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

# Takeaways

> Reusable structured outputs extracted from every call

Takeaways turn finished conversations into validated JSON. Define a takeaway once — a name, a JSON Schema, optionally a custom prompt and model — attach it to any number of agents, and after each call an LLM fills the schema from the transcript. Results arrive inside `call.ended` and the analysis API.

Use them for CSAT scores, lead qualification fields, booking details, follow-up flags — anything you'd otherwise parse out of transcripts by hand.

## Create a takeaway

```bash theme={null}
curl -X POST http://localhost:8090/v1/takeaways \
  -H "Authorization: Bearer tc_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "csat",
    "description": "Customer satisfaction, inferred from the conversation",
    "schema": {
      "type": "object",
      "properties": {
        "score": {"type": "integer", "minimum": 1, "maximum": 5},
        "reason": {"type": "string"}
      },
      "required": ["score"]
    }
  }'
```

The `name` keys the result in payloads, so it must be identifier-shaped (`csat`, `lead_quality`, `booking-details`) and is immutable after creation. The schema is validated at create time — a broken schema fails here, not after every call.

Optional fields: `prompt` (extra extraction instructions) and `model` (per-takeaway LLM override — e.g. a cheap model for a boolean, a strong one for complex extraction).

## Attach to agents

Reference takeaways by id in the agent's analysis config:

```json theme={null}
{
  "analysis": {
    "enabled": true,
    "takeaway_ids": ["<csat-takeaway-uuid>", "<lead-quality-uuid>"]
  }
}
```

Multiple agents can attach the same takeaway; editing it in one place updates extraction everywhere.

## Results

Each attached takeaway is extracted in its own concurrent LLM call after the call ends (a few seconds, in the background). Results land keyed by name:

```json theme={null}
// call.ended payload → analysis.takeaways
{
  "csat": {
    "result": {"score": 4, "reason": "Issue resolved quickly"},
    "valid": true,
    "model": "gpt-4o-mini",
    "duration_ms": 1240
  }
}
```

Also available via `GET /v1/calls/{call_id}/analysis`. Extracted JSON is validated against your schema (with one automatic retry on mismatch); a failed extraction is reported as `{"valid": false, "error": "..."}` rather than silently dropped — one bad takeaway never affects the others.

## Manage

```
POST   /v1/takeaways          # Create
GET    /v1/takeaways          # List
GET    /v1/takeaways/{id}     # Get
PUT    /v1/takeaways/{id}     # Update (schema/description/prompt/model — not name)
DELETE /v1/takeaways/{id}     # Delete (409 while agents still attach it)
```

<Note>
  The older inline `analysis.structured_extraction_schema` keeps working
  (result at `analysis.structured_data`), but takeaways are the recommended
  path — reusable, multiple per agent, independently validated.
</Note>