Cloud computer for every agent, with your tools and team.
Open-source AI agent orchestration platform for incident response and automation
Stage
Not given
Not given
Founded
Not given
Not given
Based in
NO
Not given
Pricing model
Freemium
Open source
Pricing
Free: $10 monthly usage credit. Pro: $20/month for $100 monthly usage credit. Max: $100/month for $500 monthly usage credit. Enterprise: custom volume pricing. All plans include 5× usage credit for model calls at provider's API price with no markup. Pay-as-you-go top-ups available.
Not given
Key features
Each agent gets its own dedicated computer in the cloud
Agents keep working after you close your laptop
Run many tasks in parallel, each on its own computer
Encrypted vault for credentials and secrets
Connect tools once, all agents can use them
Support for any AI model including your own subscriptions
Multiplayer chats teammates can continue
Scheduled recurring work on a schedule
Approval controls for risky actions
Start tasks from Slack, web app or phone
Incident response with agent investigation and pull request generation
Automated workflows triggered on schedules or webhooks
Agent summoning from GitHub, GitLab and Radicle with /ryph command
Semantic memory system for shared and persistent agent learning
Credentials kept behind gateways with no access in sandbox
Customized environments per repository with cached dependencies
Browser use for agent interactions with live watching
Artifacts and pages for reports and overviews
Deployment options from laptop to Kubernetes
Tool proxy with allowlisted MCP server integrations
What makes it different
Open source under Apache 2.0, self-hostable or run on managed cloud
Same harness works for team agents, software development automation, and embedding in your product
No markup on model API costs; credits give 5× value
Agents run real terminal commands, tests and git operations
Teams share one workspace with shared integrations and role-based access
Agents work without holding real credentials or keys
Changes ship as patches that require review before merge
Runs on your own infrastructure from laptop to cloud
Semantic memory learns across runs with deduplication and contradiction handling
Built-in safety assuming agents can be manipulated or exploited
Opengeni
Ryph