> ## Documentation Index
> Fetch the complete documentation index at: https://docs.minns.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Process event

> Ingest a new event and trigger automatic graph construction, episode detection, and claim extraction.

## Request body

<ParamField body="event" type="Event" required>
  Complete event object. See [Types & primitives](/api-reference/types) for the full schema.
</ParamField>

<ParamField body="enable_semantic" type="boolean" default="false">
  Enable semantic processing (NER, claims, embeddings). Set to `true` to extract claims from event content.
</ParamField>

### Minimal example (server generates ID and timestamp)

```json theme={null}
{
  "event": {
    "agent_id": 1,
    "agent_type": "movie-bot",
    "session_id": 5001,
    "event_type": {
      "Communication": {
        "message_type": "user_message",
        "sender": 0,
        "recipient": 1,
        "content": { "text": "I want to book Interstellar for tonight." }
      }
    },
    "context": {
      "active_goals": [
        {
          "id": 101,
          "description": "book_movie",
          "priority": 0.9,
          "progress": 0.1,
          "subgoals": []
        }
      ],
      "environment": {
        "variables": { "user_id": "user_99" },
        "temporal": {
          "deadlines": [],
          "patterns": []
        }
      },
      "resources": {
        "computational": {
          "cpu_percent": 10.0,
          "memory_bytes": 1024,
          "storage_bytes": 1024,
          "network_bandwidth": 100
        },
        "external": {}
      }
    },
    "metadata": { "user_id": { "String": "user_99" } }
  },
  "enable_semantic": true
}
```

### Full example (client provides all fields)

```json theme={null}
{
  "event": {
    "id": 123456789012345678901234567890,
    "timestamp": 1738425600000000000,
    "agent_id": 1,
    "agent_type": "movie-bot",
    "session_id": 5001,
    "event_type": {
      "Communication": {
        "message_type": "user_message",
        "sender": 0,
        "recipient": 1,
        "content": { "text": "I want to book Interstellar for tonight." }
      }
    },
    "causality_chain": [],
    "context": {
      "active_goals": [
        {
          "id": 101,
          "description": "book_movie",
          "priority": 0.9,
          "progress": 0.1,
          "deadline": null,
          "subgoals": []
        }
      ],
      "environment": {
        "variables": { "user_id": "user_99" },
        "spatial": null,
        "temporal": {
          "time_of_day": null,
          "deadlines": [],
          "patterns": []
        }
      },
      "resources": {
        "computational": {
          "cpu_percent": 10.0,
          "memory_bytes": 1024,
          "storage_bytes": 1024,
          "network_bandwidth": 100
        },
        "external": {}
      },
      "fingerprint": 0,
      "embeddings": null
    },
    "metadata": { "user_id": { "String": "user_99" } },
    "context_size_bytes": 0,
    "segment_pointer": null
  },
  "enable_semantic": true
}
```

## Response

<ResponseField name="success" type="boolean">
  Whether processing succeeded.
</ResponseField>

<ResponseField name="nodes_created" type="integer">
  Number of graph nodes created.
</ResponseField>

<ResponseField name="patterns_detected" type="integer">
  Number of patterns detected.
</ResponseField>

<ResponseField name="processing_time_ms" type="integer">
  Processing time in milliseconds.
</ResponseField>

```json theme={null}
{
  "success": true,
  "nodes_created": 5,
  "patterns_detected": 2,
  "processing_time_ms": 15
}
```