler
from langchain_core.messages import HumanMessage
from langchain_core.outputs import LLMResult


class MyCustomSyncHandler(BaseCallbackHandler):
    def on_llm_new_token(self, token: str, **kwargs) -> None:
        print(f"Sync handler being called in a `thread_pool_executor`: token: {token}")


class MyCustomAsyncHandler(AsyncCallbackHandler):
    """Async callback handler that can be used to handle callbacks from langchain."""

    async def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> None:
        """Run when chain starts running."""
        print("zzzz....")
        await asyncio.sleep(0.3)
        class_name = serialized["name"]
        print("Hi! I just woke up. Your llm is starting")

    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        """Run when chain ends running."""
        print("zzzz....")
        await asyncio.sleep(0.3)
        print("Hi! I just woke up. Your llm is ending")


# To enable streaming, we pass in `streaming=True` to the ChatModel constructor
# Additionally, we pass in a list with our custom handler
chat = ChatAnthropic(
    model="claude-3-sonnet-20240229",
    max_tokens=25,
    streaming=True,
    callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],
)

await chat.agenerate([[HumanMessage(content="Tell me a joke")]])

API Reference:ChatAnthropic | AsyncCallbackHandler | BaseCallbackHandler | HumanMessage | LLMResult

zzzz....
Hi! I just woke up. Your llm is starting
Sync handler being called in a `thread_pool_executor`: token: Here
Sync handler being called in a `thread_pool_executor`: token: 's
Sync handler being called in a `thread_pool_executor`: token:  a
Sync handler being called in a `thread_pool_executor`: token:  little
Sync handler being called in a `thread_pool_executor`: token:  joke
Sync handler being called in a `thread_pool_executor`: token:  for
Sync handler being called in a `thread_pool_executor`: token:  you
Sync handler being called in a `thread_pool_executor`: token: :
Sync handler being called in a `thread_pool_executor`: token: 

Why
Sync handler being called in a `thread_pool_executor`: token:  can
Sync handler being called in a `thread_pool_executor`: token: 't
Sync handler being called in a `thread_pool_executor`: token:  a
Sync handler being called in a `thread_pool_executor`: token:  bicycle
Sync handler being called in a `thread_pool_executor`: token:  stan
Sync handler being called in a `thread_pool_executor`: token: d up
Sync handler being called in a `thread_pool_executor`: token:  by
Sync handler being called in a `thread_pool_executor`: token:  itself
Sync handler being called in a `thread_pool_executor`: token: ?
Sync handler being called in a `thread_pool_executor`: token:  Because
Sync handler being called in a `thread_pool_executor`: token:  it
Sync handler being called in a `thread_pool_executor`: token: 's
Sync handler being called in a `thread_pool_executor`: token:  two
Sync handler being called in a `thread_pool_executor`: token: -
Sync handler being called in a `thread_pool_executor`: token: tire
zzzz....
Hi! I just woke up. Your llm is ending

LLMResult(generations=[[ChatGeneration(text="Here's a little joke for you:\n\nWhy can't a bicycle stand up by itself? Because it's two-tire", message=AIMessage(content="Here's a little joke for you:\n\nWhy can't a bicycle stand up by itself? Because it's two-tire", id='run-8afc89e8-02c0-4522-8480-d96977240bd4-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('8afc89e8-02c0-4522-8480-d96977240bd4'))])

Next steps

You've now learned how to create your own custom callback handlers.

Next, check out the other how-to guides in this section, such as how to attach callbacks to a runnable.