The Stack v2 vs cua
Side-by-side comparison to help you choose.
| Feature | The Stack v2 | cua |
|---|---|---|
| Type | Dataset | Agent |
| UnfragileRank | 48/100 | 53/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 1 |
| Ecosystem | 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Aggregates 67 TB of source code from the Software Heritage archive with automated license classification and filtering to retain only permissively licensed content (Apache 2.0, MIT, BSD, GPL variants, etc.). Uses metadata-driven filtering pipelines to exclude proprietary and restrictive licenses, enabling legal compliance for model training without manual license auditing. Implements a Software Heritage integration layer to access the largest open-source repository snapshot available.
Unique: Largest permissively-licensed code dataset (67 TB across 600+ languages) sourced from Software Heritage archive with automated license filtering pipeline, enabling legal training of open-source models at unprecedented scale without manual auditing
vs alternatives: Larger and more legally vetted than GitHub-only datasets (CodeSearchNet, GitHub-Code) and includes non-GitHub repositories, while maintaining strict permissive licensing unlike raw GitHub dumps that require post-hoc filtering
Implements a rigorous deduplication pipeline that identifies and removes duplicate code across 600+ programming languages using content-based hashing and semantic similarity detection. Normalizes code formatting, whitespace, and comments to identify near-duplicates that would otherwise inflate dataset size and introduce training bias. Uses language-specific tokenization and AST-aware comparison for structural duplicates, not just string matching.
Unique: Language-aware deduplication across 600+ languages using content hashing and AST-based structural comparison, not just string matching, to identify near-duplicates and boilerplate code that would bias model training
vs alternatives: More sophisticated than simple hash-based deduplication used in CodeSearchNet; handles language-specific formatting variations and generated code patterns that generic string matching would miss
Applies automated PII detection pipelines to identify and redact sensitive information (email addresses, API keys, credentials, personal names, phone numbers, etc.) from source code before dataset release. Uses pattern matching, regex-based detection, and potentially ML-based classifiers to find PII in comments, strings, and code. Implements configurable redaction strategies (masking, removal, replacement with placeholders) while preserving code functionality.
Unique: Automated PII detection and redaction pipeline applied across 67 TB of code to remove credentials, emails, names, and sensitive data before public release, with configurable redaction strategies that preserve code functionality
vs alternatives: More comprehensive than manual review or simple regex patterns; applies consistent PII removal at scale across diverse code repositories, reducing privacy risks in publicly released training data
Implements a governance framework allowing repository owners to request exclusion of their code from the dataset via an opt-out mechanism (e.g., registry, email contact, automated API). Processes exclusion requests, removes matching repositories from the dataset, and maintains an exclusion list for future dataset versions. Respects developer autonomy and copyright concerns while maintaining dataset openness by default.
Unique: Opt-out governance model allowing repository owners to request exclusion from the dataset, respecting developer autonomy and copyright concerns while maintaining an open-by-default approach to dataset curation
vs alternatives: More developer-friendly than opt-in models (which would require explicit consent from millions of developers) while more respectful than no-opt-out approaches; balances openness with individual control
Covers source code across 600+ programming languages with language-specific metadata (syntax, paradigm, ecosystem, file extensions, etc.). Implements language detection and classification pipelines to identify code language, extract language-specific features, and organize data by language family. Enables language-stratified sampling and analysis, supporting diverse model training use cases from general-purpose to language-specific code models.
Unique: Comprehensive coverage of 600+ programming languages with language-specific metadata and classification, enabling stratified sampling and language-aware model training at unprecedented scale and diversity
vs alternatives: Broader language coverage than GitHub-only datasets (typically 10-20 languages) and more structured language metadata than raw code dumps; supports both general-purpose and language-specific model training
Preserves and enriches repository-level metadata including creation date, last update, star count, fork count, contributor count, license type, and language distribution. Maintains file-to-repository mappings and directory structure information, enabling context-aware model training that understands code within its repository ecosystem. Implements metadata aggregation from Software Heritage and GitHub APIs to provide rich contextual information for each code sample.
Unique: Preserves rich repository-level metadata (stars, forks, creation date, contributor count, license) alongside code content, enabling context-aware model training that understands code within its ecosystem and quality signals
vs alternatives: More comprehensive than raw code dumps; provides repository context that enables quality-aware training and downstream applications like code search, while maintaining file-to-repository mappings for structured analysis
Integrates with the Software Heritage archive, a comprehensive snapshot of open-source software repositories worldwide, to access code at scale without relying on individual repository APIs or GitHub. Implements Software Heritage API clients and data export pipelines to retrieve code content, metadata, and version history. Enables reproducible dataset snapshots by referencing specific Software Heritage revisions, supporting dataset versioning and reproducibility.
Unique: Leverages Software Heritage archive as the data source, providing comprehensive open-source code snapshot with reproducible versioning via SWHIDs, independent of GitHub or any single platform
vs alternatives: More comprehensive and platform-independent than GitHub-only datasets; enables reproducible snapshots and includes non-GitHub repositories, while avoiding API rate limits and platform dependency
Implements versioning and release management for dataset versions (v1, v2, etc.) with documented changes, improvements, and data quality enhancements between versions. Maintains version-specific documentation, changelog, and reproducibility information. Enables users to select specific dataset versions for training, ensuring reproducibility and allowing comparison of model performance across dataset versions.
Unique: Implements explicit dataset versioning (v1, v2) with documented improvements and reproducibility information, enabling users to specify exact dataset versions for training and supporting reproducible research
vs alternatives: More structured than continuously updated datasets; enables reproducibility and comparison across versions, while providing clear documentation of improvements and changes between releases
+2 more capabilities
Captures desktop screenshots and feeds them to 100+ integrated vision-language models (Claude, GPT-4V, Gemini, local models via adapters) to reason about UI state and determine appropriate next actions. Uses a unified message format (Responses API) across heterogeneous model providers, enabling the agent to understand visual context and generate structured action commands without brittle selector-based logic.
Unique: Implements a unified Responses API message format abstraction layer that normalizes outputs from 100+ heterogeneous VLM providers (native computer-use models like Claude, composed models via grounding adapters, and local model adapters), eliminating provider-specific parsing logic and enabling seamless model swapping without agent code changes.
vs alternatives: Broader model coverage and provider flexibility than Anthropic's native computer-use API alone, with explicit support for local/open-source models and a standardized message format that decouples agent logic from model implementation details.
Provisions isolated execution environments across macOS (via Lume VMs), Linux (Docker), Windows (Windows Sandbox), and host OS, with unified provider abstraction. Handles VM/container lifecycle (creation, snapshot management, cleanup), resource allocation, and OS-specific action handlers (keyboard/mouse events, clipboard, file system access) through a pluggable provider architecture that abstracts platform differences.
Unique: Implements a pluggable provider architecture with unified Computer interface that abstracts OS-specific action handlers (macOS native events via Lume, Linux X11/Wayland via Docker, Windows input simulation via Windows Sandbox API), enabling single agent code to target multiple platforms. Includes Lume VM management with snapshot/restore capabilities for deterministic testing.
vs alternatives: More comprehensive OS coverage than single-platform solutions; Lume provider offers native macOS VM support with snapshot capabilities unavailable in Docker-only alternatives, while unified provider abstraction reduces code duplication vs. platform-specific agent implementations.
cua scores higher at 53/100 vs The Stack v2 at 48/100. The Stack v2 leads on adoption, while cua is stronger on quality and ecosystem.
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Provides Lume provider for provisioning and managing macOS virtual machines with native support for snapshot creation, restoration, and cleanup. Handles VM lifecycle (boot, shutdown, resource allocation) with optimized startup times. Integrates with image registry for VM image management and caching. Supports both Apple Silicon and Intel Macs. Enables deterministic testing through snapshot-based environment reset between agent runs.
Unique: Implements Lume provider with native macOS VM management including snapshot/restore capabilities for deterministic testing, optimized startup times, and image registry integration. Supports both Apple Silicon and Intel Macs with unified provider interface.
vs alternatives: More efficient than Docker for macOS because Lume uses native virtualization (Virtualization Framework) vs. Docker's slower emulation; snapshot/restore enables faster environment reset vs. full VM recreation.
Provides command-line interface (CLI) for quick-start agent execution, configuration, and testing without writing code. Includes Gradio-based web UI for interactive agent control, real-time monitoring, and trajectory visualization. CLI supports task specification, model selection, environment configuration, and result export. Web UI enables non-technical users to run agents and view execution traces with HUD visualization.
Unique: Implements both CLI and Gradio web UI for agent execution, with CLI supporting quick-start scenarios and web UI enabling interactive control and real-time monitoring with HUD visualization. Reduces barrier to entry for non-technical users.
vs alternatives: More accessible than SDK-only frameworks because CLI and web UI enable non-developers to run agents; Gradio integration provides quick UI prototyping vs. custom web development.
Implements Docker provider for running agents in containerized Linux environments with full isolation. Handles container lifecycle (creation, cleanup), image management, and volume mounting for persistent storage. Supports custom Dockerfiles for environment customization. Provides X11/Wayland display server integration for GUI application interaction. Enables reproducible agent execution across different host systems.
Unique: Implements Docker provider with X11/Wayland display server integration for GUI application interaction, container lifecycle management, and custom Dockerfile support. Enables reproducible agent execution across different host systems with container isolation.
vs alternatives: More lightweight than VMs because Docker uses container isolation vs. full virtualization; X11 integration enables GUI application support vs. headless-only alternatives.
Implements Windows Sandbox provider for isolated agent execution on Windows 10/11 Pro/Enterprise, and host provider for direct OS execution. Windows Sandbox provider creates ephemeral sandboxed environments with automatic cleanup. Host provider enables direct agent execution on live Windows system without isolation. Both providers support native Windows input simulation (SendInput API) and clipboard operations. Handles Windows-specific action execution (window management, registry access).
Unique: Implements both Windows Sandbox provider (ephemeral isolated environments with automatic cleanup) and host provider (direct OS execution) with native Windows input simulation (SendInput API) and clipboard support. Handles Windows-specific action execution including window management.
vs alternatives: Windows Sandbox provides better isolation than host execution while avoiding VM overhead; native SendInput API enables more reliable input simulation than generic input methods.
Implements comprehensive telemetry and logging infrastructure capturing agent execution metrics (latency, token usage, action success rate), errors, and performance data. Supports structured logging with contextual information (task ID, agent ID, timestamp). Integrates with external monitoring systems (e.g., Datadog, CloudWatch) for centralized observability. Provides error categorization and automatic error recovery suggestions. Enables debugging through detailed execution logs with configurable verbosity levels.
Unique: Implements structured telemetry and logging system with contextual information (task ID, agent ID, timestamp), error categorization, and automatic error recovery suggestions. Integrates with external monitoring systems for centralized observability.
vs alternatives: More comprehensive than basic logging because it captures metrics and structured context; integration with external monitoring enables centralized observability vs. log file analysis.
Implements the core agent loop (screenshot → LLM reasoning → action execution → repeat) via the ComputerAgent class, with pluggable callback system and custom loop support. Developers can override loop behavior at multiple extension points: custom agent loops (modify reasoning/action selection), custom tools (add domain-specific actions), and callback hooks (inject monitoring/logging). Supports both synchronous and asynchronous execution patterns.
Unique: Provides a callback-based extension system with multiple hook points (pre/post action, loop iteration, error handling) and explicit support for custom agent loop subclassing, allowing developers to override core loop logic without forking the framework. Supports both native computer-use models and composed models with grounding adapters.
vs alternatives: More flexible than frameworks with fixed loop logic; callback system enables non-invasive monitoring/logging vs. requiring loop subclassing, while custom loop support accommodates novel agent architectures that standard loops cannot express.
+7 more capabilities