| name | investigating-replay |
| description | Investigates a session recording by gathering metadata, person profile, same-session events, and linked error tracking issues in one pass. Use when a user provides a recording or session ID and wants to understand what happened — who the user was, what they did, what errors occurred, and whether there are related error tracking issues. Replaces the manual chain of session-recording-get, persons-retrieve, execute-sql, and query-error-tracking-issues-list.
|
Investigating a session recording
When a user asks "what happened in this session?" or provides a recording/session ID
to investigate, gather all relevant context in parallel rather than making them
ask for each piece.
Available tools
| Tool | Purpose |
|---|
posthog:session-recording-get | Recording metadata (duration, counts, status) |
posthog:persons-retrieve | Person profile (properties, distinct IDs) |
posthog:execute-sql | Query events, errors, and page views in session |
posthog:query-error-tracking-issues-list | Find error tracking issues linked to the session |
posthog:vision-observations-list | Check for an existing Replay Vision AI summary |
posthog:vision-scanners-list | Find summarizer scanners (scanner_type=summarizer) |
posthog:vision-scanners-scan-session | Run a summarizer scanner on the session (slow, optional) |
posthog:vision-scanners-create | Create a temporary summarizer scanner (ask first) |
posthog:vision-scanners-delete | Delete a temporary scanner after summarizing |
Workflow
Step 1 — Get recording metadata and person profile
Start with the recording to get metadata and the person's distinct ID:
posthog:session-recording-get
{
"id": "<recording_id>"
}
The response includes distinct_id, person, duration, interaction counts,
console error counts, and viewing status. Use the distinct_id to fetch
the full person profile:
posthog:persons-retrieve
{
"id": "<person_uuid_from_recording>"
}
Step 2 — Query same-session events
Get the timeline of what the user did during the session:
posthog:execute-sql
SELECT
timestamp,
event,
properties.$current_url AS url,
properties.$browser AS browser,
properties.$os AS os,
properties.$device_type AS device_type,
properties.$screen_width AS screen_width
FROM events
WHERE $session_id = '<session_id>'
ORDER BY timestamp ASC
LIMIT 200
For sessions with many events, focus on the most informative ones:
posthog:execute-sql
SELECT
timestamp,
event,
properties.$current_url AS url,
if(event = '$exception', properties.$exception_values[1], null) AS exception_message,
if(event = '$exception', properties.$exception_types[1], null) AS exception_type
FROM events
WHERE $session_id = '<session_id>'
AND event IN ('$pageview', '$pageleave', '$autocapture', '$exception', '$rageclick')
ORDER BY timestamp ASC
LIMIT 100
Step 3 — Check for linked error tracking issues
If the recording has console errors or exceptions, find related error tracking issues:
posthog:execute-sql
SELECT DISTINCT
properties.$exception_fingerprint AS fingerprint,
properties.$exception_types[1] AS type,
properties.$exception_values[1] AS message,
count() AS occurrences
FROM events
WHERE $session_id = '<session_id>'
AND event = '$exception'
GROUP BY fingerprint, type, message
ORDER BY occurrences DESC
LIMIT 10
If fingerprints are found, search for the corresponding error tracking issues
to provide links and status:
posthog:query-error-tracking-issues-list
{
"searchQuery": "<exception_type or message>"
}
Step 4 — Synthesize the investigation
Present the findings as a coherent narrative:
- Who — person properties (name, email, country, plan, etc.)
- What — sequence of pages visited and key actions taken
- Problems — exceptions, console errors, rage clicks, and their frequency
- Related issues — linked error tracking issues with their status (active/resolved)
- Context — session duration, device/browser, activity score
Optional: AI summary via Replay Vision
If the user wants a deeper analysis without reading through events manually,
offer a Replay Vision summary. Follow "check-then-scan" — don't scan blindly,
a scanner can only observe a given session once.
-
Check for an existing summary. A scheduled scanner may already have one:
posthog:vision-observations-list
{
"session_id": "<session_id>"
}
Look for an observation where scanner_snapshot.scanner_type is summarizer
and status is succeeded. If found, read scanner_result.model_output
(title, summary, intent, outcome, friction_points, keywords) — done,
no new scan needed.
-
Find a summarizer scanner if none exists yet:
posthog:vision-scanners-list
{
"scanner_type": "summarizer"
}
- Exactly one → use it.
- More than one → show the user the scanners (name + prompt) and ask which to use.
- None → no summarizer scanner exists. See
No summarizer scanner? Run a temporary one below.
-
Scan the session with the chosen scanner. Warn this is async and takes
several minutes (rasterize + LLM):
posthog:vision-scanners-scan-session
{
"id": "<scanner_id>",
"session_id": "<session_id>"
}
-
Retrieve the result by polling vision-observations-list (step 1) until
the new observation reaches succeeded.
No summarizer scanner? Run a temporary one
If the project has no summarizer scanner, you can still produce a one-off summary
with a throwaway scanner — but ask the user's permission before creating anything.
-
Ask permission to create a temporary summarizer scanner just to summarize
this one session.
-
Create it disabled so it never sweeps on a schedule — a disabled scanner
only runs when you trigger it on demand, so it won't touch other sessions or
burn quota in the background:
posthog:vision-scanners-create
{
"name": "Temporary on-demand summary",
"scanner_type": "summarizer",
"scanner_config": {
"prompt": "Summarize what the user was trying to do, whether they succeeded, and any friction they hit."
},
"query": { "kind": "RecordingsQuery" },
"model": "gemini-3.6-flash",
"enabled": false
}
-
Scan this session on demand with the new scanner, then poll for the result:
posthog:vision-scanners-scan-session
{
"id": "<new_scanner_id>",
"session_id": "<session_id>"
Tips
- Run steps 1-3 in parallel when possible — they're independent queries.
- If the recording has very few events, the session was likely very short.
Note this rather than suggesting something is broken.
- Console error count from the recording metadata is a good signal for whether
to dig into exceptions. If it's 0, skip step 3.
- The
start_url from the recording tells you where the user's journey began —
use this to frame the narrative.
- If
person is null on the recording, the user was anonymous.
Person properties won't be available, but events still are.