AI-ready product analytics CLI

Make product analytics actionable for AI agents.

Connect PostHog. Render session recordings. Analyze with Gemini vision. Output structured markdown your agents can act on.

View on GitHub
Requiresgitbungcloudffmpeg
Terminal — vesai
$ curl -fsSL https://ves.ai/install | bash
Installing VES AI...
Installed vesai to ~/.vesai/bin/vesai
$ vesai quickstart
Connected to PostHog project: My App
GCS bucket: gs://vesai-renders ready
Quickstart complete.
$ vesai replays session ph_abc123
Fetching session ph_abc123...
Rendering 47 events via Playwright...
Uploading 12 frames to GCS...
Analyzing with Gemini Vision...
{ "session_id": "ph_abc123",
"score": 72,
"key_findings": [
"User rage-clicked checkout button 3x",
"Payment form validation blocked submit"
] }

How It Works

From raw PostHog data to structured, agent-ready workspace artifacts in four steps.

Capture

Pull replays & events from PostHog

Render

Replay sessions in Playwright

Analyze

Extract insights with Gemini Vision

Act

Output markdown agents can use

See It in Action

Real commands, real output. Every data command returns structured JSON by default.

Terminal — vesai
$ vesai replays session ph_abc123
Fetching session ph_abc123...
Rendering 47 events via Playwright...
Uploading 12 frames to GCS...
Analyzing with Gemini Vision...
{
"session_id": "ph_abc123",
"duration_seconds": 184,
"score": 72,
"severity": "medium",
"key_findings": [
"User rage-clicked checkout button 3x",
"Payment form validation blocked submit",
"Session ended without conversion"
],
"workspace_artifact": "~/.vesai/workspace/sessions/ph_abc123.md"
}

Built for AI Agents

Same commands, same outputs whether you're a human or a coding agent.

Machine-Readable by Default

Every data command emits JSON. Pipe output directly into your agent workflows, CI pipelines, or dashboards.

Durable Workspace Artifacts

Session, user, and query analyses persist as git-friendly markdown in ~/.vesai/workspace/ for long-lived agent context.

Local-First, Self-Hosted

Your PostHog keys, your GCP project, your machine. No data leaves your infrastructure.

Ships with a SKILL.md

A comprehensive skill file teaches Claude Code, Codex, and other coding agents how to use every vesai command, workflow, and HogQL pattern out of the box.

Integrations

PostHog
PostHog

Session recordings & product analytics

Google Cloud
Google Cloud

Vertex AI, Gemini, Cloud Storage

Playwright
Playwright

Browser-based replay rendering

ffmpeg

Video & frame processing

Getting Started

Prerequisites

  • gitVersion control
  • bunJavaScript runtime
  • gcloudGoogle Cloud SDK, authenticated
  • ffmpegVideo processing

Authenticate gcloud:

gcloud auth login
gcloud auth application-default login

Three Steps

  1. 1Install VES AI
    curl -fsSL https://ves.ai/install | bash
  2. 2Run quickstart
    vesai quickstart
  3. 3Analyze your first session
    vesai replays session <session_id>