OpenRig vs WAI Quantum
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
OpenRig compared with WAI Quantum
| Detail | 
OpenRig | 
WAI Quantum |
|---|
| What it does | A persistent team of coding agents
| Run a quantum program. Get an answer you can check.
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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
| US
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| Pricing model | Open source
| Free
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| Pricing | Not given
| Not given
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| Key features | - Start teams with one command and manage agents across multiple machines
- Mix Claude Code, Codex and Pi agents in one team
- Agents delegate and coordinate work with named task assignments and queue ownership
- Agents message each other directly and read each other's screens
- Restore agent conversations after restart or reboot from snapshots
- Choose different models and reasoning effort per agent
- Bring custom harnesses and integrate with MCP servers and Claude Desktop
- Start Docker Compose services alongside agent teams
- Control message filtering with typing guards and chat broadcasts
| - Multiple simulation methods including state vector, stabilizer tableau, matrix product state, and tensor network
- Automatic method selection based on circuit requirements
- Measurement counts, expectation values, and amplitude extraction
- Noise simulation with depolarizing, amplitude damping, and dephasing channels
- Cryptographically signed receipts for result verification
- Model Context Protocol integration for agent access
- Browser-based execution and service-based execution
- Desktop app and command-line interface availability
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| What makes it different | - One conversation interface with a lead agent coordinating specialists behind the scenes
- Mix multiple coding models and harnesses in one managed team
- Run entirely self-hosted on your own machine with no external coordination
- Open source with Apache 2.0 license and extensible harness contracts
| - No account and no API key required to use the service
- Automatic explanation of which simulation method was chosen and why
- Signed receipts allow verification of results by anyone
- Integrated with Model Context Protocol for AI agent access
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 28 ● no change since last check | 53 ● no change since last check |
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| Trust Flow (Majestic) | 6 | 0 |
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| Citation Flow (Majestic) | 36 | 15 |
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| Referring domains (Majestic) | 191 | 3 |
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4 details filled in for both companies.
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