Agent Analytics

Agent Analytics — Website analytics your AI agent fully operates

Simple, privacy-first website analytics and growth toolkit that your AI agent controls end-to-end. Track page views, custom events, conversion funnels, user retention, and A/B experiments across all your projects — then talk to your analytics in natural language. No dashboards. Your agent creates projects, adds tracking code, queries traffic data, builds funnels, runs experiments, and tells you what to optimize next. A lightweight Plausible/Mixpanel/PostHog alternative built for the AI agent era.

Security & trust

• Open source: Full source at github.com/Agent-Analytics/agent-analytics-cli — inspect every command before running
• Read-only by default: The CLI only reads analytics data. Write operations (creating projects, experiments) require explicit user-provided API keys
• No arbitrary code execution: All CLI commands use structured flags (--days, --property, --steps). No eval, no shell interpolation, no dynamic code generation
• Scoped permissions: The API key controls access. The CLI never requests filesystem, network, or system-level permissions beyond HTTP calls to api.agentanalytics.sh
• Published on npm: @agent-analytics/cli — versioned, auditable, standard npm supply chain

Philosophy

You are NOT Mixpanel. Don't track everything. Track only what answers: "Is this project alive and growing?"

For a typical site, that's 3-5 custom events max on top of automatic page views.

First-time setup

Get an API key: Sign up at agentanalytics.sh and generate a key from the dashboard. Alternatively, self-host the open-source version from GitHub.

If the project doesn't have tracking yet:

# 1. Login (one time — uses your API key)
npx @agent-analytics/cli login --token aak_YOUR_API_KEY

# 2. Create the project (returns a project write token)
npx @agent-analytics/cli create my-site --domain https://mysite.com

# 3. Add the snippet (Step 1 below) using the returned token
# 4. Deploy, click around, verify:
npx @agent-analytics/cli events my-site

The create command returns a project write token — use it as data-token in the snippet below. This is separate from your API key (which is for reading/querying).

Step 1: Add the tracking snippet

The create command returns a tracking snippet with your project token — add it before </body>. It auto-tracks page_view events with path, referrer, browser, OS, device, screen size, and UTM params. You do NOT need to add custom page_view events.

Step 1b: Discover existing events (existing projects)

If tracking is already set up, check what events and property keys are already in use so you match the naming:

npx @agent-analytics/cli properties-received PROJECT_NAME

This shows which property keys each event type uses (e.g. cta_click → id, signup → method). Match existing naming before adding new events.

Step 2: Add custom events to important actions

Use onclick handlers on the elements that matter:

<a href="..." {id: 'ELEMENT_ID'})">

The ?. operator ensures no error if the tracker hasn't loaded yet.

Standard events for 80% of SaaS sites

Pick the ones that apply. Most sites need 2-4:

What to track as cta_click

Only buttons that indicate conversion intent:

• "Get Started" / "Sign Up" / "Try Free" buttons
• "Upgrade" / "Buy" / pricing CTAs
• Primary navigation to signup/dashboard
• "View on GitHub" / "Star" (for open source projects)

What NOT to track

• Every link or button (too noisy)
• Scroll depth (not actionable)
• Form field interactions (too granular)
• Footer links (low signal)

Property naming rules

• Use snake_case: hero_get_started not heroGetStarted
• The id property identifies WHICH element: short, descriptive
• Name IDs as section_action: hero_signup, pricing_pro, nav_dashboard
• Don't encode data the page_view already captures (path, referrer, browser)

Step 2b: Run A/B experiments (Pro)

Experiments let you test which variant of a page element converts better. The full lifecycle is API-driven — no dashboard UI needed.

Creating an experiment

npx @agent-analytics/cli experiments create my-site \
  --name signup_cta --variants control,new_cta --goal signup

Implementing variants

Declarative (recommended): Use data-aa-experiment and data-aa-variant-{key} HTML attributes. Original content is the control. The tracker swaps text for assigned variants automatically.

<h1 data-aa-experiment="signup_cta" data-aa-variant-new_cta="Start Free Trial">Sign Up</h1>

Programmatic (complex cases): Use window.aa?.experiment(name, variants) — deterministic, same user always gets same variant.

Exposure events ($experiment_exposure) are tracked automatically once per session. Track the goal event normally: window.aa?.track('signup', {method: 'github'}).

Checking results

npx @agent-analytics/cli experiments get exp_abc123

Returns Bayesian probability_best, lift, and a recommendation. The system needs ~100 exposures per variant before results are significant.

Managing experiments

# Pause (stops assigning new users)
npx @agent-analytics/cli experiments pause exp_abc123

# Resume
npx @agent-analytics/cli experiments resume exp_abc123

# Complete with a winner
npx @agent-analytics/cli experiments complete exp_abc123 --winner new_cta

# Delete
npx @agent-analytics/cli experiments delete exp_abc123

Best practices

• Name experiments with snake_case: signup_cta, pricing_layout, hero_copy
• Use 2 variants (A/B) unless you have high traffic — more variants need more data
• Set a clear goal_event that maps to a business outcome (signup, purchase, not page_view)
• Let experiments run until sufficient_data: true before picking a winner
• Complete the experiment when done: experiments complete <id> --winner new_cta

Step 3: Test immediately

After adding tracking, verify it works:

# Option A: Browser console on your site:
window.aa.track('test_event', {source: 'manual_test'})

# Option B: Click around, then check:
npx @agent-analytics/cli events PROJECT_NAME

# Events appear within seconds.

Querying the data

All commands use npx @agent-analytics/cli. Your agent uses the CLI directly — no curl needed.

CLI reference

# Setup
npx @agent-analytics/cli login --token aak_YOUR_KEY    # Save API key (one time)
npx @agent-analytics/cli projects                       # List all projects
npx @agent-analytics/cli create my-site --domain https://mysite.com  # Create project

# Real-time
npx @agent-analytics/cli live                           # Live terminal dashboard across ALL projects
npx @agent-analytics/cli live my-site                   # Live view for one project

# Analytics
npx @agent-analytics/cli stats my-site --days 7         # Overview: events, users, daily trends
npx @agent-analytics/cli insights my-site --period 7d   # Period-over-period comparison
npx @agent-analytics/cli breakdown my-site --property path --event page_view --limit 10  # Top pages/referrers/UTM
npx @agent-analytics/cli pages my-site --type entry     # Landing page performance & bounce rates
npx @agent-analytics/cli sessions-dist my-site          # Session engagement histogram
npx @agent-analytics/cli heatmap my-site                # Peak hours & busiest days
npx @agent-analytics/cli events my-site --days 30       # Raw event log
npx @agent-analytics/cli sessions my-site               # Individual session records
npx @agent-analytics/cli properties my-site             # Discover event names & property keys
npx @agent-analytics/cli properties-received my-site    # Property keys per event type (sampled)
npx @agent-analytics/cli query my-site --metrics event_count,unique_users --group-by date  # Flexible query
npx @agent-analytics/cli funnel my-site --steps "page_view,signup,purchase"  # Funnel drop-off analysis
npx @agent-analytics/cli funnel my-site --steps "page_view,signup" --breakdown country  # Funnel segmented by country
npx @agent-analytics/cli retention my-site --period week --cohorts 8        # Cohort retention analysis

# A/B experiments (pro)
npx @agent-analytics/cli experiments list my-site
npx @agent-analytics/cli experiments create my-site --name signup_cta --variants control,new_cta --goal signup
npx @agent-analytics/cli experiments get exp_abc123
npx @agent-analytics/cli experiments complete exp_abc123 --winner new_cta

# Account
npx @agent-analytics/cli whoami                         # Show account & tier
npx @agent-analytics/cli revoke-key                     # Rotate API key

Key flags:

• --days <N> — lookback window (default: 7; for stats, events)
• --limit <N> — max rows returned (default: 100)
• --since <date> — ISO date cutoff (properties-received only)
• --period <P> — comparison period: 1d, 7d, 14d, 30d, 90d (insights) or cohort grouping: day, week, month (retention)
• --property <key> — property key to group by (breakdown, required)
• --event <name> — filter by event name (breakdown) or first-seen event filter (retention)
• --returning-event <name> — what counts as "returned" (retention, defaults to same as --event)
• --cohorts <N> — number of cohort periods, 1-30 (retention, default: 8)
• --type <T> — page type: entry, exit, both (pages only, default: entry)
• --steps <csv> — comma-separated event names, 2-8 steps max (funnel, required)
• --window <N> — conversion window in hours (funnel, default: 168) or live time window in seconds (live, default: 60)
• --count-by <field> — user_id or session_id (funnel only)
• --breakdown <key> — segment funnel by a property (e.g. country, variant) — extracted from step 1 events (funnel only)
• --breakdown-limit <N> — max breakdown groups, 1-50 (funnel, default: 10)
• --interval <N> — live refresh in seconds (default: 5)

The live command

npx @agent-analytics/cli live opens a real-time TUI dashboard that refreshes every 5 seconds. It shows active visitors, sessions, and events/min across all your projects, plus top pages and recent events. Note: this is an interactive terminal UI — it clears the screen on each refresh, so it works best when run directly in a terminal rather than captured as output.

Which endpoint for which question

Match the user's question to the right call(s):

For any "how is X doing" question, always call insights first — it's the single most useful endpoint. For real-time "who's on the site right now", use live.

Analyze, don't just query

Don't return raw numbers. Interpret them. Here's how to turn each endpoint's response into something useful.

/insights → The headline

API returns metrics with current, previous, change, change_pct, and a trend field.

How to interpret:

• change_pct > 10 → "Growing" — call it out positively
• change_pct between -10 and 10 → "Stable" — mention it's steady
• change_pct < -10 → "Declining" — flag it, suggest investigating
• bounce_rate current vs previous → say "improved" (went down) or "worsened" (went up)
• avg_duration → convert ms to seconds: Math.round(value / 1000)
• Previous period is all zeros → say "new project, no prior data to compare"

Example output:

This week vs last: 173 events (+22%), 98 users (+18%).
Bounce rate: 87% (up from 82% — getting worse).
Average session: 24s. Trend: growing.

/breakdown → The ranking

API returns values: [{ value, count, unique_users }] sorted by count DESC.

How to interpret:

• Top 3-5 values is enough — don't dump the full list
• Show the unique_users too — 100 events from 2 users is very different from 100 events from 80 users
• Use total_with_property / total_events to note coverage: "155 of 155 page views have a path"
• For referrers: group "(direct)" / empty as direct traffic

Example output:

Top pages: / (98 views, 75 users), /pricing (33 views, 25 users), /docs (19 views, 4 users).
The /docs page has high repeat visits (19 views, 4 users) — power users.

/pages → Landing page quality

API returns entry_pages: [{ page, sessions, bounces, bounce_rate, avg_duration, avg_events }].

How to interpret:

• bounce_rate > 0.7 → "high bounce, needs work above the fold"
• bounce_rate < 0.3 → "strong landing page"
• avg_duration → convert ms to seconds; < 10s is concerning, > 60s is great
• avg_events → pages/session; 1.0 means everyone bounces, 3+ means good engagement
• Compare pages: "Your /pricing page converts 3× better than your homepage"

Example output:

Best landing page: /pricing — 14% bounce, 62s avg session, 4.1 pages/visit.
Worst: /blog/launch — 52% bounce, 18s avg. Consider a stronger CTA above the fold.

/sessions/distribution → Engagement shape

API returns distribution: [{ bucket, sessions, pct }], engaged_pct, median_bucket.

How to interpret:

• engaged_pct is the key number — sessions ≥30s as a percentage of total
• engaged_pct < 10% → "Most visitors leave immediately — focus on first impressions"
• engaged_pct 10-30% → "Moderate engagement, room to improve"
• engaged_pct > 30% → "Good engagement"
• If 80%+ is in the "0s" bucket, the site has a bounce problem
• If there's a healthy spread across buckets, engagement is genuine

Example output:

88% of sessions bounce instantly (0s). Only 6% stay longer than 30s.
The few who do engage stay 3-10 minutes — the content works, but first impressions don't.

/heatmap → Timing

API returns heatmap: [{ day, day_name, hour, events, users }], peak, busiest_day, busiest_hour.

How to interpret:

• peak is the single busiest slot — mention day + hour + timezone caveat (times are UTC)
• busiest_day → "Schedule blog posts/launches on this day"
• busiest_hour → "This is when your audience is online"
• Low-traffic windows → "Deploy during Sunday 3 AM UTC to minimize user impact"
• Weekend vs weekday split → tells you if audience is B2B (weekdays) or B2C (weekends)

Example output:

Peak: Friday at 11 PM UTC (35 events, 33 users). Busiest day overall: Sunday.
Traffic is heaviest on weekends — your audience browses on personal time.
Deploy on weekday mornings for minimal disruption.

/funnel → Where users drop off

CLI: funnel my-site --steps "page_view,signup,purchase". API: POST /funnel with JSON body.

API returns steps: [{ step, event, users, conversion_rate, drop_off_rate, avg_time_to_next_ms }] and overall_conversion_rate.

How to interpret:

• Each step shows how many users progressed from the previous step
• conversion_rate is step-to-step (step 2 users / step 1 users)
• drop_off_rate is 1 - conversion_rate at each step
• The biggest drop_off_rate is the bottleneck — focus optimization there
• avg_time_to_next_ms shows how long users take between steps (convert to hours/minutes)
• overall_conversion_rate is end-to-end (last step users / first step users)

Options:

• --steps "event1,event2,event3" — 2-8 step events (required)
• --window <hours> — max time from step 1 to last step (default: 168 = 7 days)
• --since <days> — lookback period, e.g. 30d (default: 30d)
• --count-by <field> — user_id (default) or session_id
• --breakdown <property> — segment funnel by a property (e.g. country, variant). Property is extracted from step 1 events. Returns overall + per-group results.
• --breakdown-limit <N> — max groups returned (default: 10, max: 50). Groups ordered by step 1 users descending.

Breakdown use case — A/B experiments: funnel my-site --steps "page_view,signup" --breakdown variant shows which experiment variant converts better through the funnel.

API-only: per-step filters — each step can have a filters array with { property, op, value } (ops: eq, neq, contains). Example: filter step 1 to path=/pricing to see conversions from the pricing page specifically.

Example output:

page_view → signup → purchase
  500 users → 80 (16%) → 12 (15%) — 2.4% overall
  Biggest drop-off: page_view → signup (84%). Focus on signup CTA visibility.
  Avg time to signup: 4.2 hours. Avg time to purchase: 2.1 days.

/retention → Are users coming back?

CLI: retention my-site --period week --cohorts 8. API: GET /retention?project=X&period=week&cohorts=8.

By default uses session-based retention — a user is "retained" if they have any return visit (session) in a subsequent period. Pass --event to switch to event-based retention.

API returns cohorts: [{ date, users, retained: [...], rates: [...] }], average_rates: [...], and users_analyzed.

How to interpret:

• Each cohort row = users who first appeared in that period
• rates[0] is always 1.0 (100% — the cohort itself)
• rates[1] = % who came back the next period — this is the critical number
• Declining rates across offsets is normal; the slope matters more than absolutes
• average_rates is weighted by cohort size — larger cohorts count more
• Compare recent cohorts vs older ones: improving rates = product is getting stickier

Options:

• --period <P> — day, week, month (default: week)
• --cohorts <N> — number of cohort periods, 1-30 (default: 8)
• --event <name> — first-seen event filter (e.g. signup). Switches to event-based retention
• --returning-event <name> — what counts as "returned" (defaults to same as --event)

Event-based retention: Set --event signup --returning-event purchase to answer "of users who signed up, what % made a purchase in subsequent weeks?"

Example output:

Cohort W0 (2026-01-27): 142 users → W1: 45% → W2: 39% → W3: 32%
Cohort W0 (2026-02-03): 128 users → W1: 42% → W2: 36%
Weighted avg: W1 = 44%, W2 = 37%, W3 = 32%
Week-1 retention of 44% is strong — nearly half of new users return.
Slight decline in recent cohorts — investigate onboarding changes.

Weekly summary recipe (3 parallel calls)

Call insights, breakdown --property path --event page_view, and pages --type entry in parallel, then synthesize into one response:

Weekly Report — my-site (Feb 8–15 vs Feb 1–8)
Events: 1,200 (+22% ↑)  Users: 450 (+18% ↑)  Bounce: 42% (improved from 48%)
Top pages: /home (523), /pricing (187), /docs (94)
Best landing: /pricing — 14% bounce, 62s avg. Worst: /blog — 52% bounce.
Trend: Growing.

Multi-project overview

For a quick real-time check, use live — it shows all projects in one view with active visitors, sessions, and events/min.

For a historical summary, call projects to list all projects, then call insights --period 7d for each. Present one line per project:

my-site         142 views (+23% ↑)  12 signups   healthy
side-project     38 views (-8% ↓)    0 signups   quiet
api-docs          0 views (—)        —            ⚠ inactive since Feb 1

Use arrows: ↑ up, ↓ down, — flat. Flag anything that needs attention.

Anomaly detection

Proactively flag — don't wait to be asked:

• Spike: any metric >2× its previous period → "unusual surge, check referrers"
• Drop: any metric <50% of previous → "significant decline, worth investigating"
• Dead project: zero page_view events → "⚠ no traffic detected"
• Errors: any error events in the window → surface the message property

Visualizing results

When reporting to messaging platforms (Slack, Discord, Telegram), raw text tables break. Use companion skills:

• table-image-generator — render stats as clean table images
• chart-image — generate line, bar, area, or pie charts from analytics data

Growth Playbook — How to grow, not just track

Tracking is step one. Growth comes from a repeatable system: clear messaging → focused distribution → obsessive tracking → rapid experimentation → learning. Here's how to apply each principle using Agent Analytics.

Principle 1: Promise clarity

The #1 conversion lever is messaging. If someone lands and has to think hard to understand the value, they're gone.

What your agent should do:

• Set up an A/B experiment on the hero headline immediately: experiments create PROJECT --name hero_headline --variants control,b,c --goal cta_click
• Test 2-3 headline variations that frame the same value differently
• Use declarative HTML: data-aa-experiment="hero_headline" data-aa-variant-b="New headline"
• Check results after ~500 visitors per variant: experiments get EXP_ID
• Ship the winner, start testing the subtitle or CTA next

Rule: Spend more time testing messaging than adding features. Even the best product won't convert if the value isn't obvious in seconds.

Principle 2: Track what drives decisions, not everything

Don't be Mixpanel. Track only what answers: "Is this project alive and growing, and what should I do next?"

The essential events (pick 3-5):

Agent workflow for tracking setup:

1. Look at the site — identify the 2-3 most important user actions
2. Add tracking on those specific actions (not everything)
3. Verify with events PROJECT that data flows
4. Set up a weekly check: insights PROJECT --period 7d

Anti-pattern: Don't track scroll depth, mouse hovers, every link click, or form field interactions. Noise kills signal.

Principle 3: Find the activation moment

Conversion doesn't happen at checkout. It happens when the user realizes the product solves their problem — the "aha moment."

How to find it:

1. Track key feature interactions: feature_used with specific feature names
2. Use breakdown --property feature --event feature_used to see which features correlate with retention
3. Check sessions-dist — if most sessions are 0s bounces, the landing page is the problem. If sessions are long but signups are low, the activation path is the problem
4. Use pages --type entry — compare bounce rates across landing pages to find which first impression works

What to optimize:

• Time to first value — how fast does the user get a result?
• Onboarding friction — where do users drop off?
• Feature discovery — are users finding the thing that makes them stay?

Principle 4: One channel, iterate relentlessly

Don't try to be everywhere. Pick one acquisition channel and go deep.

How Agent Analytics supports this:

• breakdown --property referrer --event page_view → see where traffic actually comes from
• breakdown --property utm_source → track campaign sources
• insights --period 7d → week-over-week: is the channel growing?
• Create landing page variants per channel (e.g., /reddit/, /hn/) and compare with pages --type entry

Agent workflow for channel optimization:

1. Check referrer breakdown weekly
2. Identify the top-performing channel (highest traffic + lowest bounce)
3. Double down: create content, run experiments on that channel's landing page
4. Ignore channels that aren't working — focus beats breadth

Principle 5: The autonomous growth loop

This is what makes Agent Analytics different from traditional analytics. Your agent can run the full cycle:

Track → Analyze → Experiment → Ship winner → Repeat

The loop in practice:

1. Track: Agent sets up tracking on CTAs and key actions
2. Analyze: Weekly insights + breakdown + pages calls → synthesize into a report
3. Hypothesize: "Hero headline has 87% bounce — test a clearer value prop"
4. Experiment: experiments create PROJECT --name hero_v2 --variants control,b --goal cta_click
5. Monitor: Check experiments get EXP_ID after sufficient traffic
6. Ship: experiments complete EXP_ID --winner b → deploy the winner
7. Repeat: Start the next experiment on the next weakest element

What to test (in order of impact):

1. Hero headline — biggest impact on bounce rate
2. CTA button text — directly affects conversion
3. Social proof / trust signals — affects signup confidence
4. Pricing presentation — affects revenue
5. Onboarding flow — affects activation

Cadence: One experiment at a time. ~1-2 weeks per test depending on traffic. Don't stack experiments unless traffic is very high (>1000 visitors/day).

Proactive growth monitoring

Don't wait for the user to ask. If your agent has scheduled checks, proactively flag:

• Dead project: 0 events in 7 days → "⚠ PROJECT has no traffic — is it still deployed?"
• Conversion drop: cta_click rate dropped >20% week-over-week → "Conversion declined — worth investigating"
• Experiment ready: An experiment has >100 exposures per variant → "Experiment X has enough data — check results"
• Experiment winner: Significance >95% → "Experiment X: Variant B wins with 3.8% vs 2.1%. Ship it?"
• Traffic spike: >2× normal → "Unusual traffic surge on PROJECT — check referrers for the source"

What this skill does NOT do

• No GUI dashboards — your agent IS the dashboard (or use live for a real-time TUI)
• No user management or billing
• Funnels and retention are ad-hoc queries (no saved/scheduled reports)
• No PII stored — IP addresses are not logged or retained. Privacy-first by design

Examples

Track custom events via window.aa?.track() (the ?. ensures no error if tracker hasn't loaded):

window.aa?.track('cta_click', {id: 'hero_get_started'});
window.aa?.track('signup', {method: 'github'});
window.aa?.track('feature_used', {feature: 'create_project'});
window.aa?.track('checkout', {plan: 'pro'});