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The Logger SDK allows you to log any custom operation to Helicone - database queries, API calls, ML inference, file processing, or any other operation you want to track.
1

To get started, install the @helicone/helpers package

npm install @helicone/helpers
2

Set up your Helicone API key in your .env file

Log into Helicone or create an account. Once you have an account, you can generate an API key here.
export HELICONE_API_KEY=<your-helicone-api-key>
3

Create a new HeliconeManualLogger instance

import { HeliconeManualLogger } from "@helicone/helpers";

const heliconeLogger = new HeliconeManualLogger({
  apiKey: process.env.HELICONE_API_KEY,
  headers: {} // Additional headers sent with the request (optional)
});
4

Log your request

The logRequest method takes three parameters:
  1. Request data: What you’re logging (query, operation name, etc.)
  2. Operation function: The actual work being done
  3. Headers: Optional custom properties or session tracking
const result = await heliconeLogger.logRequest(
  // 1. What you're logging
  {
    _type: "data",
    name: "user_query",
    query: "SELECT * FROM users WHERE active = true",
    database: "production"
  },
  // 2. The actual operation
  async (resultRecorder) => {
    const queryResult = await database.query(
      "SELECT * FROM users WHERE active = true"
    );

    // Record the results
    resultRecorder.appendResults({
      _type: "data",
      name: "user_query",
      status: "success",
      data: queryResult.rows,
      count: queryResult.rows.length
    });

    return queryResult;
  },
  // 3. Optional: session tracking or custom properties
  {
    "Helicone-Property-Session": "user-123",
    "Helicone-Property-Environment": "production"
  }
);
5

Verify your requests in Helicone

With the above setup, any calls to any data operation will automatically be logged and monitored by Helicone. Review them in your Helicone dashboard.

​
Understanding the Structure

All custom logs follow the same pattern with two parts:

​
Request Data

What you’re about to do. Must include:
  • _type: "data" - Identifies this as a custom data log
  • name - A descriptive name for your operation
  • Any custom fields you want to track (query, endpoint, model, etc.)

​
Response Data

What happened. Should include:
  • _type: "data" - Identifies this as a custom data response
  • name - Same name as the request
  • status - Success or error state
  • Any result data you want to track

​
More Examples

​
API Call

await heliconeLogger.logRequest(
  {
    _type: "data",
    name: "external_api_call",
    endpoint: "https://api.example.com/users",
    method: "GET"
  },
  async (resultRecorder) => {
    const response = await fetch("https://api.example.com/users?limit=10");
    const data = await response.json();

    resultRecorder.appendResults({
      _type: "data",
      name: "external_api_call",
      status: "success",
      result: data
    });

    return data;
  }
);

​
ML Model Inference

await heliconeLogger.logRequest(
  {
    _type: "data",
    name: "ml_inference",
    model: "custom-classifier-v2",
    input_features: { text: "This is a sample text" }
  },
  async (resultRecorder) => {
    const prediction = await customModel.predict({
      text: "This is a sample text",
      threshold: 0.8
    });

    resultRecorder.appendResults({
      _type: "data",
      name: "ml_inference",
      status: "success",
      result: {
        classification: prediction.classification,
        confidence: prediction.confidence
      }
    });

    return prediction;
  }
);
For more examples, check out our GitHub examples.
To learn more about the HeliconeManualLogger API, see the API Reference here.
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