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

# Session conversation completeness

> Learn how to use the session conversation completeness test to evaluate whether sessions reach a clean end state

## Definition

The session conversation completeness test evaluates whether a multi-turn conversation
**reached proper resolution with all topics adequately addressed**. An
LLM-as-a-judge reads the full conversation and scores it against four criteria:

* The user's initial request was fully addressed
* All follow-up questions were answered
* No topics were left unresolved or only partially addressed
* The session reached a clear final response rather than trailing off

## Taxonomy

* **Task types**: LLM.
* **Availability**: <Tooltip tip="Continuously evaluate your models and datasets as you iterate on their versions.">development</Tooltip>
  and <Tooltip tip="Monitor a model in production, measure its health, check for drifts and set up alerts.">monitoring</Tooltip>.
* **Evaluation level**: session.
* **Polarity**: higher score = better. `0` = completely incomplete, `1` = fully
  resolved.

## Why it matters

* Incomplete conversations often signal unresolved issues that will resurface as
  repeat sessions, support tickets, or churn.
* Complementary to [Session goal achievement](/tests/catalog/session-goal-achievement).
  The two overlap — both can penalize a session where the user's request wasn't
  addressed — but completeness also checks that *every* follow-up and sub-topic
  received an answer, not only the primary objective.

## Required columns

* **Input**: The user's message in each turn.
* **Output**: The assistant's response in each turn.
* **Session ID**: Groups turns belonging to the same conversation.
* **Timestamp**: Used to reconstruct turn order within a session.

<Note>
  This metric relies on an LLM evaluator. On Openlayer you can configure the
  underlying LLM used to compute it. Check out the
  [OpenAI](/integrations/openai#openai-llm-evaluator) or
  [Anthropic](/integrations/anthropic#anthropic-llm-evaluator) integration
  guides for details.
</Note>

## Test configuration examples

<CodeGroup>
  ```json Monitoring theme={null}
  [
    {
      "name": "Session completeness above 0.7",
      "description": "Ensure conversations reach a clean end state",
      "type": "performance",
      "subtype": "sessionConversationCompleteness",
      "thresholds": [
        {
          "insightName": "sessionConversationCompleteness",
          "measurement": "meanScore",
          "operator": ">=",
          "value": 0.7
        }
      ],
      "subpopulationFilters": null,
      "mode": "monitoring",
      "usesProductionData": true,
      "evaluationWindow": 3600,
      "delayWindow": 0
    }
  ]
  ```
</CodeGroup>

## Related

* [Session goal achievement](/tests/catalog/session-goal-achievement) — tracks whether the user's objective was met.