fix: cast OnboardingStep enum to text in funnel view

The completedSteps column is a platform."OnboardingStep" enum array.
UNNEST produces enum values that can't be compared directly to text
from the VALUES clause. Adding ::text cast fixes the type mismatch.

autogpt_platform/analytics/queries/auth_activities.sql

@@ -0,0 +1,40 @@
+-- =============================================================
+-- View: analytics.auth_activities
+-- Looker source alias: ds49  |  Charts: 1
+-- =============================================================
+-- DESCRIPTION
+--   Tracks authentication events (login, logout, SSO, password
+--   reset, etc.) from Supabase's internal audit log.
+--   Useful for monitoring sign-in patterns and detecting anomalies.
+--
+-- SOURCE TABLES
+--   auth.audit_log_entries  — Supabase internal auth event log
+--
+-- OUTPUT COLUMNS
+--   created_at      TIMESTAMPTZ  When the auth event occurred
+--   actor_id        TEXT         User ID who triggered the event
+--   actor_via_sso   TEXT         Whether the action was via SSO ('true'/'false')
+--   action          TEXT         Event type (e.g. 'login', 'logout', 'token_refreshed')
+--
+-- WINDOW
+--   Rolling 90 days from current date
+--
+-- EXAMPLE QUERIES
+--   -- Daily login counts
+--   SELECT DATE_TRUNC('day', created_at) AS day, COUNT(*) AS logins
+--   FROM analytics.auth_activities
+--   WHERE action = 'login'
+--   GROUP BY 1 ORDER BY 1;
+--
+--   -- SSO vs password login breakdown
+--   SELECT actor_via_sso, COUNT(*) FROM analytics.auth_activities
+--   WHERE action = 'login' GROUP BY 1;
+-- =============================================================
+
+SELECT
+    created_at,
+    payload->>'actor_id'      AS actor_id,
+    payload->>'actor_via_sso' AS actor_via_sso,
+    payload->>'action'        AS action
+FROM auth.audit_log_entries
+WHERE created_at >= NOW() - INTERVAL '90 days'

autogpt_platform/analytics/queries/graph_execution.sql

@@ -0,0 +1,105 @@
+-- =============================================================
+-- View: analytics.graph_execution
+-- Looker source alias: ds16  |  Charts: 21
+-- =============================================================
+-- DESCRIPTION
+--   One row per agent graph execution (last 90 days).
+--   Unpacks the JSONB stats column into individual numeric columns
+--   and normalises the executionStatus — runs that failed due to
+--   insufficient credits are reclassified as 'NO_CREDITS' for
+--   easier filtering.  Error messages are scrubbed of IDs and URLs
+--   to allow safe grouping.
+--
+-- SOURCE TABLES
+--   platform.AgentGraphExecution  — Execution records
+--   platform.AgentGraph           — Agent graph metadata (for name)
+--   platform.LibraryAgent         — To flag possibly-AI (safe-mode) agents
+--
+-- OUTPUT COLUMNS
+--   id                TEXT         Execution UUID
+--   agentGraphId      TEXT         Agent graph UUID
+--   agentGraphVersion INT          Graph version number
+--   executionStatus   TEXT         COMPLETED | FAILED | NO_CREDITS | RUNNING | QUEUED | TERMINATED
+--   createdAt         TIMESTAMPTZ  When the execution was queued
+--   updatedAt         TIMESTAMPTZ  Last status update time
+--   userId            TEXT         Owner user UUID
+--   agentGraphName    TEXT         Human-readable agent name
+--   cputime           DECIMAL      Total CPU seconds consumed
+--   walltime          DECIMAL      Total wall-clock seconds
+--   node_count        DECIMAL      Number of nodes in the graph
+--   nodes_cputime     DECIMAL      CPU time across all nodes
+--   nodes_walltime    DECIMAL      Wall time across all nodes
+--   execution_cost    DECIMAL      Credit cost of this execution
+--   correctness_score FLOAT        AI correctness score (if available)
+--   possibly_ai       BOOLEAN      True if agent has sensitive_action_safe_mode enabled
+--   groupedErrorMessage TEXT       Scrubbed error string (IDs/URLs replaced with wildcards)
+--
+-- WINDOW
+--   Rolling 90 days (createdAt > CURRENT_DATE - 90 days)
+--
+-- EXAMPLE QUERIES
+--   -- Daily execution counts by status
+--   SELECT DATE_TRUNC('day', "createdAt") AS day, "executionStatus", COUNT(*)
+--   FROM analytics.graph_execution
+--   GROUP BY 1, 2 ORDER BY 1;
+--
+--   -- Average cost per execution by agent
+--   SELECT "agentGraphName", AVG("execution_cost") AS avg_cost, COUNT(*) AS runs
+--   FROM analytics.graph_execution
+--   WHERE "executionStatus" = 'COMPLETED'
+--   GROUP BY 1 ORDER BY avg_cost DESC;
+--
+--   -- Top error messages
+--   SELECT "groupedErrorMessage", COUNT(*) AS occurrences
+--   FROM analytics.graph_execution
+--   WHERE "executionStatus" = 'FAILED'
+--   GROUP BY 1 ORDER BY 2 DESC LIMIT 20;
+-- =============================================================
+
+SELECT
+    ge."id"                                                        AS id,
+    ge."agentGraphId"                                              AS agentGraphId,
+    ge."agentGraphVersion"                                         AS agentGraphVersion,
+    CASE
+        WHEN jsonb_exists(ge."stats"::jsonb, 'error')
+         AND (
+               (ge."stats"::jsonb->>'error') ILIKE '%insufficient balance%'
+            OR (ge."stats"::jsonb->>'error') ILIKE '%you have no credits left%'
+             )
+        THEN 'NO_CREDITS'
+        ELSE CAST(ge."executionStatus" AS TEXT)
+    END                                                            AS executionStatus,
+    ge."createdAt"                                                 AS createdAt,
+    ge."updatedAt"                                                 AS updatedAt,
+    ge."userId"                                                    AS userId,
+    g."name"                                                       AS agentGraphName,
+    (ge."stats"::jsonb->>'cputime')::decimal                       AS cputime,
+    (ge."stats"::jsonb->>'walltime')::decimal                      AS walltime,
+    (ge."stats"::jsonb->>'node_count')::decimal                    AS node_count,
+    (ge."stats"::jsonb->>'nodes_cputime')::decimal                 AS nodes_cputime,
+    (ge."stats"::jsonb->>'nodes_walltime')::decimal                AS nodes_walltime,
+    (ge."stats"::jsonb->>'cost')::decimal                          AS execution_cost,
+    (ge."stats"::jsonb->>'correctness_score')::float               AS correctness_score,
+    COALESCE(la.possibly_ai, FALSE)                                AS possibly_ai,
+    REGEXP_REPLACE(
+        REGEXP_REPLACE(
+            TRIM(BOTH '"' FROM ge."stats"::jsonb->>'error'),
+            '(https?://)([A-Za-z0-9.-]+)(:[0-9]+)?(/[^\s]*)?',
+            '\1\2/...', 'gi'
+        ),
+        '[a-zA-Z0-9_:-]*\d[a-zA-Z0-9_:-]*', '*', 'g'
+    )                                                              AS groupedErrorMessage
+FROM platform."AgentGraphExecution" ge
+LEFT JOIN platform."AgentGraph" g
+       ON ge."agentGraphId" = g."id"
+      AND ge."agentGraphVersion" = g."version"
+LEFT JOIN (
+    SELECT DISTINCT ON ("userId", "agentGraphId")
+           "userId", "agentGraphId",
+           ("settings"::jsonb->>'sensitive_action_safe_mode')::boolean AS possibly_ai
+    FROM platform."LibraryAgent"
+    WHERE "isDeleted"  = FALSE
+      AND "isArchived" = FALSE
+    ORDER BY "userId", "agentGraphId", "agentGraphVersion" DESC
+) la ON la."userId" = ge."userId" AND la."agentGraphId" = ge."agentGraphId"
+WHERE ge."createdAt" > CURRENT_DATE - INTERVAL '90 days'

autogpt_platform/analytics/queries/node_block_execution.sql

@@ -0,0 +1,101 @@
+-- =============================================================
+-- View: analytics.node_block_execution
+-- Looker source alias: ds14  |  Charts: 11
+-- =============================================================
+-- DESCRIPTION
+--   One row per node (block) execution (last 90 days).
+--   Unpacks stats JSONB and joins to identify which block type
+--   was run.  For failed nodes, joins the error output and
+--   scrubs it for safe grouping.
+--
+-- SOURCE TABLES
+--   platform.AgentNodeExecution              — Node execution records
+--   platform.AgentNode                       — Node → block mapping
+--   platform.AgentBlock                      — Block name/ID
+--   platform.AgentNodeExecutionInputOutput   — Error output values
+--
+-- OUTPUT COLUMNS
+--   id                    TEXT         Node execution UUID
+--   agentGraphExecutionId TEXT         Parent graph execution UUID
+--   agentNodeId           TEXT         Node UUID within the graph
+--   executionStatus       TEXT         COMPLETED | FAILED | QUEUED | RUNNING | TERMINATED
+--   addedTime             TIMESTAMPTZ  When the node was queued
+--   queuedTime            TIMESTAMPTZ  When it entered the queue
+--   startedTime           TIMESTAMPTZ  When execution started
+--   endedTime             TIMESTAMPTZ  When execution finished
+--   inputSize             BIGINT       Input payload size in bytes
+--   outputSize            BIGINT       Output payload size in bytes
+--   walltime              NUMERIC      Wall-clock seconds for this node
+--   cputime               NUMERIC      CPU seconds for this node
+--   llmRetryCount         INT          Number of LLM retries
+--   llmCallCount          INT          Number of LLM API calls made
+--   inputTokenCount       BIGINT       LLM input tokens consumed
+--   outputTokenCount      BIGINT       LLM output tokens produced
+--   blockName             TEXT         Human-readable block name (e.g. 'OpenAIBlock')
+--   blockId               TEXT         Block UUID
+--   groupedErrorMessage   TEXT         Scrubbed error (IDs/URLs wildcarded)
+--   errorMessage          TEXT         Raw error output (only set when FAILED)
+--
+-- WINDOW
+--   Rolling 90 days (addedTime > CURRENT_DATE - 90 days)
+--
+-- EXAMPLE QUERIES
+--   -- Most-used blocks by execution count
+--   SELECT "blockName", COUNT(*) AS executions,
+--          COUNT(*) FILTER (WHERE "executionStatus"='FAILED') AS failures
+--   FROM analytics.node_block_execution
+--   GROUP BY 1 ORDER BY executions DESC LIMIT 20;
+--
+--   -- Average LLM token usage per block
+--   SELECT "blockName",
+--          AVG("inputTokenCount") AS avg_input_tokens,
+--          AVG("outputTokenCount") AS avg_output_tokens
+--   FROM analytics.node_block_execution
+--   WHERE "llmCallCount" > 0
+--   GROUP BY 1 ORDER BY avg_input_tokens DESC;
+--
+--   -- Top failure reasons
+--   SELECT "blockName", "groupedErrorMessage", COUNT(*) AS count
+--   FROM analytics.node_block_execution
+--   WHERE "executionStatus" = 'FAILED'
+--   GROUP BY 1, 2 ORDER BY count DESC LIMIT 20;
+-- =============================================================
+
+SELECT
+    ne."id"                                                            AS id,
+    ne."agentGraphExecutionId"                                         AS agentGraphExecutionId,
+    ne."agentNodeId"                                                   AS agentNodeId,
+    CAST(ne."executionStatus" AS TEXT)                                 AS executionStatus,
+    ne."addedTime"                                                     AS addedTime,
+    ne."queuedTime"                                                    AS queuedTime,
+    ne."startedTime"                                                   AS startedTime,
+    ne."endedTime"                                                     AS endedTime,
+    (ne."stats"::jsonb->>'input_size')::bigint                         AS inputSize,
+    (ne."stats"::jsonb->>'output_size')::bigint                        AS outputSize,
+    (ne."stats"::jsonb->>'walltime')::numeric                          AS walltime,
+    (ne."stats"::jsonb->>'cputime')::numeric                           AS cputime,
+    (ne."stats"::jsonb->>'llm_retry_count')::int                       AS llmRetryCount,
+    (ne."stats"::jsonb->>'llm_call_count')::int                        AS llmCallCount,
+    (ne."stats"::jsonb->>'input_token_count')::bigint                  AS inputTokenCount,
+    (ne."stats"::jsonb->>'output_token_count')::bigint                 AS outputTokenCount,
+    b."name"                                                           AS blockName,
+    b."id"                                                             AS blockId,
+    REGEXP_REPLACE(
+        REGEXP_REPLACE(
+            TRIM(BOTH '"' FROM eio."data"::text),
+            '(https?://)([A-Za-z0-9.-]+)(:[0-9]+)?(/[^\s]*)?',
+            '\1\2/...', 'gi'
+        ),
+        '[a-zA-Z0-9_:-]*\d[a-zA-Z0-9_:-]*', '*', 'g'
+    )                                                                  AS groupedErrorMessage,
+    eio."data"                                                         AS errorMessage
+FROM platform."AgentNodeExecution" ne
+LEFT JOIN platform."AgentNode" nd
+       ON ne."agentNodeId" = nd."id"
+LEFT JOIN platform."AgentBlock" b
+       ON nd."agentBlockId" = b."id"
+LEFT JOIN platform."AgentNodeExecutionInputOutput" eio
+       ON eio."referencedByOutputExecId" = ne."id"
+      AND eio."name" = 'error'
+      AND ne."executionStatus" = 'FAILED'
+WHERE ne."addedTime" > CURRENT_DATE - INTERVAL '90 days'

autogpt_platform/analytics/queries/retention_agent.sql

@@ -0,0 +1,97 @@
+-- =============================================================
+-- View: analytics.retention_agent
+-- Looker source alias: ds35  |  Charts: 2
+-- =============================================================
+-- DESCRIPTION
+--   Weekly cohort retention broken down per individual agent.
+--   Cohort = week of a user's first use of THAT specific agent.
+--   Tells you which agents keep users coming back vs. one-shot
+--   use. Only includes cohorts from the last 180 days.
+--
+-- SOURCE TABLES
+--   platform.AgentGraphExecution  — Execution records (user × agent × time)
+--   platform.AgentGraph           — Agent names
+--
+-- OUTPUT COLUMNS
+--   agent_id            TEXT   Agent graph UUID
+--   agent_label         TEXT   'AgentName [first8chars]'
+--   agent_label_n       TEXT   'AgentName [first8chars] (n=total_users)'
+--   cohort_week_start   DATE   Week users first ran this agent
+--   cohort_label        TEXT   ISO week label
+--   cohort_label_n      TEXT   ISO week label with cohort size
+--   user_lifetime_week  INT    Weeks since first use of this agent
+--   cohort_users        BIGINT Users in this cohort for this agent
+--   active_users        BIGINT Users who ran the agent again in week k
+--   retention_rate      FLOAT  active_users / cohort_users
+--   cohort_users_w0     BIGINT cohort_users only at week 0 (safe to SUM)
+--   agent_total_users   BIGINT Total users across all cohorts for this agent
+--
+-- EXAMPLE QUERIES
+--   -- Best-retained agents at week 2
+--   SELECT agent_label, AVG(retention_rate) AS w2_retention
+--   FROM analytics.retention_agent
+--   WHERE user_lifetime_week = 2 AND cohort_users >= 10
+--   GROUP BY 1 ORDER BY w2_retention DESC LIMIT 10;
+--
+--   -- Agents with most unique users
+--   SELECT DISTINCT agent_label, agent_total_users
+--   FROM analytics.retention_agent
+--   ORDER BY agent_total_users DESC LIMIT 20;
+-- =============================================================
+
+WITH params AS (SELECT 12::int AS max_weeks, (CURRENT_DATE - INTERVAL '180 days') AS cohort_start),
+events AS (
+  SELECT e."userId"::text AS user_id, e."agentGraphId" AS agent_id,
+         e."createdAt"::timestamptz AS created_at,
+         DATE_TRUNC('week', e."createdAt")::date AS week_start
+  FROM platform."AgentGraphExecution" e
+),
+first_use AS (
+  SELECT user_id, agent_id, MIN(created_at) AS first_use_at,
+         DATE_TRUNC('week', MIN(created_at))::date AS cohort_week_start
+  FROM events GROUP BY 1,2
+  HAVING MIN(created_at) >= (SELECT cohort_start FROM params)
+),
+activity_weeks AS (SELECT DISTINCT user_id, agent_id, week_start FROM events),
+user_week_age AS (
+  SELECT aw.user_id, aw.agent_id, fu.cohort_week_start,
+         ((aw.week_start - DATE_TRUNC('week',fu.first_use_at)::date)/7)::int AS user_lifetime_week
+  FROM activity_weeks aw JOIN first_use fu USING (user_id, agent_id)
+  WHERE aw.week_start >= DATE_TRUNC('week',fu.first_use_at)::date
+),
+active_counts AS (
+  SELECT agent_id, cohort_week_start, user_lifetime_week, COUNT(DISTINCT user_id) AS active_users
+  FROM user_week_age WHERE user_lifetime_week >= 0 GROUP BY 1,2,3
+),
+cohort_sizes AS (
+  SELECT agent_id, cohort_week_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_use GROUP BY 1,2
+),
+cohort_caps AS (
+  SELECT cs.agent_id, cs.cohort_week_start, cs.cohort_users,
+         LEAST((SELECT max_weeks FROM params),
+               GREATEST(0,((DATE_TRUNC('week',CURRENT_DATE)::date-cs.cohort_week_start)/7)::int)) AS cap_weeks
+  FROM cohort_sizes cs
+),
+grid AS (
+  SELECT cc.agent_id, cc.cohort_week_start, gs AS user_lifetime_week, cc.cohort_users
+  FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_weeks) gs
+),
+agent_names AS (SELECT DISTINCT ON (g."id") g."id" AS agent_id, g."name" AS agent_name FROM platform."AgentGraph" g ORDER BY g."id", g."version" DESC),
+agent_total_users AS (SELECT agent_id, SUM(cohort_users) AS agent_total_users FROM cohort_sizes GROUP BY 1)
+SELECT
+  g.agent_id,
+  COALESCE(an.agent_name,'(unnamed)')||' ['||LEFT(g.agent_id::text,8)||']'  AS agent_label,
+  COALESCE(an.agent_name,'(unnamed)')||' ['||LEFT(g.agent_id::text,8)||'] (n='||COALESCE(atu.agent_total_users,0)||')' AS agent_label_n,
+  g.cohort_week_start,
+  TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')                               AS cohort_label,
+  TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')||' (n='||g.cohort_users||')'  AS cohort_label_n,
+  g.user_lifetime_week, g.cohort_users,
+  COALESCE(ac.active_users,0)                                              AS active_users,
+  COALESCE(ac.active_users,0)::float / NULLIF(g.cohort_users,0)           AS retention_rate,
+  CASE WHEN g.user_lifetime_week=0 THEN g.cohort_users ELSE 0 END         AS cohort_users_w0,
+  COALESCE(atu.agent_total_users,0)                                        AS agent_total_users
+FROM grid g
+LEFT JOIN active_counts     ac  ON ac.agent_id=g.agent_id AND ac.cohort_week_start=g.cohort_week_start AND ac.user_lifetime_week=g.user_lifetime_week
+LEFT JOIN agent_names       an  ON an.agent_id=g.agent_id
+LEFT JOIN agent_total_users atu ON atu.agent_id=g.agent_id
+ORDER BY agent_label, g.cohort_week_start, g.user_lifetime_week;

autogpt_platform/analytics/queries/retention_execution_daily.sql

@@ -0,0 +1,81 @@
+-- =============================================================
+-- View: analytics.retention_execution_daily
+-- Looker source alias: ds111  |  Charts: 1
+-- =============================================================
+-- DESCRIPTION
+--   Daily cohort retention based on agent executions.
+--   Cohort anchor = day of user's FIRST ever execution.
+--   Only includes cohorts from the last 90 days, up to day 30.
+--   Great for early engagement analysis (did users run another
+--   agent the next day?).
+--
+-- SOURCE TABLES
+--   platform.AgentGraphExecution  — Execution records
+--
+-- OUTPUT COLUMNS
+--   Same pattern as retention_login_daily.
+--   cohort_day_start = day of first execution (not first login)
+--
+-- EXAMPLE QUERIES
+--   -- Day-3 execution retention
+--   SELECT cohort_label, retention_rate_bounded AS d3_retention
+--   FROM analytics.retention_execution_daily
+--   WHERE user_lifetime_day = 3 ORDER BY cohort_day_start;
+-- =============================================================
+
+WITH params AS (SELECT 30::int AS max_days, (CURRENT_DATE - INTERVAL '90 days') AS cohort_start),
+events AS (
+  SELECT e."userId"::text AS user_id, e."createdAt"::timestamptz AS created_at,
+         DATE_TRUNC('day', e."createdAt")::date AS day_start
+  FROM platform."AgentGraphExecution" e WHERE e."userId" IS NOT NULL
+),
+first_exec AS (
+  SELECT user_id, MIN(created_at) AS first_exec_at,
+         DATE_TRUNC('day', MIN(created_at))::date AS cohort_day_start
+  FROM events GROUP BY 1
+  HAVING MIN(created_at) >= (SELECT cohort_start FROM params)
+),
+activity_days AS (SELECT DISTINCT user_id, day_start FROM events),
+user_day_age AS (
+  SELECT ad.user_id, fe.cohort_day_start,
+         (ad.day_start - DATE_TRUNC('day',fe.first_exec_at)::date)::int AS user_lifetime_day
+  FROM activity_days ad JOIN first_exec fe USING (user_id)
+  WHERE ad.day_start >= DATE_TRUNC('day',fe.first_exec_at)::date
+),
+bounded_counts AS (
+  SELECT cohort_day_start, user_lifetime_day, COUNT(DISTINCT user_id) AS active_users_bounded
+  FROM user_day_age WHERE user_lifetime_day >= 0 GROUP BY 1,2
+),
+last_active AS (
+  SELECT cohort_day_start, user_id, MAX(user_lifetime_day) AS last_active_day FROM user_day_age GROUP BY 1,2
+),
+unbounded_counts AS (
+  SELECT la.cohort_day_start, gs AS user_lifetime_day, COUNT(*) AS retained_users_unbounded
+  FROM last_active la
+  CROSS JOIN LATERAL generate_series(0, LEAST(la.last_active_day,(SELECT max_days FROM params))) gs
+  GROUP BY 1,2
+),
+cohort_sizes AS (SELECT cohort_day_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_exec GROUP BY 1),
+cohort_caps AS (
+  SELECT cs.cohort_day_start, cs.cohort_users,
+         LEAST((SELECT max_days FROM params), GREATEST(0,(CURRENT_DATE-cs.cohort_day_start)::int)) AS cap_days
+  FROM cohort_sizes cs
+),
+grid AS (
+  SELECT cc.cohort_day_start, gs AS user_lifetime_day, cc.cohort_users
+  FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_days) gs
+)
+SELECT
+  g.cohort_day_start,
+  TO_CHAR(g.cohort_day_start,'YYYY-MM-DD')                                AS cohort_label,
+  TO_CHAR(g.cohort_day_start,'YYYY-MM-DD')||' (n='||g.cohort_users||')'   AS cohort_label_n,
+  g.user_lifetime_day, g.cohort_users,
+  COALESCE(b.active_users_bounded,0)     AS active_users_bounded,
+  COALESCE(u.retained_users_unbounded,0) AS retained_users_unbounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(b.active_users_bounded,0)::float/g.cohort_users END    AS retention_rate_bounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(u.retained_users_unbounded,0)::float/g.cohort_users END AS retention_rate_unbounded,
+  CASE WHEN g.user_lifetime_day=0 THEN g.cohort_users ELSE 0 END          AS cohort_users_d0
+FROM grid g
+LEFT JOIN bounded_counts   b ON b.cohort_day_start=g.cohort_day_start AND b.user_lifetime_day=g.user_lifetime_day
+LEFT JOIN unbounded_counts u ON u.cohort_day_start=g.cohort_day_start AND u.user_lifetime_day=g.user_lifetime_day
+ORDER BY g.cohort_day_start, g.user_lifetime_day;

autogpt_platform/analytics/queries/retention_execution_weekly.sql

@@ -0,0 +1,81 @@
+-- =============================================================
+-- View: analytics.retention_execution_weekly
+-- Looker source alias: ds92  |  Charts: 2
+-- =============================================================
+-- DESCRIPTION
+--   Weekly cohort retention based on agent executions.
+--   Cohort anchor = week of user's FIRST ever agent execution
+--   (not first login). Only includes cohorts from the last 180 days.
+--   Useful when you care about product engagement, not just visits.
+--
+-- SOURCE TABLES
+--   platform.AgentGraphExecution  — Execution records
+--
+-- OUTPUT COLUMNS
+--   Same pattern as retention_login_weekly.
+--   cohort_week_start = week of first execution (not first login)
+--
+-- EXAMPLE QUERIES
+--   -- Week-2 execution retention
+--   SELECT cohort_label, retention_rate_bounded
+--   FROM analytics.retention_execution_weekly
+--   WHERE user_lifetime_week = 2 ORDER BY cohort_week_start;
+-- =============================================================
+
+WITH params AS (SELECT 12::int AS max_weeks, (CURRENT_DATE - INTERVAL '180 days') AS cohort_start),
+events AS (
+  SELECT e."userId"::text AS user_id, e."createdAt"::timestamptz AS created_at,
+         DATE_TRUNC('week', e."createdAt")::date AS week_start
+  FROM platform."AgentGraphExecution" e WHERE e."userId" IS NOT NULL
+),
+first_exec AS (
+  SELECT user_id, MIN(created_at) AS first_exec_at,
+         DATE_TRUNC('week', MIN(created_at))::date AS cohort_week_start
+  FROM events GROUP BY 1
+  HAVING MIN(created_at) >= (SELECT cohort_start FROM params)
+),
+activity_weeks AS (SELECT DISTINCT user_id, week_start FROM events),
+user_week_age AS (
+  SELECT aw.user_id, fe.cohort_week_start,
+         ((aw.week_start - DATE_TRUNC('week',fe.first_exec_at)::date)/7)::int AS user_lifetime_week
+  FROM activity_weeks aw JOIN first_exec fe USING (user_id)
+  WHERE aw.week_start >= DATE_TRUNC('week',fe.first_exec_at)::date
+),
+bounded_counts AS (
+  SELECT cohort_week_start, user_lifetime_week, COUNT(DISTINCT user_id) AS active_users_bounded
+  FROM user_week_age WHERE user_lifetime_week >= 0 GROUP BY 1,2
+),
+last_active AS (
+  SELECT cohort_week_start, user_id, MAX(user_lifetime_week) AS last_active_week FROM user_week_age GROUP BY 1,2
+),
+unbounded_counts AS (
+  SELECT la.cohort_week_start, gs AS user_lifetime_week, COUNT(*) AS retained_users_unbounded
+  FROM last_active la
+  CROSS JOIN LATERAL generate_series(0, LEAST(la.last_active_week,(SELECT max_weeks FROM params))) gs
+  GROUP BY 1,2
+),
+cohort_sizes AS (SELECT cohort_week_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_exec GROUP BY 1),
+cohort_caps AS (
+  SELECT cs.cohort_week_start, cs.cohort_users,
+         LEAST((SELECT max_weeks FROM params),
+               GREATEST(0,((DATE_TRUNC('week',CURRENT_DATE)::date-cs.cohort_week_start)/7)::int)) AS cap_weeks
+  FROM cohort_sizes cs
+),
+grid AS (
+  SELECT cc.cohort_week_start, gs AS user_lifetime_week, cc.cohort_users
+  FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_weeks) gs
+)
+SELECT
+  g.cohort_week_start,
+  TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')                               AS cohort_label,
+  TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')||' (n='||g.cohort_users||')'  AS cohort_label_n,
+  g.user_lifetime_week, g.cohort_users,
+  COALESCE(b.active_users_bounded,0)     AS active_users_bounded,
+  COALESCE(u.retained_users_unbounded,0) AS retained_users_unbounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(b.active_users_bounded,0)::float/g.cohort_users END    AS retention_rate_bounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(u.retained_users_unbounded,0)::float/g.cohort_users END AS retention_rate_unbounded,
+  CASE WHEN g.user_lifetime_week=0 THEN g.cohort_users ELSE 0 END         AS cohort_users_w0
+FROM grid g
+LEFT JOIN bounded_counts   b ON b.cohort_week_start=g.cohort_week_start AND b.user_lifetime_week=g.user_lifetime_week
+LEFT JOIN unbounded_counts u ON u.cohort_week_start=g.cohort_week_start AND u.user_lifetime_week=g.user_lifetime_week
+ORDER BY g.cohort_week_start, g.user_lifetime_week;

autogpt_platform/analytics/queries/retention_login_daily.sql

@@ -0,0 +1,94 @@
+-- =============================================================
+-- View: analytics.retention_login_daily
+-- Looker source alias: ds112  |  Charts: 1
+-- =============================================================
+-- DESCRIPTION
+--   Daily cohort retention based on login sessions.
+--   Same logic as retention_login_weekly but at day granularity,
+--   showing up to day 30 for cohorts from the last 90 days.
+--   Useful for analysing early activation (days 1-7) in detail.
+--
+-- SOURCE TABLES
+--   auth.sessions  — Login session records
+--
+-- OUTPUT COLUMNS (same pattern as retention_login_weekly)
+--   cohort_day_start          DATE     First day the cohort logged in
+--   cohort_label              TEXT     Date string (e.g. '2025-03-01')
+--   cohort_label_n            TEXT     Date + cohort size (e.g. '2025-03-01 (n=12)')
+--   user_lifetime_day         INT      Days since first login (0 = signup day)
+--   cohort_users              BIGINT   Total users in cohort
+--   active_users_bounded      BIGINT   Users active on exactly day k
+--   retained_users_unbounded  BIGINT   Users active any time on/after day k
+--   retention_rate_bounded    FLOAT    bounded / cohort_users
+--   retention_rate_unbounded  FLOAT    unbounded / cohort_users
+--   cohort_users_d0           BIGINT   cohort_users only at day 0, else 0 (safe to SUM)
+--
+-- EXAMPLE QUERIES
+--   -- Day-1 retention rate (came back next day)
+--   SELECT cohort_label, retention_rate_bounded AS d1_retention
+--   FROM analytics.retention_login_daily
+--   WHERE user_lifetime_day = 1 ORDER BY cohort_day_start;
+--
+--   -- Average retention curve across all cohorts
+--   SELECT user_lifetime_day,
+--          SUM(active_users_bounded)::float / NULLIF(SUM(cohort_users_d0), 0) AS avg_retention
+--   FROM analytics.retention_login_daily
+--   GROUP BY 1 ORDER BY 1;
+-- =============================================================
+
+WITH params AS (SELECT 30::int AS max_days, (CURRENT_DATE - INTERVAL '90 days')::date AS cohort_start),
+events AS (
+  SELECT s.user_id::text AS user_id, s.created_at::timestamptz AS created_at,
+         DATE_TRUNC('day', s.created_at)::date AS day_start
+  FROM auth.sessions s WHERE s.user_id IS NOT NULL
+),
+first_login AS (
+  SELECT user_id, MIN(created_at) AS first_login_time,
+         DATE_TRUNC('day', MIN(created_at))::date AS cohort_day_start
+  FROM events GROUP BY 1
+  HAVING MIN(created_at) >= (SELECT cohort_start FROM params)
+),
+activity_days AS (SELECT DISTINCT user_id, day_start FROM events),
+user_day_age AS (
+  SELECT ad.user_id, fl.cohort_day_start,
+         (ad.day_start - DATE_TRUNC('day', fl.first_login_time)::date)::int AS user_lifetime_day
+  FROM activity_days ad JOIN first_login fl USING (user_id)
+  WHERE ad.day_start >= DATE_TRUNC('day', fl.first_login_time)::date
+),
+bounded_counts AS (
+  SELECT cohort_day_start, user_lifetime_day, COUNT(DISTINCT user_id) AS active_users_bounded
+  FROM user_day_age WHERE user_lifetime_day >= 0 GROUP BY 1,2
+),
+last_active AS (
+  SELECT cohort_day_start, user_id, MAX(user_lifetime_day) AS last_active_day FROM user_day_age GROUP BY 1,2
+),
+unbounded_counts AS (
+  SELECT la.cohort_day_start, gs AS user_lifetime_day, COUNT(*) AS retained_users_unbounded
+  FROM last_active la
+  CROSS JOIN LATERAL generate_series(0, LEAST(la.last_active_day,(SELECT max_days FROM params))) gs
+  GROUP BY 1,2
+),
+cohort_sizes AS (SELECT cohort_day_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_login GROUP BY 1),
+cohort_caps AS (
+  SELECT cs.cohort_day_start, cs.cohort_users,
+         LEAST((SELECT max_days FROM params), GREATEST(0,(CURRENT_DATE-cs.cohort_day_start)::int)) AS cap_days
+  FROM cohort_sizes cs
+),
+grid AS (
+  SELECT cc.cohort_day_start, gs AS user_lifetime_day, cc.cohort_users
+  FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_days) gs
+)
+SELECT
+  g.cohort_day_start,
+  TO_CHAR(g.cohort_day_start,'YYYY-MM-DD')                                  AS cohort_label,
+  TO_CHAR(g.cohort_day_start,'YYYY-MM-DD')||' (n='||g.cohort_users||')'     AS cohort_label_n,
+  g.user_lifetime_day, g.cohort_users,
+  COALESCE(b.active_users_bounded,0)     AS active_users_bounded,
+  COALESCE(u.retained_users_unbounded,0) AS retained_users_unbounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(b.active_users_bounded,0)::float/g.cohort_users END    AS retention_rate_bounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(u.retained_users_unbounded,0)::float/g.cohort_users END AS retention_rate_unbounded,
+  CASE WHEN g.user_lifetime_day=0 THEN g.cohort_users ELSE 0 END            AS cohort_users_d0
+FROM grid g
+LEFT JOIN bounded_counts   b ON b.cohort_day_start=g.cohort_day_start AND b.user_lifetime_day=g.user_lifetime_day
+LEFT JOIN unbounded_counts u ON u.cohort_day_start=g.cohort_day_start AND u.user_lifetime_day=g.user_lifetime_day
+ORDER BY g.cohort_day_start, g.user_lifetime_day;

autogpt_platform/analytics/queries/retention_login_onboarded_weekly.sql

@@ -0,0 +1,96 @@
+-- =============================================================
+-- View: analytics.retention_login_onboarded_weekly
+-- Looker source alias: ds101  |  Charts: 2
+-- =============================================================
+-- DESCRIPTION
+--   Weekly cohort retention from login sessions, restricted to
+--   users who "onboarded" — defined as running at least one
+--   agent within 365 days of their first login.
+--   Filters out users who signed up but never activated,
+--   giving a cleaner view of engaged-user retention.
+--
+-- SOURCE TABLES
+--   auth.sessions                  — Login session records
+--   platform.AgentGraphExecution   — Used to identify onboarders
+--
+-- OUTPUT COLUMNS
+--   Same as retention_login_weekly (cohort_week_start, user_lifetime_week,
+--   retention_rate_bounded, retention_rate_unbounded, etc.)
+--   Only difference: cohort is filtered to onboarded users only.
+--
+-- EXAMPLE QUERIES
+--   -- Compare week-4 retention: all users vs onboarded only
+--   SELECT 'all_users' AS segment, AVG(retention_rate_bounded) AS w4_retention
+--   FROM analytics.retention_login_weekly WHERE user_lifetime_week = 4
+--   UNION ALL
+--   SELECT 'onboarded', AVG(retention_rate_bounded)
+--   FROM analytics.retention_login_onboarded_weekly WHERE user_lifetime_week = 4;
+-- =============================================================
+
+WITH params AS (SELECT 12::int AS max_weeks, 365::int AS onboarding_window_days),
+events AS (
+  SELECT s.user_id::text AS user_id, s.created_at::timestamptz AS created_at,
+         DATE_TRUNC('week', s.created_at)::date AS week_start
+  FROM auth.sessions s WHERE s.user_id IS NOT NULL
+),
+first_login_all AS (
+  SELECT user_id, MIN(created_at) AS first_login_time,
+         DATE_TRUNC('week', MIN(created_at))::date AS cohort_week_start
+  FROM events GROUP BY 1
+),
+onboarders AS (
+  SELECT fl.user_id FROM first_login_all fl
+  WHERE EXISTS (
+    SELECT 1 FROM platform."AgentGraphExecution" e
+    WHERE e."userId"::text = fl.user_id
+      AND e."createdAt" >= fl.first_login_time
+      AND e."createdAt" < fl.first_login_time
+          + make_interval(days => (SELECT onboarding_window_days FROM params))
+  )
+),
+first_login AS (SELECT * FROM first_login_all WHERE user_id IN (SELECT user_id FROM onboarders)),
+activity_weeks AS (SELECT DISTINCT user_id, week_start FROM events),
+user_week_age AS (
+  SELECT aw.user_id, fl.cohort_week_start,
+         ((aw.week_start - DATE_TRUNC('week',fl.first_login_time)::date)/7)::int AS user_lifetime_week
+  FROM activity_weeks aw JOIN first_login fl USING (user_id)
+  WHERE aw.week_start >= DATE_TRUNC('week',fl.first_login_time)::date
+),
+bounded_counts AS (
+  SELECT cohort_week_start, user_lifetime_week, COUNT(DISTINCT user_id) AS active_users_bounded
+  FROM user_week_age WHERE user_lifetime_week >= 0 GROUP BY 1,2
+),
+last_active AS (
+  SELECT cohort_week_start, user_id, MAX(user_lifetime_week) AS last_active_week FROM user_week_age GROUP BY 1,2
+),
+unbounded_counts AS (
+  SELECT la.cohort_week_start, gs AS user_lifetime_week, COUNT(*) AS retained_users_unbounded
+  FROM last_active la
+  CROSS JOIN LATERAL generate_series(0, LEAST(la.last_active_week,(SELECT max_weeks FROM params))) gs
+  GROUP BY 1,2
+),
+cohort_sizes AS (SELECT cohort_week_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_login GROUP BY 1),
+cohort_caps AS (
+  SELECT cs.cohort_week_start, cs.cohort_users,
+         LEAST((SELECT max_weeks FROM params),
+               GREATEST(0,((DATE_TRUNC('week',CURRENT_DATE)::date-cs.cohort_week_start)/7)::int)) AS cap_weeks
+  FROM cohort_sizes cs
+),
+grid AS (
+  SELECT cc.cohort_week_start, gs AS user_lifetime_week, cc.cohort_users
+  FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_weeks) gs
+)
+SELECT
+  g.cohort_week_start,
+  TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')                               AS cohort_label,
+  TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')||' (n='||g.cohort_users||')'  AS cohort_label_n,
+  g.user_lifetime_week, g.cohort_users,
+  COALESCE(b.active_users_bounded,0)     AS active_users_bounded,
+  COALESCE(u.retained_users_unbounded,0) AS retained_users_unbounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(b.active_users_bounded,0)::float/g.cohort_users END    AS retention_rate_bounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(u.retained_users_unbounded,0)::float/g.cohort_users END AS retention_rate_unbounded,
+  CASE WHEN g.user_lifetime_week=0 THEN g.cohort_users ELSE 0 END         AS cohort_users_w0
+FROM grid g
+LEFT JOIN bounded_counts   b ON b.cohort_week_start=g.cohort_week_start AND b.user_lifetime_week=g.user_lifetime_week
+LEFT JOIN unbounded_counts u ON u.cohort_week_start=g.cohort_week_start AND u.user_lifetime_week=g.user_lifetime_week
+ORDER BY g.cohort_week_start, g.user_lifetime_week;

autogpt_platform/analytics/queries/retention_login_weekly.sql

@@ -0,0 +1,103 @@
+-- =============================================================
+-- View: analytics.retention_login_weekly
+-- Looker source alias: ds83  |  Charts: 2
+-- =============================================================
+-- DESCRIPTION
+--   Weekly cohort retention based on login sessions.
+--   Users are grouped by the ISO week of their first ever login.
+--   For each cohort × lifetime-week combination, outputs both:
+--     - bounded rate: % active in exactly that week
+--     - unbounded rate: % who were ever active on or after that week
+--   Weeks are capped to the cohort's actual age (no future data points).
+--
+-- SOURCE TABLES
+--   auth.sessions  — Login session records
+--
+-- HOW TO READ THE OUTPUT
+--   cohort_week_start   The Monday of the week users first logged in
+--   user_lifetime_week  0 = signup week, 1 = one week later, etc.
+--   retention_rate_bounded   = active_users_bounded / cohort_users
+--   retention_rate_unbounded = retained_users_unbounded / cohort_users
+--
+-- OUTPUT COLUMNS
+--   cohort_week_start         DATE     First day of the cohort's signup week
+--   cohort_label              TEXT     ISO week label (e.g. '2025-W01')
+--   cohort_label_n            TEXT     ISO week label with cohort size (e.g. '2025-W01 (n=42)')
+--   user_lifetime_week        INT      Weeks since first login (0 = signup week)
+--   cohort_users              BIGINT   Total users in this cohort (denominator)
+--   active_users_bounded      BIGINT   Users active in exactly week k
+--   retained_users_unbounded  BIGINT   Users active any time on/after week k
+--   retention_rate_bounded    FLOAT    bounded active / cohort_users
+--   retention_rate_unbounded  FLOAT    unbounded retained / cohort_users
+--   cohort_users_w0           BIGINT   cohort_users only at week 0, else 0 (safe to SUM in pivot tables)
+--
+-- EXAMPLE QUERIES
+--   -- Week-1 retention rate per cohort
+--   SELECT cohort_label, retention_rate_bounded AS w1_retention
+--   FROM analytics.retention_login_weekly
+--   WHERE user_lifetime_week = 1
+--   ORDER BY cohort_week_start;
+--
+--   -- Overall average retention curve (all cohorts combined)
+--   SELECT user_lifetime_week,
+--          SUM(active_users_bounded)::float / NULLIF(SUM(cohort_users_w0), 0) AS avg_retention
+--   FROM analytics.retention_login_weekly
+--   GROUP BY 1 ORDER BY 1;
+-- =============================================================
+
+WITH params AS (SELECT 12::int AS max_weeks),
+events AS (
+  SELECT s.user_id::text AS user_id, s.created_at::timestamptz AS created_at,
+         DATE_TRUNC('week', s.created_at)::date AS week_start
+  FROM auth.sessions s WHERE s.user_id IS NOT NULL
+),
+first_login AS (
+  SELECT user_id, MIN(created_at) AS first_login_time,
+         DATE_TRUNC('week', MIN(created_at))::date AS cohort_week_start
+  FROM events GROUP BY 1
+),
+activity_weeks AS (SELECT DISTINCT user_id, week_start FROM events),
+user_week_age AS (
+  SELECT aw.user_id, fl.cohort_week_start,
+         ((aw.week_start - DATE_TRUNC('week', fl.first_login_time)::date) / 7)::int AS user_lifetime_week
+  FROM activity_weeks aw JOIN first_login fl USING (user_id)
+  WHERE aw.week_start >= DATE_TRUNC('week', fl.first_login_time)::date
+),
+bounded_counts AS (
+  SELECT cohort_week_start, user_lifetime_week, COUNT(DISTINCT user_id) AS active_users_bounded
+  FROM user_week_age WHERE user_lifetime_week >= 0 GROUP BY 1,2
+),
+last_active AS (
+  SELECT cohort_week_start, user_id, MAX(user_lifetime_week) AS last_active_week FROM user_week_age GROUP BY 1,2
+),
+unbounded_counts AS (
+  SELECT la.cohort_week_start, gs AS user_lifetime_week, COUNT(*) AS retained_users_unbounded
+  FROM last_active la
+  CROSS JOIN LATERAL generate_series(0, LEAST(la.last_active_week,(SELECT max_weeks FROM params))) gs
+  GROUP BY 1,2
+),
+cohort_sizes AS (SELECT cohort_week_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_login GROUP BY 1),
+cohort_caps AS (
+  SELECT cs.cohort_week_start, cs.cohort_users,
+         LEAST((SELECT max_weeks FROM params),
+               GREATEST(0,((DATE_TRUNC('week',CURRENT_DATE)::date - cs.cohort_week_start)/7)::int)) AS cap_weeks
+  FROM cohort_sizes cs
+),
+grid AS (
+  SELECT cc.cohort_week_start, gs AS user_lifetime_week, cc.cohort_users
+  FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_weeks) gs
+)
+SELECT
+  g.cohort_week_start,
+  TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')                                    AS cohort_label,
+  TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')||' (n='||g.cohort_users||')'       AS cohort_label_n,
+  g.user_lifetime_week, g.cohort_users,
+  COALESCE(b.active_users_bounded,0)     AS active_users_bounded,
+  COALESCE(u.retained_users_unbounded,0) AS retained_users_unbounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(b.active_users_bounded,0)::float/g.cohort_users END    AS retention_rate_bounded,
+  CASE WHEN g.cohort_users>0 THEN COALESCE(u.retained_users_unbounded,0)::float/g.cohort_users END AS retention_rate_unbounded,
+  CASE WHEN g.user_lifetime_week=0 THEN g.cohort_users ELSE 0 END               AS cohort_users_w0
+FROM grid g
+LEFT JOIN bounded_counts   b ON b.cohort_week_start=g.cohort_week_start AND b.user_lifetime_week=g.user_lifetime_week
+LEFT JOIN unbounded_counts u ON u.cohort_week_start=g.cohort_week_start AND u.user_lifetime_week=g.user_lifetime_week
+ORDER BY g.cohort_week_start, g.user_lifetime_week

autogpt_platform/analytics/queries/user_block_spending.sql

@@ -0,0 +1,71 @@
+-- =============================================================
+-- View: analytics.user_block_spending
+-- Looker source alias: ds6  |  Charts: 5
+-- =============================================================
+-- DESCRIPTION
+--   One row per credit transaction (last 90 days).
+--   Shows how users spend credits broken down by block type,
+--   LLM provider and model.  Joins node execution stats for
+--   token-level detail.
+--
+-- SOURCE TABLES
+--   platform.CreditTransaction   — Credit debit/credit records
+--   platform.AgentNodeExecution  — Node execution stats (for token counts)
+--
+-- OUTPUT COLUMNS
+--   transactionKey        TEXT         Unique transaction identifier
+--   userId                TEXT         User who was charged
+--   amount                DECIMAL      Credit amount (positive = credit, negative = debit)
+--   negativeAmount        DECIMAL      amount * -1 (convenience for spend charts)
+--   transactionType       TEXT         Transaction type (e.g. 'USAGE', 'REFUND', 'TOP_UP')
+--   transactionTime       TIMESTAMPTZ  When the transaction was recorded
+--   blockId               TEXT         Block UUID that triggered the spend
+--   blockName             TEXT         Human-readable block name
+--   llm_provider          TEXT         LLM provider (e.g. 'openai', 'anthropic')
+--   llm_model             TEXT         Model name (e.g. 'gpt-4o', 'claude-3-5-sonnet')
+--   node_exec_id          TEXT         Linked node execution UUID
+--   llm_call_count        INT          LLM API calls made in that execution
+--   llm_retry_count       INT          LLM retries in that execution
+--   llm_input_token_count INT          Input tokens consumed
+--   llm_output_token_count INT         Output tokens produced
+--
+-- WINDOW
+--   Rolling 90 days (createdAt > CURRENT_DATE - 90 days)
+--
+-- EXAMPLE QUERIES
+--   -- Total spend per user (last 90 days)
+--   SELECT "userId", SUM("negativeAmount") AS total_spent
+--   FROM analytics.user_block_spending
+--   WHERE "transactionType" = 'USAGE'
+--   GROUP BY 1 ORDER BY total_spent DESC;
+--
+--   -- Spend by LLM provider + model
+--   SELECT "llm_provider", "llm_model",
+--          SUM("negativeAmount") AS total_cost,
+--          SUM("llm_input_token_count") AS input_tokens,
+--          SUM("llm_output_token_count") AS output_tokens
+--   FROM analytics.user_block_spending
+--   WHERE "llm_provider" IS NOT NULL
+--   GROUP BY 1, 2 ORDER BY total_cost DESC;
+-- =============================================================
+
+SELECT
+    c."transactionKey"                                        AS transactionKey,
+    c."userId"                                                AS userId,
+    c."amount"                                                AS amount,
+    c."amount" * -1                                           AS negativeAmount,
+    c."type"                                                  AS transactionType,
+    c."createdAt"                                             AS transactionTime,
+    c.metadata->>'block_id'                                   AS blockId,
+    c.metadata->>'block'                                      AS blockName,
+    c.metadata->'input'->'credentials'->>'provider'           AS llm_provider,
+    c.metadata->'input'->>'model'                             AS llm_model,
+    c.metadata->>'node_exec_id'                               AS node_exec_id,
+    (ne."stats"->>'llm_call_count')::int                       AS llm_call_count,
+    (ne."stats"->>'llm_retry_count')::int                      AS llm_retry_count,
+    (ne."stats"->>'input_token_count')::int                    AS llm_input_token_count,
+    (ne."stats"->>'output_token_count')::int                   AS llm_output_token_count
+FROM platform."CreditTransaction" c
+LEFT JOIN platform."AgentNodeExecution" ne
+       ON (c.metadata->>'node_exec_id') = ne."id"::text
+WHERE c."createdAt" > CURRENT_DATE - INTERVAL '90 days'

autogpt_platform/analytics/queries/user_onboarding.sql

@@ -0,0 +1,45 @@
+-- =============================================================
+-- View: analytics.user_onboarding
+-- Looker source alias: ds68  |  Charts: 3
+-- =============================================================
+-- DESCRIPTION
+--   One row per user onboarding record.  Contains the user's
+--   stated usage reason, selected integrations, completed
+--   onboarding steps and optional first agent selection.
+--   Full history (no date filter) since onboarding happens
+--   once per user.
+--
+-- SOURCE TABLES
+--   platform.UserOnboarding  — Onboarding state per user
+--
+-- OUTPUT COLUMNS
+--   id                            TEXT         Onboarding record UUID
+--   createdAt                     TIMESTAMPTZ  When onboarding started
+--   updatedAt                     TIMESTAMPTZ  Last update to onboarding state
+--   usageReason                   TEXT         Why user signed up (e.g. 'work', 'personal')
+--   integrations                  TEXT[]       Array of integration names the user selected
+--   userId                        TEXT         User UUID
+--   completedSteps                TEXT[]       Array of onboarding step enums completed
+--   selectedStoreListingVersionId TEXT         First marketplace agent the user chose (if any)
+--
+-- EXAMPLE QUERIES
+--   -- Usage reason breakdown
+--   SELECT "usageReason", COUNT(*) FROM analytics.user_onboarding GROUP BY 1;
+--
+--   -- Completion rate per step
+--   SELECT step, COUNT(*) AS users_completed
+--   FROM analytics.user_onboarding
+--   CROSS JOIN LATERAL UNNEST("completedSteps") AS step
+--   GROUP BY 1 ORDER BY users_completed DESC;
+-- =============================================================
+
+SELECT
+    id,
+    "createdAt",
+    "updatedAt",
+    "usageReason",
+    integrations,
+    "userId",
+    "completedSteps",
+    "selectedStoreListingVersionId"
+FROM platform."UserOnboarding"

autogpt_platform/analytics/queries/user_onboarding_funnel.sql

@@ -0,0 +1,100 @@
+-- =============================================================
+-- View: analytics.user_onboarding_funnel
+-- Looker source alias: ds74  |  Charts: 1
+-- =============================================================
+-- DESCRIPTION
+--   Pre-aggregated onboarding funnel showing how many users
+--   completed each step and the drop-off percentage from the
+--   previous step.  One row per onboarding step (all 22 steps
+--   always present, even with 0 completions — prevents sparse
+--   gaps from making LAG compare the wrong predecessors).
+--
+-- SOURCE TABLES
+--   platform.UserOnboarding  — Onboarding records with completedSteps array
+--
+-- OUTPUT COLUMNS
+--   step             TEXT     Onboarding step enum name (e.g. 'WELCOME', 'CONGRATS')
+--   step_order       INT      Numeric position in the funnel (1=first, 22=last)
+--   users_completed  BIGINT   Distinct users who completed this step
+--   pct_from_prev    NUMERIC  % of users from the previous step who reached this one
+--
+-- STEP ORDER
+--   1  WELCOME               9  MARKETPLACE_VISIT     17  SCHEDULE_AGENT
+--   2  USAGE_REASON         10  MARKETPLACE_ADD_AGENT  18  RUN_AGENTS
+--   3  INTEGRATIONS         11  MARKETPLACE_RUN_AGENT  19  RUN_3_DAYS
+--   4  AGENT_CHOICE         12  BUILDER_OPEN           20  TRIGGER_WEBHOOK
+--   5  AGENT_NEW_RUN        13  BUILDER_SAVE_AGENT     21  RUN_14_DAYS
+--   6  AGENT_INPUT          14  BUILDER_RUN_AGENT      22  RUN_AGENTS_100
+--   7  CONGRATS             15  VISIT_COPILOT
+--   8  GET_RESULTS          16  RE_RUN_AGENT
+--
+-- WINDOW
+--   Users who started onboarding in the last 90 days
+--
+-- EXAMPLE QUERIES
+--   -- Full funnel
+--   SELECT * FROM analytics.user_onboarding_funnel ORDER BY step_order;
+--
+--   -- Biggest drop-off point
+--   SELECT step, pct_from_prev FROM analytics.user_onboarding_funnel
+--   ORDER BY pct_from_prev ASC LIMIT 3;
+-- =============================================================
+
+WITH all_steps AS (
+  -- Complete ordered grid of all 22 steps so zero-completion steps
+  -- are always present, keeping LAG comparisons correct.
+  SELECT step_name, step_order
+  FROM (VALUES
+    ('WELCOME',               1),
+    ('USAGE_REASON',          2),
+    ('INTEGRATIONS',          3),
+    ('AGENT_CHOICE',          4),
+    ('AGENT_NEW_RUN',         5),
+    ('AGENT_INPUT',           6),
+    ('CONGRATS',              7),
+    ('GET_RESULTS',           8),
+    ('MARKETPLACE_VISIT',     9),
+    ('MARKETPLACE_ADD_AGENT', 10),
+    ('MARKETPLACE_RUN_AGENT', 11),
+    ('BUILDER_OPEN',          12),
+    ('BUILDER_SAVE_AGENT',    13),
+    ('BUILDER_RUN_AGENT',     14),
+    ('VISIT_COPILOT',         15),
+    ('RE_RUN_AGENT',          16),
+    ('SCHEDULE_AGENT',        17),
+    ('RUN_AGENTS',            18),
+    ('RUN_3_DAYS',            19),
+    ('TRIGGER_WEBHOOK',       20),
+    ('RUN_14_DAYS',           21),
+    ('RUN_AGENTS_100',        22)
+  ) AS t(step_name, step_order)
+),
+raw AS (
+  SELECT
+      u."userId",
+      step_txt::text AS step
+  FROM platform."UserOnboarding" u
+  CROSS JOIN LATERAL UNNEST(u."completedSteps") AS step_txt
+  WHERE u."createdAt" >= CURRENT_DATE - INTERVAL '90 days'
+),
+step_counts AS (
+  SELECT step, COUNT(DISTINCT "userId") AS users_completed
+  FROM raw GROUP BY step
+),
+funnel AS (
+  SELECT
+      a.step_name                          AS step,
+      a.step_order,
+      COALESCE(sc.users_completed, 0)      AS users_completed,
+      ROUND(
+        100.0 * COALESCE(sc.users_completed, 0)
+        / NULLIF(
+            LAG(COALESCE(sc.users_completed, 0)) OVER (ORDER BY a.step_order),
+            0
+          ),
+        2
+      )                                    AS pct_from_prev
+  FROM all_steps a
+  LEFT JOIN step_counts sc ON sc.step = a.step_name
+)
+SELECT * FROM funnel ORDER BY step_order

autogpt_platform/analytics/queries/user_onboarding_integration.sql

@@ -0,0 +1,41 @@
+-- =============================================================
+-- View: analytics.user_onboarding_integration
+-- Looker source alias: ds75  |  Charts: 1
+-- =============================================================
+-- DESCRIPTION
+--   Pre-aggregated count of users who selected each integration
+--   during onboarding.  One row per integration type, sorted
+--   by popularity.
+--
+-- SOURCE TABLES
+--   platform.UserOnboarding  — integrations array column
+--
+-- OUTPUT COLUMNS
+--   integration            TEXT    Integration name (e.g. 'github', 'slack', 'notion')
+--   users_with_integration BIGINT  Distinct users who selected this integration
+--
+-- WINDOW
+--   Users who started onboarding in the last 90 days
+--
+-- EXAMPLE QUERIES
+--   -- Full integration popularity ranking
+--   SELECT * FROM analytics.user_onboarding_integration;
+--
+--   -- Top 5 integrations
+--   SELECT * FROM analytics.user_onboarding_integration LIMIT 5;
+-- =============================================================
+
+WITH exploded AS (
+  SELECT
+      u."userId" AS user_id,
+      UNNEST(u."integrations") AS integration
+  FROM platform."UserOnboarding" u
+  WHERE u."createdAt" >= CURRENT_DATE - INTERVAL '90 days'
+)
+SELECT
+    integration,
+    COUNT(DISTINCT user_id) AS users_with_integration
+FROM exploded
+WHERE integration IS NOT NULL AND integration <> ''
+GROUP BY integration
+ORDER BY users_with_integration DESC

autogpt_platform/analytics/queries/users_activities.sql

@@ -0,0 +1,145 @@
+-- =============================================================
+-- View: analytics.users_activities
+-- Looker source alias: ds56  |  Charts: 5
+-- =============================================================
+-- DESCRIPTION
+--   One row per user with lifetime activity summary.
+--   Joins login sessions with agent graphs, executions and
+--   node-level runs to give a full picture of how engaged
+--   each user is.  Includes a convenience flag for 7-day
+--   activation (did the user return at least 7 days after
+--   their first login?).
+--
+-- SOURCE TABLES
+--   auth.sessions                    — Login/session records
+--   platform.AgentGraph              — Graphs (agents) built by the user
+--   platform.AgentGraphExecution     — Agent run history
+--   platform.AgentNodeExecution      — Individual block execution history
+--
+-- PERFORMANCE NOTE
+--   Each CTE aggregates its own table independently by userId.
+--   This avoids the fan-out that occurs when driving every join
+--   from user_logins across the two largest tables
+--   (AgentGraphExecution and AgentNodeExecution).
+--
+-- OUTPUT COLUMNS
+--   user_id                   TEXT         Supabase user UUID
+--   first_login_time          TIMESTAMPTZ  First ever session created_at
+--   last_login_time           TIMESTAMPTZ  Most recent session created_at
+--   last_visit_time           TIMESTAMPTZ  Max of last refresh or login
+--   last_agent_save_time      TIMESTAMPTZ  Last time user saved an agent graph
+--   agent_count               BIGINT       Number of distinct active graphs built (0 if none)
+--   first_agent_run_time      TIMESTAMPTZ  First ever graph execution
+--   last_agent_run_time       TIMESTAMPTZ  Most recent graph execution
+--   unique_agent_runs         BIGINT       Distinct agent graphs ever run (0 if none)
+--   agent_runs                BIGINT       Total graph execution count (0 if none)
+--   node_execution_count      BIGINT       Total node executions across all runs
+--   node_execution_failed     BIGINT       Node executions with FAILED status
+--   node_execution_completed  BIGINT       Node executions with COMPLETED status
+--   node_execution_terminated BIGINT       Node executions with TERMINATED status
+--   node_execution_queued     BIGINT       Node executions with QUEUED status
+--   node_execution_running    BIGINT       Node executions with RUNNING status
+--   is_active_after_7d        INT          1=returned after day 7, 0=did not, NULL=too early to tell
+--   node_execution_incomplete BIGINT       Node executions with INCOMPLETE status
+--   node_execution_review     BIGINT       Node executions with REVIEW status
+--
+-- EXAMPLE QUERIES
+--   -- Users who ran at least one agent and returned after 7 days
+--   SELECT COUNT(*) FROM analytics.users_activities
+--   WHERE agent_runs > 0 AND is_active_after_7d = 1;
+--
+--   -- Top 10 most active users by agent runs
+--   SELECT user_id, agent_runs, node_execution_count
+--   FROM analytics.users_activities
+--   ORDER BY agent_runs DESC LIMIT 10;
+--
+--   -- 7-day activation rate
+--   SELECT
+--     SUM(CASE WHEN is_active_after_7d = 1 THEN 1 ELSE 0 END)::float
+--     / NULLIF(COUNT(CASE WHEN is_active_after_7d IS NOT NULL THEN 1 END), 0)
+--     AS activation_rate
+--   FROM analytics.users_activities;
+-- =============================================================
+
+WITH user_logins AS (
+  SELECT
+    user_id::text                                    AS user_id,
+    MIN(created_at)                                  AS first_login_time,
+    MAX(created_at)                                  AS last_login_time,
+    GREATEST(
+      MAX(refreshed_at)::timestamptz,
+      MAX(created_at)::timestamptz
+    )                                                AS last_visit_time
+  FROM auth.sessions
+  GROUP BY user_id
+),
+user_agents AS (
+  -- Aggregate AgentGraph directly by userId (no fan-out from user_logins)
+  SELECT
+    "userId"::text                AS user_id,
+    MAX("updatedAt")              AS last_agent_save_time,
+    COUNT(DISTINCT "id")          AS agent_count
+  FROM platform."AgentGraph"
+  WHERE "isActive"
+  GROUP BY "userId"
+),
+user_graph_runs AS (
+  -- Aggregate AgentGraphExecution directly by userId
+  SELECT
+    "userId"::text                        AS user_id,
+    MIN("createdAt")                      AS first_agent_run_time,
+    MAX("createdAt")                      AS last_agent_run_time,
+    COUNT(DISTINCT "agentGraphId")        AS unique_agent_runs,
+    COUNT("id")                           AS agent_runs
+  FROM platform."AgentGraphExecution"
+  GROUP BY "userId"
+),
+user_node_runs AS (
+  -- Aggregate AgentNodeExecution directly; resolve userId via a
+  -- single join to AgentGraphExecution instead of fanning out from
+  -- user_logins through both large tables.
+  SELECT
+    g."userId"::text                                                   AS user_id,
+    COUNT(*)                                                           AS node_execution_count,
+    COUNT(*) FILTER (WHERE n."executionStatus" = 'FAILED')             AS node_execution_failed,
+    COUNT(*) FILTER (WHERE n."executionStatus" = 'COMPLETED')          AS node_execution_completed,
+    COUNT(*) FILTER (WHERE n."executionStatus" = 'TERMINATED')         AS node_execution_terminated,
+    COUNT(*) FILTER (WHERE n."executionStatus" = 'QUEUED')             AS node_execution_queued,
+    COUNT(*) FILTER (WHERE n."executionStatus" = 'RUNNING')            AS node_execution_running,
+    COUNT(*) FILTER (WHERE n."executionStatus" = 'INCOMPLETE')         AS node_execution_incomplete,
+    COUNT(*) FILTER (WHERE n."executionStatus" = 'REVIEW')             AS node_execution_review
+  FROM platform."AgentNodeExecution" n
+  JOIN platform."AgentGraphExecution" g
+    ON g."id" = n."agentGraphExecutionId"
+  GROUP BY g."userId"
+)
+SELECT
+  ul.user_id,
+  ul.first_login_time,
+  ul.last_login_time,
+  ul.last_visit_time,
+  ua.last_agent_save_time,
+  COALESCE(ua.agent_count, 0)             AS agent_count,
+  gr.first_agent_run_time,
+  gr.last_agent_run_time,
+  COALESCE(gr.unique_agent_runs, 0)       AS unique_agent_runs,
+  COALESCE(gr.agent_runs, 0)              AS agent_runs,
+  COALESCE(nr.node_execution_count, 0)      AS node_execution_count,
+  COALESCE(nr.node_execution_failed, 0)     AS node_execution_failed,
+  COALESCE(nr.node_execution_completed, 0)  AS node_execution_completed,
+  COALESCE(nr.node_execution_terminated, 0) AS node_execution_terminated,
+  COALESCE(nr.node_execution_queued, 0)     AS node_execution_queued,
+  COALESCE(nr.node_execution_running, 0)    AS node_execution_running,
+  CASE
+    WHEN ul.first_login_time < NOW() - INTERVAL '7 days'
+     AND ul.last_visit_time  >= ul.first_login_time + INTERVAL '7 days' THEN 1
+    WHEN ul.first_login_time < NOW() - INTERVAL '7 days'
+     AND ul.last_visit_time  <  ul.first_login_time + INTERVAL '7 days' THEN 0
+    ELSE NULL
+  END AS is_active_after_7d,
+  COALESCE(nr.node_execution_incomplete, 0) AS node_execution_incomplete,
+  COALESCE(nr.node_execution_review, 0)     AS node_execution_review
+FROM user_logins ul
+LEFT JOIN user_agents     ua ON ul.user_id = ua.user_id
+LEFT JOIN user_graph_runs gr ON ul.user_id = gr.user_id
+LEFT JOIN user_node_runs  nr ON ul.user_id = nr.user_id

autogpt_platform/backend/generate_views.py

@@ -0,0 +1,246 @@
+#!/usr/bin/env python3
+"""
+AutoGPT Analytics — View Generator
+====================================
+Reads every .sql file in analytics/queries/ and registers it as a
+CREATE OR REPLACE VIEW in the analytics schema.
+
+Quick start (from autogpt_platform/backend/):
+
+Step 1 — one-time setup (creates schema, role, grants):
+
+  poetry run analytics-setup
+
+Step 2 — create / refresh all 14 analytics views:
+
+  poetry run analytics-views
+
+Both commands auto-detect credentials from .env (DB_* vars).
+Use --db-url to override.
+
+Step 3 (optional) — enable login and set a password for the read-only
+role so external tools (Supabase MCP, PostHog Data Warehouse) can connect.
+The role is created as NOLOGIN, so you must grant LOGIN at the same time.
+Run in Supabase SQL Editor:
+
+  ALTER ROLE analytics_readonly WITH LOGIN PASSWORD 'your-password';
+
+Usage
+-----
+  poetry run analytics-setup              # apply setup to DB
+  poetry run analytics-setup --dry-run   # print setup SQL only
+  poetry run analytics-views             # apply all views to DB
+  poetry run analytics-views --dry-run   # print all view SQL only
+  poetry run analytics-views --only graph_execution,retention_login_weekly
+
+Environment variables
+---------------------
+  DATABASE_URL   Postgres connection string (checked before .env)
+
+Notes
+-----
+- .env DB_* vars are read automatically as a fallback.
+- Safe to re-run: uses CREATE OR REPLACE VIEW.
+- Looker, PostHog Data Warehouse, and Supabase MCP all read from the
+  same analytics.* views — no raw tables exposed.
+"""
+
+import argparse
+import os
+import sys
+from pathlib import Path
+from urllib.parse import quote
+
+QUERIES_DIR = Path(__file__).parent.parent / "analytics" / "queries"
+ENV_FILE = Path(__file__).parent / ".env"
+SCHEMA = "analytics"
+
+SETUP_SQL = """\
+-- =============================================================
+-- AutoGPT Analytics Schema Setup
+-- Run ONCE as the postgres superuser (e.g. via Supabase SQL Editor).
+-- After this, run: poetry run analytics-views
+-- =============================================================
+
+-- 1. Create the analytics schema
+CREATE SCHEMA IF NOT EXISTS analytics;
+
+-- 2. Create the read-only role (skip if already exists)
+DO $$
+BEGIN
+  IF NOT EXISTS (SELECT FROM pg_roles WHERE rolname = 'analytics_readonly') THEN
+    CREATE ROLE analytics_readonly NOLOGIN;
+  END IF;
+END
+$$;
+
+-- 3. Analytics schema grants only.
+--    Views use security_invoker = false so they execute as their
+--    owner (postgres). analytics_readonly never needs direct access
+--    to the platform or auth schemas.
+GRANT USAGE ON SCHEMA analytics TO analytics_readonly;
+GRANT SELECT ON ALL TABLES IN SCHEMA analytics TO analytics_readonly;
+ALTER DEFAULT PRIVILEGES IN SCHEMA analytics
+  GRANT SELECT ON TABLES TO analytics_readonly;
+"""
+
+
+def load_db_url_from_env() -> str | None:
+    """Read DB_* vars from .env and build a psycopg2 connection string."""
+    if not ENV_FILE.exists():
+        return None
+    env: dict[str, str] = {}
+    for line in ENV_FILE.read_text().splitlines():
+        line = line.strip()
+        if not line or line.startswith("#") or "=" not in line:
+            continue
+        key, _, value = line.partition("=")
+        env[key.strip()] = value.strip().strip('"').strip("'")
+    host = env.get("DB_HOST", "localhost")
+    port = env.get("DB_PORT", "5432")
+    user = env.get("DB_USER", "postgres")
+    password = env.get("DB_PASS", "")
+    dbname = env.get("DB_NAME", "postgres")
+    if not password:
+        return None
+    return (
+        "postgresql://"
+        f"{quote(user, safe='')}:{quote(password, safe='')}"
+        f"@{host}:{port}/{quote(dbname, safe='')}"
+    )
+
+
+def get_db_url(args: argparse.Namespace) -> str | None:
+    return args.db_url or os.environ.get("DATABASE_URL") or load_db_url_from_env()
+
+
+def connect(db_url: str):
+    try:
+        import psycopg2
+    except ImportError:
+        print("psycopg2 not found. Run: poetry install", file=sys.stderr)
+        sys.exit(1)
+    return psycopg2.connect(db_url)
+
+
+def run_sql(db_url: str, statements: list[tuple[str, str]]) -> None:
+    """Execute a list of (label, sql) pairs in a single transaction."""
+    conn = connect(db_url)
+    conn.autocommit = False
+    cur = conn.cursor()
+    try:
+        for label, sql in statements:
+            print(f"  {label} ...", end=" ")
+            cur.execute(sql)
+            print("OK")
+        conn.commit()
+        print(f"\n✓ {len(statements)} statement(s) applied.")
+    except Exception as e:
+        conn.rollback()
+        print(f"\n✗ Error: {e}", file=sys.stderr)
+        sys.exit(1)
+    finally:
+        cur.close()
+        conn.close()
+
+
+def build_view_sql(name: str, query_body: str) -> str:
+    body = query_body.strip().rstrip(";")
+    # security_invoker = false → view runs as its owner (postgres), not the
+    # caller, so analytics_readonly only needs analytics schema access.
+    return f"CREATE OR REPLACE VIEW {SCHEMA}.{name} WITH (security_invoker = false) AS\n{body};\n"
+
+
+def load_views(only: list[str] | None = None) -> list[tuple[str, str]]:
+    """Return [(label, sql)] for all views, in alphabetical order."""
+    files = sorted(QUERIES_DIR.glob("*.sql"))
+    if not files:
+        print(f"No .sql files found in {QUERIES_DIR}", file=sys.stderr)
+        sys.exit(1)
+    known = {f.stem for f in files}
+    if only:
+        unknown = [n for n in only if n not in known]
+        if unknown:
+            print(
+                f"Unknown view name(s): {', '.join(unknown)}\n"
+                f"Available: {', '.join(sorted(known))}",
+                file=sys.stderr,
+            )
+            sys.exit(1)
+    result = []
+    for f in files:
+        name = f.stem
+        if only and name not in only:
+            continue
+        result.append((f"view analytics.{name}", build_view_sql(name, f.read_text())))
+    return result
+
+
+def no_db_url_error() -> None:
+    print(
+        "No database URL found.\n"
+        "Tried: --db-url, DATABASE_URL env var, and .env (DB_* vars).\n"
+        "Use --dry-run to just print the SQL.",
+        file=sys.stderr,
+    )
+    sys.exit(1)
+
+
+def cmd_setup(args: argparse.Namespace) -> None:
+    if args.dry_run:
+        print(SETUP_SQL)
+        return
+    db_url = get_db_url(args)
+    if not db_url:
+        no_db_url_error()
+    assert db_url
+    print("Applying analytics setup...")
+    run_sql(db_url, [("schema / role / grants", SETUP_SQL)])
+
+
+def cmd_views(args: argparse.Namespace) -> None:
+    only = [v.strip() for v in args.only.split(",")] if args.only else None
+    views = load_views(only=only)
+    if not views:
+        print("No matching views found.")
+        sys.exit(0)
+
+    if args.dry_run:
+        print(f"-- {len(views)} views\n")
+        for label, sql in views:
+            print(f"-- {label}")
+            print(sql)
+        return
+
+    db_url = get_db_url(args)
+    if not db_url:
+        no_db_url_error()
+    assert db_url
+    print(f"Applying {len(views)} view(s)...")
+    # Append grant refresh so the readonly role sees any new views
+    grant = f"GRANT SELECT ON ALL TABLES IN SCHEMA {SCHEMA} TO analytics_readonly;"
+    run_sql(db_url, views + [("grant analytics_readonly", grant)])
+
+
+def main_setup() -> None:
+    parser = argparse.ArgumentParser(description="Apply analytics schema setup to DB")
+    parser.add_argument(
+        "--dry-run", action="store_true", help="Print SQL, don't execute"
+    )
+    parser.add_argument("--db-url", help="Postgres connection string")
+    cmd_setup(parser.parse_args())
+
+
+def main_views() -> None:
+    parser = argparse.ArgumentParser(description="Apply analytics views to DB")
+    parser.add_argument(
+        "--dry-run", action="store_true", help="Print SQL, don't execute"
+    )
+    parser.add_argument("--db-url", help="Postgres connection string")
+    parser.add_argument("--only", help="Comma-separated view names to update")
+    cmd_views(parser.parse_args())
+
+
+if __name__ == "__main__":
+    # Default: apply views (backwards-compatible with direct python invocation)
+    main_views()

autogpt_platform/backend/pyproject.toml

@@ -120,6 +120,8 @@ ws = "backend.ws:main"
 scheduler = "backend.scheduler:main"
 notification = "backend.notification:main"
 executor = "backend.exec:main"
+analytics-setup = "generate_views:main_setup"
+analytics-views = "generate_views:main_views"
 copilot-executor = "backend.copilot.executor.__main__:main"
 cli = "backend.cli:main"
 format = "linter:format"