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caro
caro: fast Rust CLI that turns natural‑language tasks into a safe POSIX command. Built for macOS (MLX/Metal) with a built‑in model; supports vLLM/Ollama/LM Studio. JSON‑only output, safety checks, confirmation, multi‑step goals, devcontainer included.
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Recent activity35% weight
100
Community adoption25% weight
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Maintenance state20% weight
40
License clarity10% weight
100
Project information10% weight
100
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README preview
caro
✨ Now Generally Available! - Published on crates.io with all core features working. Visit caro.sh for more info.
🆕 CaroML preview — Caro now interprets .caro task files: an eight-keyword line-keyword DSL that lets you commit intent and have Caro generate the script. Per-platform variants, A/B challengers, runbook drift detection, and the bundled caro-scaffold skill for any skill-aware coder agent. See docs/caroml/intro.md and examples/library/system/.
caro (formerly cmdai) converts natural language descriptions into safe POSIX shell commands using local LLMs. Built with Rust for blazing-fast performance, single-binary distribution, and safety-first design with intelligent platform detection.
$ caro "list all PDF files in Downloads folder larger than 10MB"
Generated command:
find ~/Downloads -name "*.pdf" -size +10M -ls
Execute this command? (y/N) y
💬 Community & Support
Have questions or want to discuss caro with other users? Join the community!
Discussions - Ask questions, share ideas, and get support from the community
This project is generally available with all core features implemented, tested, and working. The CLI achieves 93.1% pass rate on comprehensive test suite with zero false positives in safety validation.
Note: The project was originally named cmdai but has been renamed to caro. See Naming History for details.
✅ Completed & Published
✨ Published to crates.io - Install via cargo install caro
📦 Pre-built binaries - Download for Linux, macOS, Windows (all architectures)
🎯 Core CLI structure with comprehensive argument parsing
🏗️ Modular architecture with trait-based backends
🧠 Embedded model backend with MLX (Apple Silicon) and CPU variants
🤖 Agentic context loop - Iterative refinement with platform detection
🌍 Platform-aware generation - Detects OS, architecture, available commands
📍 Execution context detection - CWD, shell type, system constraints
🌐 Remote backend support (Ollama, vLLM, Exo, Mesh-LLM, AI-Horde) with automatic fallback
🌍 Platform-aware - Automatically detects OS, architecture, shell, and available commands
🛡️ Safety-first - Comprehensive validation with 52+ dangerous command patterns
📦 Self-contained - Single binary distribution with embedded models
🎯 Multiple backends - Extensible system supporting MLX, CPU, vLLM, and Ollama
💾 Model management - Built-in model loading with optimization
🌐 Cross-platform - Full support for macOS (including Apple Silicon), Linux, and Windows
🎬 Safe execution - Optional command execution with shell-aware handling
How Caro fits in the guardian agent architecture
In 2026, Gartner named Guardian Agents as a formal market category: autonomous oversight layers that monitor, constrain, and govern AI agent behavior in production. Microsoft's Agent Governance Toolkit (OWASP + EU AI Act compliance) and Orchid Security's zero-trust identity layer are two early category leaders.
Most guardian agent products focus on policy, identity, and observability — who is the agent, what permissions does it have, what did it do. Caro occupies a complementary but distinct layer: execution safety for shell and tool calls. It sits at the exact point where a prompt becomes a command, applying deterministic validation before anything reaches the OS.
Guardian concern
Caro's contribution
Prompt injection → RCE
Deterministic regex + CVE rule validation breaks the injection→execution chain (see CVE-2026-25592, CVE-2026-26030)
Tool-call governance
Every shell command passes through SafetyValidator regardless of which agent or backend produced it
Risk-tiered approval
CRITICAL / HIGH / MEDIUM / LOW risk levels map directly onto tiered human-in-the-loop approval patterns
Auditability
Structured JSON output and telemetry hooks give guardian orchestrators a machine-readable safety signal
Defense in depth
5-layer validation pipeline (allowlist → built-in patterns → CVE rules → custom patterns → user confirmation) — no single bypass produces execution
In short: if your guardian agent stack needs a safety primitive that sits between "the LLM decided to run a command" and "the command hits the shell", Caro is that layer.
# Generate a shell command from natural language
caro "list all PDF files larger than 10MB"
# Output:
# Generated command:
# find . -name "*.pdf" -size +10M
#
# Execute this command? (y/N)
Usage
Basic Syntax
caro [OPTIONS] <PROMPT>
Examples
# Basic command generation
caro "list all files in the current directory"
# With specific shell
caro --shell zsh "find large files"
# JSON output for scripting
caro --output json "show disk usage"
# Adjust safety level
caro --safety permissive "clean temporary files"
# Auto-confirm dangerous commands
caro --confirm "remove old log files"
# Verbose mode with timing info
caro --verbose "search for Python files"
Force LLM inference, bypass static pattern matcher
✅ Implemented
Examples (Target Functionality)
# Simple command generation
caro "compress all images in current directory"
# With specific backend
caro --backend ollama "find large log files"
# Force LLM inference (bypass cached patterns)
caro --backend embedded --force-llm "list files"
# Verbose mode for debugging
caro --verbose "show disk usage"
Use Caro from your agent
Caro is built to be called by other coder agents and agentic IDEs. Pick the
surface that matches your stack:
Tool
How
Status
Claude Code
Auto-discovered skill at .claude/skills/caro-shell/SKILL.md (bundled with this repo). Triggers when the user asks for shell-command synthesis.
✅ Working
Claude Code (MCP)
caro mcp serve — exposes generate_command / validate_command / explain_safety over the Model Context Protocol.
🚧 In progress
Codex / Cursor / Continue / Aider / Tabby
Point at caro serve --openai (an OpenAI Chat Completions endpoint backed by caro's safety validator).
