Direct API

Overview

Send data directly to the Moda ingest API. This supports LLM conversations across any channel:

Standard chat completions (OpenAI, Anthropic, etc.)
Chat/messaging platforms (Slack, Discord, etc.)
Email conversations
Voice and video call transcripts
Tool/function calls within conversations
Custom integrations and languages without Moda SDK support

All events are processed by Moda and appear in your dashboard alongside SDK-ingested data.

Endpoint

POST https://moda-ingest.modas.workers.dev/v1/ingest

Authentication

Include your Moda API key in the Authorization header:

-H "Authorization: Bearer YOUR_MODA_API_KEY"

Request format

You can set the environment at the request level to apply to all events:

{
  "environment": "staging",
  "events": [...]
}

Send an array of events in the request body:

{
  "events": [
    {
      "conversation_id": "conv-123",
      "role": "user",
      "message": "What is the capital of France?",
      "timestamp": "2024-01-15T10:30:00Z"
    },
    {
      "conversation_id": "conv-123",
      "role": "assistant",
      "message": "The capital of France is Paris.",
      "timestamp": "2024-01-15T10:30:01Z",
      "input_tokens": 12,
      "output_tokens": 8,
      "model": "gpt-4o",
      "provider": "openai"
    }
  ]
}

Event fields

Required fields

Field	Type	Description
`conversation_id`	string	Unique ID for the conversation
`role`	string	One of: `user`, `assistant`, `system`
`message` or `content`	string (or array for `content`)	The message content. Provide either `message` (string) or `content` (string or array of content blocks).

Optional fields

Field	Type	Description
`timestamp`	string	ISO 8601 timestamp (defaults to now)
`trace_id`	string	For linking related events (defaults to conversation_id)
`user_id`	string	Identifier for the end user
`input_tokens`	number	Number of input/prompt tokens used
`output_tokens`	number	Number of output/completion tokens used
`reasoning_tokens`	number	Tokens used for extended thinking (Claude models)
`model`	string	Model name (e.g., `gpt-4o`, `claude-3-opus`)
`provider`	string	Provider name (e.g., `openai`, `anthropic`)
`environment`	string	Environment name: `development`, `staging`, or `production` (defaults to `production`). Can also be set at the request level.
`prompt_id`	string	Prompt template ID (for prompt management tracking)
`prompt_name`	string	Prompt template name
`prompt_version`	string	Prompt template version
`content_blocks`	array	Structured content blocks (see below)

When using content as an array of content blocks, text is automatically extracted from text-type blocks for search and analytics. If both message and content are provided, message takes precedence.

Content blocks

For conversations with tool use, extended thinking, or images, include structured content blocks:

Block Type	Fields	Description
`text`	`text`	Plain text content
`thinking`	`text`	Model reasoning (extended thinking)
`tool_use`	`tool_name`, `tool_use_id`, `input`	Tool/function call
`tool_result`	`tool_use_id`, `content`, `is_error`	Tool response
`image`	`source`	Image (base64 or URL)

Example with tool use:

{
  "events": [{
    "conversation_id": "conv-123",
    "role": "assistant",
    "message": "Let me search for that.",
    "content_blocks": [
      {"type": "text", "text": "Let me search for that."},
      {"type": "tool_use", "tool_name": "web_search", "tool_use_id": "toolu_abc", "input": {"query": "latest news"}}
    ]
  }]
}

Example with extended thinking:

{
  "events": [{
    "conversation_id": "conv-123",
    "role": "assistant",
    "message": "The answer is 42.",
    "reasoning_tokens": 150,
    "output_tokens": 10,
    "content_blocks": [
      {"type": "thinking", "text": "Let me think through this step by step..."},
      {"type": "text", "text": "The answer is 42."}
    ]
  }]
}

Channel-specific event formats

LLM conversations happen across many channels beyond standard chat completions. Use the messageType field to send conversations from messaging platforms, email, voice calls, and to log tool invocations. You can mix these with standard events in the same request.

Channel messages

LLM-powered conversations in chat platforms or messaging systems.

{
  "messageType": "channel",
  "id": "msg-123",
  "conversationId": "thread-456",
  "message": "Hello, how can I help you today?",
  "role": "assistant",
  "userId": "user-789",
  "timestamp": "2025-01-04T12:00:00Z"
}

Field	Type	Required	Description
`messageType`	string	Yes	Must be `"channel"`
`id`	string	Yes	Unique message ID
`conversationId`	string	Yes	Thread/channel ID for grouping messages
`message`	string	Yes	Message content
`role`	string	Yes	One of: `user`, `assistant`, `system`
`userId`	string	No	User identifier
`timestamp`	string	No	ISO 8601 timestamp (defaults to now)
`metadata`	object	No	Custom metadata

Tool calls

Tool and function invocations made by the LLM during a conversation, with full request and response data.

{
  "messageType": "tool_call",
  "id": "tool-123",
  "conversationId": "thread-456",
  "name": "search_knowledge_base",
  "toolCallRequest": {
    "query": "refund policy",
    "limit": 5
  },
  "toolCallResponse": {
    "results": [
      {"title": "Refund Policy", "content": "..."}
    ]
  }
}

Field	Type	Required	Description
`messageType`	string	Yes	Must be `"tool_call"`
`id`	string	Yes	Unique tool call ID
`conversationId`	string	Yes	Conversation ID for grouping
`name`	string	Yes	Tool/function name
`toolCallRequest`	any	No	Request payload (can be any JSON)
`toolCallResponse`	any	No	Response payload (can be any JSON)
`userId`	string	No	User identifier
`timestamp`	string	No	ISO 8601 timestamp
`metadata`	object	No	Custom metadata

Emails

LLM-powered conversations happening over email, with threading support via conversationId and optional inReplyTo for referencing specific parent emails.

{
  "messageType": "email",
  "id": "email-123",
  "conversationId": "conv-456",
  "role": "user",
  "subject": "Re: Order #12345 - Shipping question",
  "body": "Thank you for reaching out. Your order is scheduled to arrive...",
  "inReplyTo": "parent-email-message-id",
  "from": "support@example.com",
  "to": ["customer@example.com"]
}

Field	Type	Required	Description
`messageType`	string	Yes	Must be `"email"`
`id`	string	Yes	Unique email ID
`conversationId`	string	Yes	Thread/conversation ID (groups emails together)
`role`	string	Yes	`"user"` (inbound) or `"assistant"` (outbound)
`body`	string	Yes	Email body content
`inReplyTo`	string	No	Email thread reference (e.g. parent email Message-ID)
`subject`	string	No	Email subject line
`from`	string	No	Sender email address
`to`	array	No	Recipient email addresses
`userId`	string	No	User identifier
`timestamp`	string	No	ISO 8601 timestamp

The conversationId field groups emails into conversations, consistent with all other event types. Use inReplyTo optionally to reference specific parent emails within a conversation (e.g. when there are multiple email threads within the same conversation).

Call transcripts

LLM-powered conversations happening over phone or video calls, with full speaker turn transcripts.

{
  "messageType": "call",
  "id": "call-123",
  "conversationId": "call-session-456",
  "transcript": [
    {"role": "user", "content": "Hi, I'm having trouble with my account."},
    {"role": "assistant", "content": "I'd be happy to help. Can you tell me more about the issue?"},
    {"role": "user", "content": "I can't log in after resetting my password."}
  ],
  "duration": 180,
  "callType": "phone"
}

Field	Type	Required	Description
`messageType`	string	Yes	Must be `"call"`
`id`	string	Yes	Unique call ID
`conversationId`	string	Yes	Call session ID
`transcript`	array	Yes	Array of transcript entries (see below)
`duration`	number	No	Call duration in seconds
`callType`	string	No	One of: `phone`, `video`, `voice`
`userId`	string	No	User identifier
`timestamp`	string	No	ISO 8601 timestamp
`metadata`	object	No	Custom metadata

Transcript entry format:

Field	Type	Required	Description
`role`	string	Yes	Speaker role (e.g., `user`, `assistant`, `customer`, `agent`)
`content`	string	Yes	What was said
`timestamp`	string	No	When this was said

Call transcripts are stored as a single record containing the full transcript. The complete transcript is searchable in the Moda dashboard, and the structured speaker turns are preserved for detailed analysis.

Mixed batch example

You can mix standard events and channel-specific events in the same request:

{
  "events": [
    {
      "conversation_id": "conv-123",
      "role": "user",
      "message": "What is the weather?"
    },
    {
      "messageType": "channel",
      "id": "msg-001",
      "conversationId": "support-thread-456",
      "message": "I need help with my subscription",
      "role": "user",
      "userId": "customer-789"
    },
    {
      "messageType": "tool_call",
      "id": "tool-001",
      "conversationId": "support-thread-456",
      "name": "get_subscription",
      "toolCallRequest": {"user_id": "customer-789"},
      "toolCallResponse": {"plan": "pro", "status": "active"}
    }
  ]
}

Each event is validated individually. Standard events (without messageType) use the fields documented above, while channel-specific events are validated according to their messageType schema.

Example

curl https://moda-ingest.modas.workers.dev/v1/ingest \
  -H "Authorization: Bearer YOUR_MODA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "events": [
      {
        "conversation_id": "conv-abc123",
        "role": "user",
        "message": "Hello, how are you?"
      },
      {
        "conversation_id": "conv-abc123",
        "role": "assistant",
        "message": "I am doing well, thank you for asking!",
        "input_tokens": 8,
        "output_tokens": 12,
        "model": "gpt-4o",
        "provider": "openai"
      }
    ]
  }'

import requests

response = requests.post(
    "https://moda-ingest.modas.workers.dev/v1/ingest",
    headers={
        "Authorization": "Bearer YOUR_MODA_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "events": [
            {
                "conversation_id": "conv-abc123",
                "role": "user",
                "message": "Hello, how are you?"
            },
            {
                "conversation_id": "conv-abc123",
                "role": "assistant",
                "message": "I am doing well, thank you for asking!",
                "input_tokens": 8,
                "output_tokens": 12,
                "model": "gpt-4o",
                "provider": "openai"
            }
        ]
    }
)

print(response.json())

const response = await fetch('https://moda-ingest.modas.workers.dev/v1/ingest', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_MODA_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    events: [
      {
        conversation_id: 'conv-abc123',
        role: 'user',
        message: 'Hello, how are you?'
      },
      {
        conversation_id: 'conv-abc123',
        role: 'assistant',
        message: 'I am doing well, thank you for asking!',
        input_tokens: 8,
        output_tokens: 12,
        model: 'gpt-4o',
        provider: 'openai'
      }
    ]
  })
});

console.log(await response.json());

Response

Success response

{
  "success": true,
  "count": 2,
  "requestId": "550e8400-e29b-41d4-a716-446655440000"
}

When the request includes channel-specific events (with messageType), the response includes a details breakdown:

{
  "success": true,
  "count": 4,
  "requestId": "550e8400-e29b-41d4-a716-446655440000",
  "details": {
    "channel": 2,
    "tool_call": 1,
    "email": 0,
    "call": 1,
    "call_transcript_messages": 0
  }
}

Error response

{
  "success": false,
  "count": 0,
  "message": "Invalid or missing API key",
  "requestId": "550e8400-e29b-41d4-a716-446655440000",
  "retryable": false
}

Field	Type	Description
`success`	boolean	Whether the request succeeded
`count`	number	Number of events processed
`requestId`	string	Unique request ID for debugging
`message`	string	Error message (on failure)
`retryable`	boolean	Whether the error is temporary and should be retried
`details`	object	Breakdown by channel event type (only present when channel-specific events are included)

Batch limits

Limit	Value
Max events per request	1,000
Max message size	100 KB
Max request size	5 MB

Error handling

Status	Meaning	Retryable
200	Success	-
400	Invalid request format	No
401	Invalid or missing API key	No
413	Request too large	No
503	Service temporarily unavailable	Yes

For 503 errors, use exponential backoff when retrying. Start with 1 second and double each retry, up to a maximum of 30 seconds.

Getting Started

Ingestion

Frameworks

Prompt Management

Harness

Data API

Overview

Endpoint

Authentication

Request format

Event fields

Required fields

Optional fields

Content blocks

Channel-specific event formats

Channel messages

Tool calls

Emails

Call transcripts

Mixed batch example

Example

Response

Success response

Error response

Batch limits

Error handling

​Overview

​Endpoint

​Authentication

​Request format

​Event fields

​Required fields

​Optional fields

​Content blocks

​Channel-specific event formats

​Channel messages

​Tool calls

​Emails

​Call transcripts

​Mixed batch example

​Example

​Response

​Success response

​Error response

​Batch limits

​Error handling

Overview

Endpoint

Authentication

Request format

Event fields

Required fields

Optional fields

Content blocks

Channel-specific event formats

Channel messages

Tool calls

Emails

Call transcripts

Mixed batch example

Example

Response

Success response

Error response

Batch limits

Error handling