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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.
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npm install @helicone/helpers
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export HELICONE_API_KEY=<your-helicone-api-key>
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import { HeliconeManualLogger } from "@helicone/helpers";

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