set_data_collection

set_data_collection_result(context: dict, key: str, value: str) -> dict

Stores a collected data result on the conversation — the same store the AI data collection feature writes to. Results appear in the conversation sidebar, Metrics → Custom Data charts, inbox "Collected Data" filters, and campaign analytics. If the key already exists, its value is replaced.

Use this for facts you want to measure across conversations (outcomes, reasons, feedback categories). For values your own flows read back later, use set_conversation_metadata instead.

Parameters

Name	Type	Description
`context`	`dict`	The context object passed to your action or event handler
`key`	`str`	The result key (max 200 characters), e.g. `"cancel_reason"`
`value`	`str`	The value to store (max 2,000 characters), e.g. `"price"`

Returns

dict — {"success": bool, "message": str, "key": str, "value": str}

Limits

Maximum key length: 200 characters
Maximum value length: 2,000 characters
Maximum 50 keys per conversation

Behavior

One value per key: writing an existing key replaces its value.
Values are strings; store booleans as "true"/"false".
On phone campaign calls, the success key ("true"/"false") drives the campaign's success-rate KPI.
Results sync to your connected helpdesk (Zendesk/HelpScout) as octocom:key:value tags, same as AI-collected results.
In test/mock environments, returns a mock success response without persisting.

Examples

Record a campaign call outcome from an event handler

def handle_event(context):
    # ... inspect context["conversation"]["messages"] ...
    set_data_collection_result(context, "success", "true")
    set_data_collection_result(context, "cancel_reason", "price")
    return {"recorded": True}

Record structured feedback from a mid-call action

def execute_action(context):
    feedback = context["args"]["feedback_category"]
    set_data_collection_result(context, "product_feedback", feedback)
    return {"success": True}

set_data_collection_result