LangChain Webhooks & Events

Overview

Implement callback handlers and event-driven patterns for LangChain applications including streaming, webhooks, and real-time updates.

Prerequisites

• LangChain application configured
• Understanding of async programming
• Webhook endpoint (for external integrations)

Instructions

Step 1: Create Custom Callback Handler

from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage
from typing import Any, Dict, List
import httpx

class WebhookCallbackHandler(BaseCallbackHandler):
    """Send events to external webhook."""

    def __init__(self, webhook_url: str):
        self.webhook_url = webhook_url
        self.client = httpx.Client(timeout=10.0)

    def on_llm_start(
        self,
        serialized: Dict[str, Any],
        prompts: List[str],
        **kwargs
    ) -> None:
        """Called when LLM starts."""
        self._send_event("llm_start", {
            "model": serialized.get("name"),
            "prompt_count": len(prompts)
        })

    def on_llm_end(self, response, **kwargs) -> None:
        """Called when LLM completes."""
        self._send_event("llm_end", {
            "generations": len(response.generations),
            "token_usage": response.llm_output.get("token_usage") if response.llm_output else None
        })

    def on_llm_error(self, error: Exception, **kwargs) -> None:
        """Called on LLM error."""
        self._send_event("llm_error", {
            "error_type": type(error).__name__,
            "message": str(error)
        })

    def on_chain_start(
        self,
        serialized: Dict[str, Any],
        inputs: Dict[str, Any],
        **kwargs
    ) -> None:
        """Called when chain starts."""
        self._send_event("chain_start", {
            "chain": serialized.get("name"),
            "input_keys": list(inputs.keys())
        })

    def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:
        """Called when chain completes."""
        self._send_event("chain_end", {
            "output_keys": list(outputs.keys())
        })

    def on_tool_start(
        self,
        serialized: Dict[str, Any],
        input_str: str,
        **kwargs
    ) -> None:
        """Called when tool starts."""
        self._send_event("tool_start", {
            "tool": serialized.get("name"),
            "input_length": len(input_str)
        })

    def _send_event(self, event_type: str, data: Dict[str, Any]) -> None:
        """Send event to webhook."""
        try:
            self.client.post(self.webhook_url, json={
                "event": event_type,
                "data": data,
                "timestamp": datetime.now().isoformat()
            })
        except Exception as e:
            print(f"Webhook error: {e}")

Step 2: Implement Streaming Handler

from langchain_core.callbacks import StreamingStdOutCallbackHandler
import asyncio
from typing import AsyncIterator

class StreamingWebSocketHandler(BaseCallbackHandler):
    """Stream tokens to WebSocket clients."""

    def __init__(self, websocket):
        self.websocket = websocket
        self.queue = asyncio.Queue()

    async def on_llm_new_token(self, token: str, **kwargs) -> None:
        """Called for each new token."""
        await self.queue.put(token)

    async def on_llm_end(self, response, **kwargs) -> None:
        """Signal end of stream."""
        await self.queue.put(None)

    async def stream_tokens(self) -> AsyncIterator[str]:
        """Iterate over streamed tokens."""
        while True:
            token = await self.queue.get()
            if token is None:
                break
            yield token

# FastAPI WebSocket endpoint
from fastapi import WebSocket

@app.websocket("/ws/chat")
async def websocket_chat(websocket: WebSocket):
    await websocket.accept()

    handler = StreamingWebSocketHandler(websocket)
    llm = ChatOpenAI(streaming=True, callbacks=[handler])

    while True:
        data = await websocket.receive_json()

        # Start streaming in background
        asyncio.create_task(chain.ainvoke(
            {"input": data["message"]},
            config={"callbacks": [handler]}
        ))

        # Stream tokens to client
        async for token in handler.stream_tokens():
            await websocket.send_json({"token": token})

Step 3: Server-Sent Events (SSE)

from fastapi import Request
from fastapi.responses import StreamingResponse
from langchain_openai import ChatOpenAI

@app.get("/chat/stream")
async def stream_chat(request: Request, message: str):
    """Stream response using Server-Sent Events."""

    async def event_generator():
        llm = ChatOpenAI(model="gpt-4o-mini", streaming=True)
        prompt = ChatPromptTemplate.from_template("{input}")
        chain = prompt | llm

        async for chunk in chain.astream({"input": message}):
            if await request.is_disconnected():
                break
            yield f"data: {chunk.content}\n\n"

        yield "data: [DONE]\n\n"

    return StreamingResponse(
        event_generator(),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
        }
    )

Step 4: Event Aggregation

from dataclasses import dataclass, field
from datetime import datetime
from typing import List

@dataclass
class ChainEvent:
    event_type: str
    timestamp: datetime
    data: Dict[str, Any]

@dataclass
class ChainTrace:
    chain_id: str
    events: List[ChainEvent] = field(default_factory=list)
    start_time: datetime = None
    end_time: datetime = None

class TraceAggregator(BaseCallbackHandler):
    """Aggregate all events for a chain execution."""

    def __init__(self):
        self.traces: Dict[str, ChainTrace] = {}

    def on_chain_start(self, serialized, inputs, run_id, **kwargs):
        self.traces[str(run_id)] = ChainTrace(
            chain_id=str(run_id),
            start_time=datetime.now()
        )
        self._add_event(run_id, "chain_start", {"inputs": inputs})

    def on_chain_end(self, outputs, run_id, **kwargs):
        self._add_event(run_id, "chain_end", {"outputs": outputs})
        if str(run_id) in self.traces:
            self.traces[str(run_id)].end_time = datetime.now()

    def _add_event(self, run_id, event_type, data):
        trace = self.traces.get(str(run_id))
        if trace:
            trace.events.append(ChainEvent(
                event_type=event_type,
                timestamp=datetime.now(),
                data=data
            ))

    def get_trace(self, run_id: str) -> ChainTrace:
        return self.traces.get(run_id)

Output

• Custom webhook callback handler
• WebSocket streaming implementation
• Server-Sent Events endpoint
• Event aggregation for tracing

Examples

Using Callbacks

from langchain_openai import ChatOpenAI

webhook_handler = WebhookCallbackHandler("https://api.example.com/webhook")

llm = ChatOpenAI(
    model="gpt-4o-mini",
    callbacks=[webhook_handler]
)

# All LLM calls will trigger webhook events
response = llm.invoke("Hello!")

Client-Side SSE Consumption

// JavaScript client
const eventSource = new EventSource('/chat/stream?message=Hello');

eventSource.onmessage = (event) => {
    if (event.data === '[DONE]') {
        eventSource.close();
        return;
    }
    document.getElementById('output').textContent += event.data;
};

Error Handling

Error	Cause	Solution
Webhook Timeout	Slow endpoint	Increase timeout, use async
WebSocket Disconnect	Client closed	Handle disconnect gracefully
Event Queue Full	Too many events	Add queue size limit
SSE Timeout	Long response	Add keep-alive pings

Resources

• LangChain Callbacks
• FastAPI WebSocket
• Server-Sent Events

Next Steps

Use langchain-observability for comprehensive monitoring.