Run and fine-tune models. Deploy custom models. All with one line of code.
Open-source AI agent orchestration platform for incident response and automation
Stage
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Founded
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Based in
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Pricing model
Paid
Open source
Pricing
Most models billed by time based on hardware used (CPU $0.000100/sec, T4 GPU $0.000225/sec, L40S GPU $0.000975/sec). Some models billed by input/output tokens or generated items. Private custom models billed for all uptime; fast-booting fine-tunes billed only for active processing.
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Key features
Run thousands of community-contributed open-source models
Fine-tune models with custom data to create specialized versions
Deploy custom models using Cog, an open-source packaging tool
Automatic scaling based on demand
Pay only for compute time used
Image generation from text
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
One-line code interface for running models without ML expertise
Automatic infrastructure scaling without manual management
Open-source community with thousands of production-ready models
Fine-tuning capability to customize models for specific tasks
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