Create projects and track events
Spin up a project, install the tracker, and start collecting page views and custom events.
Claude Code
Codex
OpenClaw
AgentAnalytics lets your AI agent track traffic, find drop-offs, compare projects, and run experiments so you stop shipping blind.
What changed across my projects this week?
One answer combines cross-project changes, bottlenecks, experiment results, and the likely explanation, after your agent already did the obvious follow-up analysis.
Create projects, add tracking, and run experiments via CLI, MCP, or API. The AgentAnalytics skill packages the workflows Claude Code, Codex, OpenClaw, and similar tools use to inspect performance, form hypotheses, and review experiments in chat.
What your AI agent can do with AgentAnalytics:
Spin up a project, install the tracker, and start collecting page views and custom events.
Create variants, measure lift, and check winners from the same analytics surface.
The AgentAnalytics skill packages setup, inspection, and experiment workflows for Claude Code, Codex, OpenClaw, and similar tools.
Dashboards were built for humans. Your AI agent can inspect the data, form hypotheses, and manage experiments in chat.
This is the work traditional analytics leaves on you. AgentAnalytics gives your AI agent a measurement layer it can query directly, so it can review performance, spot what changed, and tell you what deserves attention next.
Your agent checks every project, ranks the changes that matter, and gives you a founder-readable brief instead of a metric dump.
Your agent can tell whether HN, Reddit, search, docs, or a launch post drove qualified traffic instead of vanity visits.
Your agent inspects the path from visit to signup or checkout, then tells you exactly where the drop-off happens.
Your agent can launch, read, and retire A/B tests from code or chat, then recommend the next test instead of stopping at the result.
These are not old analytics verticals with an AI label slapped on top. They are the product shapes where builders already rely on coding agents to ship faster and need the same agents to measure what happened.
Track onboarding, activation, pricing, and retention across the product loops where users decide whether your assistant becomes part of their workflow.
Measure the flow from docs visit to install to first request, then let your agent connect launches and devrel spikes to actual product usage.
See which articles, docs pages, and community posts attract people who keep reading, click the CTA, and eventually sign up.
Inspect the steps from landing to demo request or checkout, compare experiments, and find which flows create repeat usage instead of one-time visits.
These are the questions your AI agent can answer and the next moves it can recommend.
Ask "How are my projects doing?" and your AI agent can pull stats, anomalies, top pages, and what deserves attention this week.
Ask where people drop off on the path to signup. Your AI agent points to the exact step losing users.
Create an experiment with declarative HTML variants. AgentAnalytics tracks exposures and results while your AI agent reads lift and significance.
When there is enough data, your AI agent can summarize the winner and point you to the next page, funnel step, or experiment worth attention.
Self-host for full control, start free on our cloud, or upgrade when you want the full hosted analytics surface.
Best for teams that want full control over infra, deployment, and data.
See if agentic analytics across multiple projects is something you'd use.
When you want the full operating loop.
Pick the setup that fits now, then start free on our cloud or self-host the open-source version on your own infra.