Open-source AI agent orchestration platform for incident response and automation
Stage
Not given
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Founded
2023
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Based in
US
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Pricing model
Freemium
Open source
Pricing
Free plan: $0/month with 2,000 credits, 3 active agents, 2,000 tasks/month, 1 user. Startup: $12/month with 10,000 credits, up to 15 active agents, 5 users. Growth: $34/month with 10,000 credits, up to 50 active agents, 25 users. Teams: $56/month with 10,000 credits, up to 100 active agents, unlimit
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Key features
Visitor intelligence identification and ICP matching
Outbound prospecting with contact enrichment
Automated pipeline enrichment from multiple sources
Sales research and call brief preparation
Campaign personalization with company context
Inbound lead scoring and qualification
Three operating modes: autopilot, human-in-the-loop, and chat
500+ app integrations with one-click connection
Custom workflow builder with logic and flow controls
Task history, analytics and dashboard reporting
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
Deploy in minutes without engineering setup or technical requirements
Enterprise-ready with data privacy and no training on customer data
Eliminates manual prospecting and research through automated agents
Real-time CRM synchronization across all connected tools
Personalized outreach at scale for every prospect
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
Konnectify
Ryph