Analyzing Experiment Session Replays

First, retrieve the experiment information and the feature flag variants (source of truth).

Step 1a: Get experiment metadata

You can either:

• Option A: Use the experiment_results_summary tool if you already have the experiment ID from context
• Option B: Query the experiments table via HogQL:

SELECT
    id,
    name,
    feature_flag_key,
    start_date,
    end_date
FROM system.experiments
WHERE id = <experiment_id>
  AND team_id = <team_id>

From the experiment data, extract:

• feature_flag_key: The feature flag controlling the experiment
• start_date and end_date: The experiment's time range

Step 1b: Get variants from the feature flag

IMPORTANT: Always get variants from the feature flag, NOT from experiment.parameters.feature_flag_variants. The parameters can be out of sync or deprecated. The feature flag is the source of truth.

Query the feature flag to get the current variants:

SELECT
    key,
    filters
FROM system.feature_flags
WHERE key = '<feature_flag_key>'
  AND team_id = <team_id>

Extract the variant keys from filters.multivariate.variants array. Example structure: [{"key": "control", "name": "Control", "rollout_percentage": 50}, {"key": "test", ...}]

The variant key values (e.g., "control", "test", "variant_a") are what you'll use to filter session recordings.

2. Build session recording filters for each variant

For each variant in the experiment, construct recording filters that match users exposed to that variant.

Filter structure for a variant:

{
  "date_from": "<experiment.start_date>",
  "date_to": "<experiment.end_date or current time>",
  "filter_test_accounts": true,
  "events": [
    {
      "id": "$feature_flag_called",
      "type": "events",
      "properties": [
        {
          "key": "$feature_flag",
          "value": ["<feature_flag_key>"],
          "operator": "exact",
          "type": "event"
        },
        {
          "key": "$feature/<feature_flag_key>",
          "value": ["<variant_key>"],
          "operator": "exact",
          "type": "event"
        }
      ]
    }
  ]
}

Key points:

• Filter by $feature_flag_called events where the flag matches the experiment's feature flag
• Use $feature/<flag_key> property to filter for the specific variant value
• Set the date range to the experiment's start and end dates
• Enable filter_test_accounts: true to exclude test users

3. Retrieve recordings for each variant

Use the filter_session_recordings tool with the filters constructed in step 2.

Call the tool once per variant to get recordings for each group:

• Variant "control" → recordings for control group
• Variant "test" → recordings for test variant
• Additional variants if the experiment has more than 2

The tool returns a list of recordings with metadata including:

• User/distinct_id
• Session duration
• Activity metrics (clicks, keypresses)
• Console errors
• First URL visited
• Start time

4. Compare and analyze

Compare the recordings between variants by looking for:

Quantitative patterns:

• Session duration differences
• Activity levels (clicks, keypresses)
• Console error rates
• Bounce rates

Qualitative insights:

• User confusion or frustration indicators
• Different navigation paths
• Feature discovery patterns
• Error recovery behavior

5. Present findings

Summarize the behavioral differences between variants, highlighting:

• Total recordings per variant
• Notable behavior patterns unique to each variant
• Usability issues or friction points observed
• Recommendations based on the qualitative data

Example interaction

User: "How are users behaving in my checkout experiment?"
Agent steps:
1. Query experiment details (ID: 123, feature_flag_key: "checkout-flow-test", date range: 2025-01-01 to 2025-01-31)
2. Query feature flag "checkout-flow-test" to get variants from filters.multivariate.variants
3. Extract variant keys: "control" and "new-checkout"
4. Build filters for control variant:
   - Events: $feature_flag_called with $feature_flag="checkout-flow-test" and $feature/checkout-flow-test="control"
   - Date range: 2025-01-01 to 2025-01-31
5. Call filter_session_recordings with control filters → 147 recordings found
6. Build filters for new-checkout variant and call filter_session_recordings → 152 recordings found
7. Compare patterns:
   - Control: Average 3m 45s session duration, 12% console errors
   - New-checkout: Average 2m 30s session duration, 5% console errors
8. Present findings:
   "I analyzed session replays for your checkout experiment. The new checkout flow shows:
   - 33% faster completion (2m 30s vs 3m 45s)
   - 58% fewer console errors (5% vs 12%)
   - Users in the new variant navigate directly to payment, while control users often backtrack to review cart
   - Recommendation: The new checkout flow reduces friction and errors"

Important notes

Do not make assumptions:

• Always verify the experiment has recordings before analyzing
• Check that the experiment is launched (has a start_date)
• If no recordings are found, inform the user clearly

Filter construction:

• The $feature/<flag_key> property is how PostHog tracks which variant a user saw
• Always use $feature_flag_called as the event type
• Both $feature_flag and $feature/<flag_key> properties are required

Error handling:

• If the experiment is in draft state, tell the user it hasn't started yet
• If no recordings exist, suggest enabling session replay or waiting for user traffic
• If the variant count is unexpected, double-check the experiment configuration

Related tools

• filter_session_recordings: Core tool for retrieving session recordings with filters
• experiment_results_summary: Get experiment metadata and statistical results
• execute_sql: Query experiments table for details via HogQL