Observability & Tracing for Langchain (Python & JS/TS)

pip install langfuse

# Initialize Langfuse handler
from langfuse.callback import CallbackHandler
langfuse_handler = CallbackHandler(
    secret_key="sk-lf-...",
    public_key="pk-lf-...",
    host="https://cloud.langfuse.com", # 🇪🇺 EU region
  # host="https://us.cloud.langfuse.com", # 🇺🇸 US region
)
 
# Your Langchain code
 
# Add Langfuse handler as callback (classic and LCEL)
chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler]})

chain.run(input="<user_input>", callbacks=[langfuse_handler]) # Legacy
conversation.predict(input="<user_input>", callbacks=[langfuse_handler])

pip install langfuse

LANGFUSE_SECRET_KEY="sk-lf-..."
LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_HOST="https://cloud.langfuse.com" # 🇪🇺 EU region
# LANGFUSE_HOST="https://us.cloud.langfuse.com" # 🇺🇸 US region

# Initialize Langfuse handler
from langfuse.callback import CallbackHandler
langfuse_handler = CallbackHandler()
 
# Your Langchain code
 
# Add Langfuse handler as callback (classic and LCEL)
chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler]})

chain.run(input="<user_input>", callbacks=[langfuse_handler]) # Legacy
conversation.predict(input="<user_input>", callbacks=[langfuse_handler])

npm i langfuse-langchain

import { CallbackHandler } from "langfuse-langchain";
// Deno: import CallbackHandler from "https://esm.sh/langfuse-langchain";
 
const langfuseHandler = new CallbackHandler({
  secretKey: "sk-lf-...",
  publicKey: "pk-lf-...",
  baseUrl: "https://cloud.langfuse.com", // 🇪🇺 EU region
  // baseUrl: "https://us.cloud.langfuse.com", // 🇺🇸 US region
});
 
// Your Langchain code
 
// Add Langfuse handler as callback to `run` or `invoke`
await chain.invoke({ input: "<user_input>" }, { callbacks: [langfuseHandler] });

npm i langfuse-langchain

LANGFUSE_SECRET_KEY="sk-lf-..."
LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_BASEURL="https://cloud.langfuse.com" # 🇪🇺 EU region
# LANGFUSE_BASEURL="https://us.cloud.langfuse.com" # 🇺🇸 US region

import { CallbackHandler } from "langfuse-langchain";
// Deno: import CallbackHandler from "https://esm.sh/langfuse-langchain";
 
const langfuseHandler = new CallbackHandler();
 
// Your Langchain code
 
// Add Langfuse handler as callback to `run` or `invoke`
await chain.invoke({ input: "<user_input>" }, { callbacks: [langfuseHandler] });

from langfuse.callback import CallbackHandler
 
langfuse_handler = CallbackHandler()
 
trace_name = "langchain_trace_name"
session_id = "random-session"
user_id = "random-user"
tags = ["random-tag-1", "random-tag-2"]
 
# Your existing Langchain code to create the chain
 
# Pass config to the chain invocation to be parsed as Langfuse trace attributes
 
chain.invoke(
    {"animal": "dog"},
    config={
        "callbacks": [langfuse_handler],
        "run_name": trace_name,
        "tags": tags,
        "metadata": {
            "langfuse_session_id": session_id,
            "langfuse_user_id": user_id,
        },
    },
)

import { CallbackHandler } from "langfuse-langchain";
 
const langfuseHandler = new CallbackHandler();
 
const traceName = "langchain_trace_name";
const sessionId = "random-session";
const userId = "random-user";
const tags = ["random-tag-1", "random-tag-2"];
 
await chain.invoke(
  { animal: "dog" },
  {
    callbacks: [langfuseHandler],
    runName: traceName,
    tags,
    metadata: { langfuseUserId: userId, langfuseSessionId: sessionId },
  }
);

import uuid
 
predefined_run_id = str(uuid.uuid4())
 
langfuse_handler = CallbackHandler()
 
# Your existing Langchain code to create the chain
 
# Pass run_id to the chain invocation
chain.invoke({"person": "Ada Lovelace"}, config={
    "run_id": predefined_run_id,
    "callbacks": [langfuse_handler],
  }
)

import { CallbackHandler } from "langfuse-langchain";
import { v4 as uuidv4 } from "uuid";
 
const langfuseHandler = new CallbackHandler();
 
const predefinedRunId = uuid4();
 
await chain.invoke(
  { animal: "dog" },
  {
    callbacks: [langfuseHandler],
    runId: predefinedRunId,
  }
);

from langfuse import Langfuse
 
langfuse = Langfuse()
 
langfuse.score(
    trace_id=predefined_run_id,
    name="user-feedback",
    value=1,
    comment="This was correct, thank you"
)

import { Langfuse } from "langfuse";
 
const langfuse = new Langfuse();
 
await langfuse.score({
  traceId: predefinedRunId,
  name: "user-feedback",
  value: 1,
  comment: "This was correct, thank you",
});

from langfuse.decorators import langfuse_context, observe
 
# Create a trace via Langfuse decorators and get a Langchain Callback handler for it
@observe() # automtically log function as a trace to Langfuse
def main():
    # update trace attributes (e.g, name, session_id, user_id)
    langfuse_context.update_current_trace(
        name="custom-trace",
        session_id="user-1234",
        user_id="session-1234",
    )
    # get the langchain handler for the current trace
    langfuse_handler = langfuse_context.get_current_langchain_handler()
 
    # Your Langchain code
 
    # Add Langfuse handler as callback (classic and LCEL)
    chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler]})
 
main()

import { CallbackHandler, Langfuse } from "langfuse-langchain";
const langfuse = new Langfuse();
 
// Get Langchain handler for a trace
const trace = langfuse.trace();
const langfuseHandlerTrace = new CallbackHandler({ root: trace });
 
// Add Langfuse handler as callback (classic and LCEL)
await chain.invoke(
  { input: "<user_input>" },
  { callbacks: [langfuseHandlerTrace] }
);
 
// Get Langchain handler for a span
const span = trace.span();
const langfuseHandlerSpan = new CallbackHandler({ root: span });
 
// Add Langfuse handler as callback (classic and LCEL)
await chain.invoke(
  { input: "<user_input>" },
  { callbacks: [langfuseHandlerSpan] }
);

const langfuseHandlerTrace = new CallbackHandler({
  root: trace,
  updateRoot: true,
});

# Intialize Langfuse Client
from langfuse import Langfuse
langfuse = Langfuse()
 
# Get Langchain handler for a trace
trace = langfuse.trace()
langfuse_handler_trace = trace.get_langchain_handler()
 
# Add Langfuse handler as callback (classic and LCEL)
chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler_trace]})
 
# Get Langchain handler for a span
span = trace.span()
langfuse_handler_span = span.get_langchain_handler()
 
# Add Langfuse handler as callback (classic and LCEL)
chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler_span]})

langfuse_handler_trace = trace.get_langchain_handler(update_parent=True)

langfuse_handler.shutdown()

await langfuseHandler.shutdownAsync();

langfuse_handler.flush()

await langfuseHandler.flushAsync();

from langchain_openai import AzureChatOpenAI
 
llm = AzureChatOpenAI(
azure_deployment="my-gpt-4o-deployment",
model="gpt-4o",
)
 

import { AzureChatOpenAI } from "@langchain/openai";
 
const llm = new AzureChatOpenAI({
  azureOpenAIApiDeploymentName: "my-gpt-4o-deployment",
  model: "gpt-4o",
});