Number 1: 
Ora
Make your site work for agents
- AI testing & review
- United States
- DR 60 +1 (up) since last check
AI testing & review · United States
Oqoqo
The easiest way to build evals and custom benchmarks for real-world agentic tasks
- Community votes
- 0
- DR by Ahrefs
- 13 no change since last check
Ahrefs Domain Rating benchmarked 1 Nov 2026, rebuilt 1 Nov 2026.
Run eval experiments at scale in realistic environments on fully managed cloud infrastructure to build private benchmarks, measure agent usability, and compare
What it is
Oqoqo enables teams to build evals and custom benchmarks for real-world agentic tasks by running experiments at scale in realistic, sandboxed environments. Each task runs on its own machine with full project state, context, credentials, and tools, capturing every step agents take—from tool calls and commands to errors and where they got stuck.
The platform supports evaluating any agent, model, and treatment (toolsets like skills, MCP servers, CLIs, and SDKs) across the same tasks simultaneously. Users see pass or fail results with written reasons, full trajectories, token counts, and friction points. Teams can build private benchmarks from real-world workflows, compare agents and model combinations, integrate with CI/CD pipelines, and iterate quickly by fixing what failed and re-running the experiment.
Oqoqo handles durable experiment workflows and cloud infrastructure while users bring their own model API keys and subscriptions. The platform is accessible via web app, CLI, and MCP.
What it is built with
- Compare treatments (toolsets including skills, MCP servers, CLIs, SDKs)
- Build and version private benchmarks in plain language
Pricing
Pricing model: Freemium
Free: 100 plan runs per month, $0/month. Pro: 300 plan runs per month, $20/month ($0.067 per run). Ultra: 1,000 plan runs per month, $60/month ($0.06 per run). Top-ups available: 25 runs for $5, 100 runs for $15, 300 runs for $40, 1,000 runs for $120.
Oqoqo metrics
| Metric | Value |
|---|---|
| Domain Rating | 13 no change since last check |
| Trust Flow | 7 no change since last check |
| Citation Flow | 28 no change since last check |
| Referring domains | 130 no change since last check |
| External backlinks | 1,652 no change since last check |
Ahrefs Domain Rating benchmarked 1 Nov 2026, rebuilt 1 Nov 2026. Main build topic: Computers/Internet/On the Web (7). Majestic benchmarked 1 Nov 2026, rebuilt 1 Nov 2026. Link data built by Majestic. Trust Flow, Citation Flow and Topical Trust Flow are trademarks of Majestic-12 Ltd.
Oqoqo domain build sheet
Open the build sheet
| Domain age | 1 year |
|---|---|
| Registered | |
| Expires | |
| Registrar | GoDaddy.com, LLC |
| DNSSEC | Not signed |
Oqoqo after-launch build log
Since Oqoqo launched on Maker Launches on 1 November 2026, these are the figures we have tracked.
- Domain Rating (Ahrefs)
- 13
- First measured 1 Nov 2026
- Referring domains (Majestic)
- 130
- First measured 1 Nov 2026
Day 0 since launch. Next receipt: day 30, 1 Dec 2026. We show what changed, not what caused it: many things affect these numbers. Nothing here is estimated.
Compare and explore
Questions from the bench
- What is Oqoqo for?
- Oqoqo is for building evals and private benchmarks for real-world agentic tasks. It lets you see whether any agent can use any product, compare agents and models on the same tasks, and find where they fail.
- How is this different from trying an agent myself?
- Trying once is not an experiment. Oqoqo repeats the same tasks across agents, models, and treatments, then reports pass rates, lift, tokens, and frictions.
- What can I evaluate?
- You can evaluate skills, MCP servers, CLIs, SDKs, APIs, docs, files, and the workflows that use them.
- Can I build private benchmarks?
- Yes. You can write your own tasks and rubrics in plain language. They stay versioned with each experiment, so later runs stay comparable.
- Which agents can I run?
- Oqoqo supports Claude Code, Codex, Cursor, GitHub Copilot, OpenCode, Grok Build, Qwen Code, OpenClaw, Pi, Hermes, and Antigravity today, with the ability to choose model and effort per agent where supported.
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