withHuman vs MiroFish
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
withHuman compared with MiroFish
| Detail | 
withHuman | M
MiroFish |
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| What it does | The approval layer for AI agents
| A simple and universal swarm intelligence engine
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| Stage | Not given
| Not given
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| Founded | Not given
| Not given
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| Based in | US
| Not given
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| Pricing model | Freemium
| Freemium
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| Pricing | Open Source: free and self-hosted. Free: $0/month hosted plan with up to 5 members. Scale: $20 per user per month. Enterprise: Custom annual contract.
| Starter: $7.50/month (monthly) or $9/month (annual); Pro: $15.83/month (monthly) or $19/month (annual); Premium: $40.83/month (monthly) or $49/month (annual). Annual plans save 17%.
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| Key features | - Tool call interception for all agent frameworks
- Automatic approval rules to prevent fatigue
- AI judge for complex rule evaluation
- Works with LangGraph, CrewAI, Mastra, Claude Agent SDK and more
- Open Agent Approval Protocol for framework independence
- Self-hosted or cloud-hosted deployment options
- Custom permission policies and organization controls
| - Upload PDF, Markdown, and text documents to seed simulations
- Convert source material into ontology and graph structure
- Run parallel simulations with up to 1 million agents per run
- Generate prediction reports with key risks and confidence signals
- Deep interaction through ReportAgent or direct character interviews
- Dual-surface simulation across Twitter and Reddit style channels
- Follow-up questions in the same workspace for continued analysis
- Build executable scenarios for public opinion, market, policy, and narrative use cases
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| What makes it different | - Framework-agnostic: works with any AI agent through the open Agent Approval Protocol
- No vendor lock-in: open source spec and SDKs let you swap providers without rewriting agents
- Comprehensive audit trail for accountability and compliance
- Meets reviewers where they work with multi-platform notifications
- Policy-based routing to eliminate approval bottlenecks
| - Inspectable AI forecasts treated as exploratory hypotheses before operational decisions
- Review simulations before upload with the Forecast Explorer gallery
- Graph-native simulation that exposes actor incentives, narrative spread, and reaction chains
- Templates and guides for recurring scenario types like crises and product launches
- Keeps judgment with humans on scenario boundaries and high-stakes operating calls
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 30 ▼ −1 (down) since last check | 56 ● no change since last check |
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| Trust Flow (Majestic) | 6 | 5 |
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| Citation Flow (Majestic) | 28 | 30 |
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| Referring domains (Majestic) | 48 | 160 |
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5 details filled in for both companies.
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