Open-source AI agent orchestration platform for incident response and automation
Web infrastructure for AI agents
Stage
Not given
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Founded
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Based in
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US
Pricing model
Open source
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Pricing
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Free: 1,000 credits/month, no card; Builder: $49/month for 10,000 credits; Pro: $199/month for 50,000 credits; Scale: $799/month for 250,000 credits. Enterprise with custom terms available. Credits cost approximately $0.0032-$0.0049 per page, $3.20-$4.90 per 1,000 pages, or about $0.19-$0.29/hour of
Key features
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
Scrape: Convert any web page into clean markdown or structured JSON
Crawl: Fetch every page on a site from a single link and receive results as they are ready
Search: Query the web with top results already read and parsed
Browsers: Real browser instances in the cloud controlled via Playwright, Puppeteer or SDK
Agents: Autonomous task execution in a real browser with plain English descriptions
Session recordings: Every browser session is recorded for debugging and verification
Monitor: Watch pages on a schedule and receive notifications when content changes
Saved logins: Store credentials securely for agent runs requiring authentication
MCP integration: Connect to Claude Code, Codex or Cursor with one command
Real browser handling: Sites treat agents as humans so pages load and sessions persist
What makes it different
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
Outcrawl