| What it does | Open-source AI agent orchestration platform for incident response and automation
| An engine for long-running agents that rebuilds its prompt under a cap and runs on your budget
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| Stage | Not given
| MVP
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| Founded | Not given
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| Based in | Not given
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| Pricing model | Open source
| Freemium
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| Pricing | Not given
| Prototype: free during beta, then from December 16, 2026 it costs $1 per 1,000 agent calls after 1,000 monthly included calls. Production: $20 a month with 20,000 agent calls included, then $1 per 1,000 calls after that. Top-ups: $1 per 1,000 calls, from $10 to $500 at a time, never expiring.
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| 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
| - Prompt rebuilt from journal under token cap at each step to keep it bounded as history grows
- Spending limits checked before each model call at token prices you declare
- Resume after crash without repeating finished actions
- Tool calls returned as actions for your code to carry out
- Proof of what ran: check prompts, re-run engine, download hash-chained journal
- Integrations with Vercel AI SDK, OpenAI Agents SDK, LangGraph, Mastra, Pydantic AI
- MCP server for Claude Code, Claude Projects and Codex
- Works with any model via bring-your-own-key
- TypeScript and Python clients for HTTP API access
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| 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
| - Bounded prompt under a fixed cap while history grows, so costs per call stop climbing on long sessions
- Every run recorded with cryptographic proof: rebuilable prompts, hash-chained journal, re-runnable engine
- Resumes from crash without repeating finished actions using stable idempotency keys
- Budget enforcement before each model call, not reactive overage billing
- No model key storage or logging; your provider bills you directly
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| Free plan | Not given
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| Open source | Not given
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| Platforms | Not given
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| Public API | Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 0 | 0 |
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| Citation Flow (Majestic) | 0 | 9 |
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| Referring domains (Majestic) | 0 | 4 |
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Ryph
Argentic