The agent-native Growth OS that turns your AI agent into a disciplined growth hacker
Stage
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Founded
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Based in
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US
Pricing model
Freemium
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Pricing
Free trial: $100 in credits, no credit card required. Plus: $30/user/month with $30 usage included. Pro: $100/user/month or $200/user/month with corresponding usage included, each with pay-as-you-go beyond included usage.
Free: 1 member, 1 project, unlimited free contributors and viewers. Solo: $19.90/month for 1 member with unlimited projects. Team: $39/month for up to 3 members, then $10/month per extra member, with unlimited projects and private projects.
Key features
Browser automation and computer control
Agentic apps built automatically without coding
Integration with frontier AI models from multiple providers
Works across team communication channels like Slack, Email, and Telegram
Media generation with 300+ image, video and audio models
Autonomous 24/7 cloud execution
Pre-built agent templates for sales, operations, marketing and support
Sandboxed agent execution with permission controls
Comprehensive audit logging of all agent actions
Open source and self-hostable under MIT license
Complete experiment backlog with candidate ideas
Status tracking from idea to outcome
Shared experiment learnings across team and agents
ICE, PIE, RICE scoring frameworks with voting
Enforced experiment stages with mandatory results and learnings
MCP server for AI agent read and write access
Skills that teach agents the methodology
Plugin for Claude Code and Codex
Web app for team review and collaboration
OAuth security with granular permissions
What makes it different
No coding required to create and deploy agents
Shared team workspace for managing multiple AI agents together
Built-in safety and security with sandboxed execution and full audit trails
Agents run autonomously 24/7 in the cloud with scheduled and event-based triggers
Open source with option to self-host on your own infrastructure
Agents cannot bypass the method: tests cannot close without measured results and written learnings
Enforced workflow prevents confident guesses and invented wins
Shared memory ensures experiments are never repeated and evidence is preserved
Agents read and write to the same record as teams under identical rules
Gamut
Hypotize