Create LLM Monitoring Application¶

1. Go to Agent Monitoring > Application List.
2. Click "Create Application".
3. Select the LLM Monitoring Application Tab.

Integration Types¶

Upon entering the create application page, it currently supports Integrating Langfuse by default:

Access Configuration¶

Three types are supported here:

Access Python

Access JS/TS

Access Other Models/Frameworks

Access Python¶

1. Install Dependencies¶

pip install langfuse

2. Initialize Client¶

LANGFUSE_PUBLIC_KEY="app_id"
LANGFUSE_SECRET_KEY="llm_bb0f30d.....66eec"
LANGFUSE_HOST="https://llm-openway.guance.com"

from langfuse import Langfuse
langfuse = Langfuse()

from langfuse import Langfuse
langfuse = Langfuse(
  public_key="app_id",
  secret_key="llm_bb0f30d.....66eec",
  host="https://llm-openway.guance.com"
)

3. Verify Connection¶

You can use langfuse.auth_check() to verify if the connection is successful. It is not recommended to use this in production as it adds latency to your application.

from langfuse import Langfuse
# Initialize (via parameter construction)
langfuse = Langfuse(
    public_key="app_id",
    secret_key="llm_d9f06c320a9***6e9807",
    host="https://llm-openway.guance.com"
)
# Verify connection. This is a synchronous call, not recommended for production. This method is only for verifying configuration.
if langfuse.auth_check():
    print("Langfuse client is authenticated and ready!")

If the connection fails, you might encounter the following error:

langfuse.api.resources.commons.errors.unauthorized_error.UnauthorizedError: status_code: 401, body: {}

This error usually indicates that the App ID or Client Token is incorrect. Please check your parameter configuration.

For more details and end-to-end examples, refer to Python.

Access JS/TS¶

1. Install Dependencies¶

npm install @langfuse/tracing @langfuse/otel @opentelemetry/sdk-node

Click to view more package information.

2. Register Credentials¶

Set the following environment variables in your application environment. Ensure you have a .env file in your project root and a package like dotenv to load the variables.

LANGFUSE_SECRET_KEY="llm_122f6b85d0c7454fa0f9*****d360"
LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_BASE_URL="https://llm-openway.guance.com",

3. Initialize OpenTelemetry¶

Langfuse SDK tracing is built on OpenTelemetry, so you need to set up the OpenTelemetry SDK. The LangfuseSpanProcessor is the key component for sending traces to Langfuse.

import { NodeSDK } from "@opentelemetry/sdk-node";
import { LangfuseSpanProcessor } from "@langfuse/otel";
const sdk = new NodeSDK({
  spanProcessors: [new LangfuseSpanProcessor()],
});
sdk.start();

Click to view the Ollama integration example.

For more details and end-to-end examples, refer to JS/TS.

Access Others¶

If you need to integrate other models/frameworks, such as: DeepSeek, Ollama, LangChain, Dify, etc., refer to the Langfuse official documentation for configuration.

Fill in Variables in Code¶

Fill in the following information on the right side of the page:

After confirming the information is correct, click Create to complete the LLM monitoring application integration.