Crab

4.6 (324 reviews)

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CRAB helps you test AI agents across various digital environments, like computers and phones. It lets you build agents, run them in different settings, and automatically creates complex tasks. A detailed evaluation system then shows how well they perform and where they can get better.

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Overview Features Screenshots & Demo Use Cases Reviews FAQ Pricing Integrations

What is Crab?

Who It's For

CRAB helps researchers and developers test AI agents. If you build agents that interact with different digital systems, like computers or phones, this tool lets you evaluate their performance and find improvements.

What You Get

You get a complete system to evaluate AI agents. It supports many environments, so agents can work across various interfaces. CRAB includes a smart evaluation tool for detailed performance insights, not just pass/fail. It also automatically creates complex tasks, saving time.

How It Works

CRAB lets you define agent actions and environments with Python. Agents run in diverse settings, from Linux to Android. The system automatically generates realistic tasks by combining smaller steps. A "graph evaluator" tracks agent progress, checking each step for a detailed report on strengths and weaknesses.

Features & Capabilities

⚙️ Core Benchmark Framework

Cross-Environment Support

Allows agents to operate and be evaluated across multiple distinct environments, including Ubuntu and Android.

Graph-based Task Generation

Automates the creation of complex, dynamic tasks by composing multiple sub-tasks into realistic scenarios.

End-to-End Framework

Provides a complete and user-friendly system for building agents, managing environments, and conducting evaluations.

📊 Evaluation & Analysis Tools

Fine-Grained Graph Evaluation

Offers detailed performance analysis beyond binary success rates, identifying specific strengths and weaknesses of agents.

Multimodal Observation Integration

Supports vision-based observations, such as screenshots, to enable agents to perceive and interact with graphical user interfaces.

Reproducible Benchmark Configuration

Facilitates easy reproduction of experiments through a declarative programming paradigm for benchmark setup.

Screenshots & Demo

See Crab in action with screenshots and video demonstrations

Product Screenshots

Crab

AI agents tested across environments with unprecedented depth

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Real-World Use Cases

Comprehensive Benchmarking for Multimodal AI Agents

AI developers and researchers struggle with accurately evaluating the performance of complex multimodal language model agents across diverse environments. CRAB provides an end-to-end framework with cross-environment support and a novel graph evaluator, offering fine-grained analysis beyond simple success rates to pinpoint agent strengths and areas for improvement.

Industry: AI/ML Development • User Type: AI Researchers, ML Engineers, AI Product Managers

Streamlined Development and Testing of Cross-Platform AI Agents

Building AI agents that can reliably operate across different operating systems and applications (e.g., desktop and mobile) presents significant testing challenges. CRAB offers a flexible framework to build and test agents in multiple simulated environments like Ubuntu and Android, enabling developers to ensure seamless adaptation and consistent performance across diverse interfaces.

Industry: Software Development, AI Solutions • User Type: AI Developers, Software Engineers, QA Engineers

Automated Creation of Realistic AI Agent Evaluation Tasks

Manually crafting a wide array of complex and realistic tasks for evaluating AI agents is a time-consuming bottleneck in development. CRAB automates the generation of dynamic, real-world mimicking tasks by combining multiple sub-tasks using a graph-based method, drastically reducing the effort and time required for benchmark creation.

Industry: AI Research & Development • User Type: AI Researchers, Benchmark Developers, ML Engineers