> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cekura.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Calls
> Observe production calls and run metrics on real traffic
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Setup steps and authentication are in the [Overview](/cli-sdk/overview). This page covers production-call observability.
A **call** is a production conversation — captured from your live voice agent. Once a call is in Cekura, you can run metrics on it, build test sets from it, and analyze trends across thousands of calls. Webhooks from Vapi / Retell / ElevenLabs / LiveKit / Pipecat ingest automatically; you can also send calls programmatically.
## List calls
```bash theme={null}
cekura calls list --project-id 742
cekura calls list --agent-id 123 --format json | jq '.[] | .id, .duration'
```
```python theme={null}
calls = client.calls.list(project_id=742, page_size=50)
for c in calls.get("results", calls):
print(c["id"], c.get("duration"), c.get("call_ended_reason"))
```
Common filters: `project_id`, `agent_id`, `from_date`, `to_date`, `success`, `topic`. See the API Reference for the full list.
## Inspect a call
```bash theme={null}
cekura calls get 7788
```
```python theme={null}
call = client.calls.get(call_id=7788)
print(call["transcript"])
print(call["metric_evaluations"])
```
## Send a call for evaluation
If your provider isn't on the auto-ingestion list — or you want to ship a call from your own backend — send it explicitly.
```bash theme={null}
cekura calls send --from-file call.json
```
Where `call.json` contains the agent ID, transcript, duration, and metadata.
```python theme={null}
client.calls.send(
agent=123,
transcript=[
{"role": "agent", "text": "How can I help?"},
{"role": "user", "text": "I'd like to reschedule my appointment."},
],
duration=124,
metadata={"source": "internal-bot"},
)
```
Provider-specific webhook helpers are also available:
```python theme={null}
client.calls.observe_vapi(payload=...)
client.calls.observe_retell(payload=...)
client.calls.observe_elevenlabs(payload=...)
client.calls.observe_livekit(payload=...)
client.calls.observe_pipecat(payload=...)
```
In production these are usually wired as webhooks pointing at your backend or directly at `api.cekura.ai/observe` — the SDK helpers are useful for replays and testing.
## Run metrics on a call
Score an already-ingested call against one or more metrics.
```bash theme={null}
cekura calls evaluate 7788 --metric-ids 55,56
# Re-run a previous evaluation (e.g. after editing a metric prompt)
cekura calls rerun-evaluation 7788 --metric-ids 55
```
```python theme={null}
client.calls.evaluate_metrics(call_log_id=7788, metric_ids=[55, 56])
# Re-run a previous evaluation
client.calls.rerun_evaluation(call_log_id=7788, metric_ids=[55])
```
## Generate scenarios from real calls
Found an interesting production call? Turn it into regression scenarios.
```bash theme={null}
cekura calls create-scenarios 7788 --count 3
cekura calls create-scenarios-progress --progress-id
```
```python theme={null}
job = client.calls.create_scenarios(call_log_id=7788, count=3)
client.calls.create_scenarios_progress(progress_id=job["progress_id"])
```
## Promote a call into a test set
```bash theme={null}
cekura test-sets create-from-call-log \
--call-log-id 7788 \
--name "support-edge-cases"
```
```python theme={null}
client.test_sets.create_from_call_log(
call_log_id=7788,
name="support-edge-cases",
)
```
## Vote on a metric result
Capture thumbs up/down feedback on a specific metric evaluation for a call, optionally attach the expected value and free-text feedback. The call is marked as reviewed; the metric evaluation is updated. Feeds the labs / metric-review workflow.
```bash theme={null}
# Thumbs up — agreement with the metric's verdict
cekura calls mark-metric-vote 7788 \
--metric-id 55 \
--thumbs-up \
--expected-value 5 \
--feedback "Agent greeted correctly"
# Thumbs down — disagreement, supply what you'd have expected
cekura calls mark-metric-vote 7788 \
--metric-id 55 \
--thumbs-down \
--expected-value 2 \
--feedback "Greeting was too curt"
```
`--expected-value` is parsed as JSON when possible (`5`, `true`, `"foo"`), so numeric / boolean metrics get the right type.
```python theme={null}
client.calls.mark_metric_vote(
call_log_id=7788,
metric_id=55,
thumbs_up=True,
expected_value=5,
feedback="Agent greeted correctly",
)
```
## Flag a critical-scenario verdict as wrong
If a critical-scenario evaluation looks incorrect, flag it (and later unflag if it was a misclick or has since been corrected).
```python theme={null}
client.calls.mark_critical_scenario_wrong(call_log_id=7788, scenario_id=42)
client.calls.unmark_critical_scenario_wrong(call_log_id=7788, scenario_id=42)
```
## Improve the agent's prompt from real failures
Use the failure pattern across recent calls to suggest a prompt improvement.
```bash theme={null}
cekura calls improve-prompt --agent-id 123 --lookback-days 7
cekura calls improve-prompt-progress --progress-id
```
```python theme={null}
job = client.calls.improve_prompt(agent_id=123, lookback_days=7)
client.calls.improve_prompt_progress(progress_id=job["progress_id"])
```
## See also
Define what you score production calls on.
Same scoring engine — for simulated, not production, traffic.
Visualize call quality and metric trends over time.
Full field reference for call logs and observation payloads.