Generic API Callback (Webhook)

Send LiteLLM logs to any HTTP endpoint.

Quick Start

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: openai/gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY

litellm_settings:
  callbacks: ["custom_api_name"]

callback_settings:
  custom_api_name:
    callback_type: generic_api
    endpoint: https://your-endpoint.com/logs
    headers:
      Authorization: Bearer sk-1234

Configuration

Basic Setup

callback_settings:
  <callback_name>:
    callback_type: generic_api
    endpoint: https://your-endpoint.com  # required
    headers:                              # optional
      Authorization: Bearer <token>
      Custom-Header: value
    event_types:                          # optional, defaults to all events
      - llm_api_success
      - llm_api_failure

Parameters

Parameter	Type	Required	Description
callback_type	string	Yes	Must be generic_api
endpoint	string	Yes	HTTP endpoint to send logs to
headers	dict	No	Custom headers for the request
event_types	list	No	Filter events: llm_api_success, llm_api_failure. Defaults to all events.
log_format	string	No	Output format: json_array (default), ndjson, or single. Controls how logs are batched and sent.

Pre-configured Callbacks

Use built-in configurations from generic_api_compatible_callbacks.json:

litellm_settings:
  callbacks: ["rubrik"]  # loads pre-configured settings

callback_settings:
  rubrik:
    callback_type: generic_api
    endpoint: https://your-endpoint.com  # override defaults
    headers:
      Authorization: Bearer ${RUBRIK_API_KEY}

Payload Format

Logs are sent as StandardLoggingPayload objects in JSON format:

[
  {
    "id": "chatcmpl-123",
    "call_type": "litellm.completion",
    "model": "gpt-3.5-turbo",
    "messages": [...],
    "response": {...},
    "usage": {...},
    "cost": 0.0001,
    "startTime": "2024-01-01T00:00:00",
    "endTime": "2024-01-01T00:00:01",
    "metadata": {...}
  }
]

Environment Variables

Set via environment variables instead of config:

export GENERIC_LOGGER_ENDPOINT=https://your-endpoint.com
export GENERIC_LOGGER_HEADERS="Authorization=Bearer token,Custom-Header=value"

Batch Settings

Control batching behavior (inherits from CustomBatchLogger):

callback_settings:
  my_api:
    callback_type: generic_api
    endpoint: https://your-endpoint.com
    batch_size: 100        # default: 100
    flush_interval: 60     # seconds, default: 60

Log Format Options

Control how logs are formatted and sent to your endpoint.

JSON Array (Default)

callback_settings:
  my_api:
    callback_type: generic_api
    endpoint: https://your-endpoint.com
    log_format: json_array  # default if not specified

Sends all logs in a batch as a single JSON array [{log1}, {log2}, ...]. This is the default behavior and maintains backward compatibility.

When to use: Most HTTP endpoints expecting batched JSON data.

NDJSON (Newline-Delimited JSON)

callback_settings:
  my_api:
    callback_type: generic_api
    endpoint: https://your-endpoint.com
    log_format: ndjson

Sends logs as newline-delimited JSON (one record per line):

{log1}
{log2}
{log3}

When to use: Log aggregation services like Sumo Logic, Splunk, or Datadog that support field extraction on individual records.

Benefits:

• Each log is ingested as a separate message
• Field Extraction Rules work at ingest time
• Better parsing and querying performance

Single

callback_settings:
  my_api:
    callback_type: generic_api
    endpoint: https://your-endpoint.com
    log_format: single

Sends each log as an individual HTTP request in parallel when the batch is flushed.

When to use: Endpoints that expect individual records, or when you need maximum compatibility.

Note: This mode sends N HTTP requests per batch (more overhead). Consider using ndjson instead if your endpoint supports it.