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POST
/
api
/
traces
/
ingest
[
  {
    "id": "log_id_123",
    "log_type": "chat",
    "trace_unique_id": "trace_abc123",
    "span_unique_id": "span_xyz789",
    "span_name": "llm_call",
    "span_parent_id": null,
    "span_workflow_name": "customer_support",
    "input": "[{\"role\":\"user\",\"content\":\"Hello\"}]",
    "output": "{\"role\":\"assistant\",\"content\":\"Hi! How can I help?\"}",
    "model": "gpt-4o-mini",
    "usage": {
      "prompt_tokens": 10,
      "completion_tokens": 8,
      "total_tokens": 18
    },
    "timestamp": "2024-01-15T10:30:00Z",
    "start_time": "2024-01-15T10:29:50Z",
    "latency": 1.2,
    "status": "success"
  }
]

Documentation Index

Fetch the complete documentation index at: https://docs.keywordsai.co/llms.txt

Use this file to discover all available pages before exploring further.

Ingest trace data as logs. This endpoint allows you to convert trace data into log format for storage and analysis using the universal input/output design.

​
Universal input/output support

This endpoint accepts logs with:
  • input: Any JSON-serializable structure (string, object, array)
  • output: Any JSON-serializable structure (string, object, array)
  • log_type: Determines how input/output are interpreted ("chat", "embedding", "workflow", "task", etc.)
The system automatically extracts type-specific fields based on the log_type. See log types for complete specifications and the Create Log documentation for all field details.

​
Authentication

All endpoints require API key authentication: 
Authorization: Bearer YOUR_API_KEY

​
Request body

The request body should be an array of log objects. Each log object should contain trace and span information. 
[
  {
    "id": "log_id_123",
    "log_type": "chat",
    "trace_unique_id": "trace_abc123",
    "span_unique_id": "span_xyz789",
    "span_name": "llm_call",
    "span_parent_id": null,
    "span_workflow_name": "customer_support",
    "input": "[{\"role\":\"user\",\"content\":\"Hello\"}]",
    "output": "{\"role\":\"assistant\",\"content\":\"Hi! How can I help?\"}",
    "model": "gpt-4o-mini",
    "usage": {
      "prompt_tokens": 10,
      "completion_tokens": 8,
      "total_tokens": 18
    },
    "timestamp": "2024-01-15T10:30:00Z",
    "start_time": "2024-01-15T10:29:50Z",
    "latency": 1.2,
    "status": "success"
  }
]

​
Examples

import requests

url = "https://api.keywordsai.co/api/traces/ingest/"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

logs = [
    {
        "id": "log_id_123",
        "log_type": "chat",
        "trace_unique_id": "trace_abc123",
        "span_unique_id": "span_xyz789",
        "span_name": "llm_call",
        "span_workflow_name": "customer_support",
        "input": '[{"role":"user","content":"Hello"}]',
        "output": '{"role":"assistant","content":"Hi! How can I help?"}',
        "model": "gpt-4o-mini",
        "usage": {
            "prompt_tokens": 10,
            "completion_tokens": 8,
            "total_tokens": 18
        },
        "timestamp": "2024-01-15T10:30:00Z",
        "start_time": "2024-01-15T10:29:50Z",
        "latency": 1.2
    }
]

response = requests.post(url, headers=headers, json=logs)
print(response.json())

​
Response

200 OK
{
  "success": true,
  "ingested_count": 1
}