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cURL Manual Logger

You can log custom model calls directly to Helicone using cURL or any HTTP client that can make POST requests.

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Request Structure

A typical request will have the following structure:

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Endpoint

POST https://api.worker.helicone.ai/custom/v1/log

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Headers

NameValue
AuthorizationBearer {API_KEY}
Replace {API_KEY} with your actual Helicone API Key.

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Body

The request body follows this structure: 
export type HeliconeAsyncLogRequest = {
  providerRequest: ProviderRequest;
  providerResponse: ProviderResponse;
  timing?: Timing; // Optional field
};

export type ProviderRequest = {
  url: "custom-model-nopath";
  json: {
    [key: string]: any;
  };
  meta: Record<string, string>;
};

export type ProviderResponse = {
  headers: Record<string, string>;
  status: number;
  json?: {
    [key: string]: any;
  };
  textBody?: string;
};

export type Timing = {
  startTime: {
    seconds: number;
    milliseconds: number;
  };
  endTime: {
    seconds: number;
    milliseconds: number;
  };
  timeToFirstToken?: number;
};

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Example Usage

Here’s a complete example of logging a request to a custom model: 
curl -X POST https://api.worker.helicone.ai/custom/v1/log \
-H "Authorization: Bearer your_api_key" \
-H "Content-Type: application/json" \
-d '{
  "providerRequest": {
    "url": "custom-model-nopath",
    "json": {
      "model": "text-embedding-ada-002",
      "input": "The food was delicious and the waiter was very friendly.",
      "encoding_format": "float"
    },
    "meta": {
      "metaKey1": "metaValue1",
      "metaKey2": "metaValue2"
    }
  },
  "providerResponse": {
    "json": {
      "responseKey1": "responseValue1",
      "responseKey2": "responseValue2"
    },
    "status": 200,
    "headers": {
      "headerKey1": "headerValue1",
      "headerKey2": "headerValue2"
    }
  }
}'
Note: The timing field is optional. If not provided, Helicone will automatically set the current time as both start and end time.

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Token Tracking

Helicone supports token tracking for custom model integrations. To enable this, include a usage object in your providerResponse.json. Here are the supported formats:

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OpenAI-style Format

{
  "providerResponse": {
    "json": {
      "usage": {
        "prompt_tokens": 10,
        "completion_tokens": 20,
        "total_tokens": 30
      }
      // ... rest of your response
    }
  }
}

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Anthropic-style Format

{
  "providerResponse": {
    "json": {
      "usage": {
        "input_tokens": 10,
        "output_tokens": 20
      }
      // ... rest of your response
    }
  }
}

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Google-style Format

{
  "providerResponse": {
    "json": {
      "usageMetadata": {
        "promptTokenCount": 10,
        "candidatesTokenCount": 20,
        "totalTokenCount": 30
      }
      // ... rest of your response
    }
  }
}

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Alternative Format

{
  "providerResponse": {
    "json": {
      "prompt_token_count": 10,
      "generation_token_count": 20
      // ... rest of your response
    }
  }
}
If your model returns token counts in a different format, you can transform the response to match one of these formats before logging to Helicone. If no token information is provided, Helicone will still log the request but token metrics will not be available.

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Advanced Usage

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Adding Custom Properties

You can add custom properties to your requests by including them in the meta field: 
"meta": {
  "Helicone-Property-User-Id": "user-123",
  "Helicone-Property-App-Version": "1.2.3",
  "Helicone-Property-Custom-Field": "custom-value"
}

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Session Tracking

To group requests into sessions, include a session ID in the meta field: 
"meta": {
  "Helicone-Session-Id": "session-123456"
}

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User Tracking

To associate requests with specific users, include a user ID in the meta field: 
"meta": {
  "Helicone-User-Id": "user-123456"
}

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Calculating Timing Information

The timing information is optional but recommended for accurate latency metrics. It should be calculated as follows:
  1. Record the start time before making your request to the LLM provider
  2. Record the end time after receiving the response
  3. Convert these times to Unix epoch format (seconds and milliseconds)
Regional Support: Helicone supports both US and EU regions for caching. In development/preview environments, both regions use the same cache URL, while in production they use region-specific endpoints.
Example in JavaScript: 
const startTime = new Date();
// Make your API call
const endTime = new Date();

const timing = {
  startTime: {
    seconds: Math.floor(startTime.getTime() / 1000),
    milliseconds: startTime.getMilliseconds(),
  },
  endTime: {
    seconds: Math.floor(endTime.getTime() / 1000),
    milliseconds: endTime.getMilliseconds(),
  },
};

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Complete Example with Python Requests

Here’s a complete example using Python’s requests library: 
import requests
import time
import json

# Record start time
start_time = time.time()
start_ms = int((start_time - int(start_time)) * 1000)

# Make your API call to the LLM provider
llm_response = requests.post(
    "https://your-llm-provider.com/generate",
    json={
        "model": "your-model",
        "prompt": "Tell me a story about dragons"
    },
    headers={"Authorization": "Bearer your-provider-api-key"}
)

# Record end time
end_time = time.time()
end_ms = int((end_time - int(end_time)) * 1000)

# Prepare the Helicone log request
helicone_request = {
    "providerRequest": {
        "url": "custom-model-nopath",
        "json": {
            "model": "your-model",
            "prompt": "Tell me a story about dragons"
        },
        "meta": {
            "Helicone-User-Id": "user-123",
            "Helicone-Session-Id": "session-456"
        }
    },
    "providerResponse": {
        "json": llm_response.json(),
        "status": llm_response.status_code,
        "headers": dict(llm_response.headers)
    },
    "timing": {
        "startTime": {
            "seconds": int(start_time),
            "milliseconds": start_ms
        },
        "endTime": {
            "seconds": int(end_time),
            "milliseconds": end_ms
        }
    }
}

# Log to Helicone
helicone_response = requests.post(
    "https://api.worker.helicone.ai/custom/v1/log",
    json=helicone_request,
    headers={
        "Authorization": "Bearer your-helicone-api-key",
        "Content-Type": "application/json"
    }
)

print(f"Helicone logging status: {helicone_response.status_code}")
For more examples and detailed usage, check out our Manual Logger with Streaming cookbook.

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Examples

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Basic Example

curl -X POST https://api.worker.helicone.ai/custom/v1/log \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-helicone-api-key" \
  -d '{
    "providerRequest": {
      "url": "custom-model-nopath",
      "json": {
        "model": "my-custom-model",
        "messages": [
          {
            "role": "user",
            "content": "Hello, world!"
          }
        ]
      },
      "meta": {}
    },
    "providerResponse": {
      "headers": {},
      "status": 200,
      "json": {
        "id": "response-123",
        "choices": [
          {
            "message": {
              "role": "assistant",
              "content": "Hello! How can I assist you today?"
            }
          }
        ],
        "usage": {
          "prompt_tokens": 10,
          "completion_tokens": 8,
          "total_tokens": 18
        }
      }
    },
    "timing": {
      "startTime": {
        "seconds": 1677721748,
        "milliseconds": 123
      },
      "endTime": {
        "seconds": 1677721749,
        "milliseconds": 456
      }
    }
  }'

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String Response Example

You can now log string responses directly using the textBody field: 
curl -X POST https://api.worker.helicone.ai/custom/v1/log \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-helicone-api-key" \
  -d '{
    "providerRequest": {
      "url": "custom-model-nopath",
      "json": {
        "model": "my-custom-model",
        "prompt": "Tell me a joke"
      },
      "meta": {}
    },
    "providerResponse": {
      "headers": {},
      "status": 200,
      "textBody": "Why did the chicken cross the road? To get to the other side!"
    },
    "timing": {
      "startTime": {
        "seconds": 1677721748,
        "milliseconds": 123
      },
      "endTime": {
        "seconds": 1677721749,
        "milliseconds": 456
      }
    }
  }'

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Time to First Token Example

For streaming responses, you can include the time to first token: 
curl -X POST https://api.worker.helicone.ai/custom/v1/log \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-helicone-api-key" \
  -d '{
    "providerRequest": {
      "url": "custom-model-nopath",
      "json": {
        "model": "my-streaming-model",
        "messages": [
          {
            "role": "user",
            "content": "Write a story about a robot"
          }
        ],
        "stream": true
      },
      "meta": {}
    },
    "providerResponse": {
      "headers": {},
      "status": 200,
      "textBody": "Once upon a time, there was a robot named Rusty who dreamed of becoming human..."
    },
    "timing": {
      "startTime": {
        "seconds": 1677721748,
        "milliseconds": 123
      },
      "endTime": {
        "seconds": 1677721749,
        "milliseconds": 456
      },
      "timeToFirstToken": 150
    }
  }'
Note that timeToFirstToken is measured in milliseconds.