Cloud agent sandbox with persistent filesystem and on-demand compute
Open-source AI agent orchestration platform for incident response and automation
Stage
MVP
Not given
Founded
Not given
Not given
Based in
US
Not given
Pricing model
Paid
Open source
Pricing
Compute: $0.015 per GiB-hour (approximately $0.000004167 per GiB-second, billed by the second). Storage: $0.02 per GiB-month for retained incremental RootFS data and memory checkpoints. Network ingress and egress currently free. Object-store GET and PUT requests free. New users receive $5 free credi
Not given
Key features
Automatic filesystem growth as storage fills
Pause compute without losing work or paying compute charges
Resume to same environment on demand
Access files while paused without waking compute
Public HTTP services with auto-resume capability
Command-line interface (s0 CLI) for agent-native workflows
Incremental checkpoints to avoid full filesystem copies
Scoped API keys and team-based access controls
On-demand file reads without downloading entire filesystem
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
Persistent filesystem across pause/resume without duplicating data
Pay only for compute time used; paused sandboxes have zero compute charges
Automatic filesystem growth so agents can install and persist unlimited tools
Same API for both human users and AI agents
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
Ryph