Agent layer for SaaS with grounded answers, sandboxed tools, and per-customer isolation
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
Free
Open source
Pricing
Free during launch period. Customers pay their LLM provider directly for model usage at provider rates; Oberik does not markup or resell tokens. No credit card required, no usage meters tracked by Oberik itself.
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Key features
Document-grounded answers with page and quote citations
Linux sandbox environment for agent execution
Per-customer cost tracking and billing
Memory and wiki persistence across sessions
Scheduled and recurring work with durability
Capability-scoped JWT tokens enforced per-request
Support for custom tools and MCP servers
Multi-model support with bring-your-own-keys
Server-side prompt injection and PII screening
SSH dashboard interface for programmatic configuration
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
Hard isolation between customers with no shared state or tenant leakage
Citations with document, page and quote provided automatically with every answer
Only two API calls needed to integrate grounded AI into any SaaS product
Customers never charged by Oberik; direct billing from their own LLM provider keys
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
Oberik
Ryph