Open-source AI agent orchestration platform for incident response and automation
Stage
Not given
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Founded
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Based in
US
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Pricing model
Freemium
Open source
Pricing
Free for one developer with up to 25 source-built services, 50,000 tool calls a month, then $0.20 per thousand. Team plan is $25 per developer per month with 100,000 tool calls per developer a month, pooled, then $0.20 per thousand. Enterprise from $1,500 per month.
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Key features
Source-built servers with TypeScript or Python projects
Remote MCP, OpenAPI, npm package, and container image support
Per-tool and per-argument access control policies
Tool definition review and approval workflow
Content scanning with literal and regex detectors
Activity tracking and audit trails
OIDC and SAML 2.0 single sign-on
SCIM 2.0 provisioning
Workspace-specific data encryption
Custom domain publishing with DNS and Cloudflare certification
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 endpoint replaces every client config, eliminating need for each client to store every server and secret
Coding agent builds the server automatically with no adapter needed
Credentials held once by Rayrun instead of distributed across clients
Built-in security review with published controls and audit trails for compliance teams
Support for multiple service types without writing adapters
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
Rayrun
Ryph