Ryph vs RELAI
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Ryph compared with RELAI
| Detail | 
Ryph | 
RELAI |
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| What it does | Open-source AI agent orchestration platform for incident response and automation
| The continual learning engine for AI agents
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| Stage | Not given
| Not given
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| Founded | Not given
| Not given
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| Based in | Not given
| Not given
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| Pricing model | Open source
| Freemium
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| Pricing | Not given
| RELAI Free: $0. RELAI Enterprise: Custom pricing.
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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
| - Capture signals from code, requirements, and production traces
- Build and maintain EvalsWiki as versioned behavioral knowledge
- Find missing eval coverage and unclear expectations
- Generate complete evals with scenarios, environments, and evaluators
- Optimize harness across prompts, tools, workflows, and code
- Multi-objective optimization for quality, cost, latency, and KPIs
- Model selection, migration, and routing with evaluation
- Regression-controlled improvement with evaluation evidence
- Versioned storage in repository with pull requests
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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
| - Turns failures into replayable learning environments
- Prevents regressions by keeping previous evals in the loop
- Integrates with existing observability stacks and traces
- Preserves business intelligence versioned in repository
- Adapts harness to model changes without redefining use cases
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| Free plan | Not given
| Not given
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| Open source | Not given
| Not given
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| Platforms | Not given
| Not given
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| Public API | Not given
| 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 | 23 ● no change since last check |
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| Trust Flow (Majestic) | 0 | 13 |
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| Citation Flow (Majestic) | 0 | 25 |
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| Referring domains (Majestic) | 0 | 79 |
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4 details filled in for both companies.
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