AI Agent Control Plane: Observability & Governance
Open-source AI agent orchestration platform for incident response and automation
Stage
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Founded
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Based in
IN
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Pricing model
Freemium
Open source
Pricing
Free plan: $0/month with 50K events/month and 7-day retention. Team plan: $299/month with 2M events/month and 90-day retention. Business plan: $799/month with 10M events/month and 1-year retention. Enterprise plan: Custom pricing with volume pricing, custom retention, and unlimited policies.
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Key features
Unified Agent Registry across LangChain, CrewAI, and OpenAI Agents SDK
End-to-End OpenTelemetry-native tracing with full execution visibility
Accurate cost tracking across 2,500+ models independent of trace sampling
PII and sensitive data detection with optional redaction and hard blocks
Prompt registry with versioning, comparison, and production label protection
Datasets, scorers, and experiment framework with evidence-attached promotion
Framework-agnostic design supporting dynamic agent identity and multi-agent systems
Real-time health scores and dashboard for agent performance monitoring
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
Only platform that enforces policies mid-execution instead of alerting after the fact
Zero runtime overhead guardrail detection using passive span processor inspection
Accurate cost totals at any sampling rate through OTEL metrics per LLM call
Framework-agnostic and portable through OpenTelemetry-native SDK design
Enterprise-grade security with SOC 2 Type II and RBAC built in
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
Traccia
Ryph