Tiny Open-source AI Agent for Shell Commands – Agent-C

Agent-C is an open-source, ultra-lightweight command-line AI agent that communicates with the OpenRouter API and executes shell commands using natural language.

It’s ideal for developers, sysadmins, and tech enthusiasts who need a low-resource AI tool for scripting and automation without the bloat of larger frameworks.

Features

• Tool Calling: Executes shell commands directly through AI responses, converting natural language requests into system operations.
• Optimized Binaries: Compresses to 4.4KB on macOS using GZEXE compression and approximately 16KB on Linux with UPX compression.
• Conversation Memory: Implements sliding window memory management for tracking conversation context without excessive memory usage.
• Cross-Platform Support: Works on both macOS and Linux systems with platform-specific optimizations.
• OpenRouter Integration: Connects to OpenRouter API for access to multiple AI models through a single interface.
• Minimal Dependencies: Requires only GCC compiler and curl command-line tool for basic operation.

Use Cases

• System Administration: Automate routine server maintenance tasks by describing what you need done rather than remembering complex command syntax.
• Development Workflow: Execute git operations, build processes, and deployment commands through conversational interface during coding sessions.
• File Management: Organize directories, search for files, and perform batch operations using natural language descriptions.
• Network Diagnostics: Run connectivity tests, check system status, and troubleshoot network issues through AI-interpreted commands.
• Quick Prototyping: Test command combinations and system interactions without writing full scripts for one-off tasks.

How to Use It

Getting Agent-C up and running is straightforward. Here’s a quick guide to get you started:

1. To get started, make sure you have the GCC compiler and the curl command-line tool installed. On macOS, gzexe is usually pre-installed, and on Linux, you need to install upx for optimal compression. You’ll also need an API key from OpenRouter.

2. Get source code from GitHub.

git clone https://github.com/bravenewxyz/agent-c.git
cd agent-c

3. The project uses a Makefile that handles the compilation and compression for your specific platform. This command will create the agent-c executable in the directory.

make

4. Set your OpenRouter API key as an environment variable. Replace your_openrouter_api_key_here with your actual key.

export OR_KEY=your_openrouter_api_key_here

5. Start the AI agent, and you can now interact with it using natural language.

./agent-c

Pros

• Extremely Small Size: At 4.4KB compressed, it’s likely the smallest functional AI agent available, perfect for embedded systems or minimal environments.
• Fast Startup: Minimal overhead means near-instantaneous launch times compared to heavyweight AI frameworks.
• Direct System Access: Executes shell commands immediately without additional abstraction layers or security restrictions.
• No Complex Setup: Simple build process with minimal dependencies reduces installation complexity.
• Memory Efficient: Sliding window memory management prevents excessive RAM usage during long conversations.
• Multi-Model Access: OpenRouter integration provides access to dozens of AI models through a single API endpoint.

Cons

• Security Risks: Direct shell command execution without sandboxing poses potential security vulnerabilities if misused.
• Limited Error Handling: Minimal codebase means fewer safeguards against malformed commands or unexpected system states.
• Command Line Only: No graphical interface limits accessibility for users uncomfortable with terminal environments.
• API Dependency: Requires internet connection and OpenRouter API access to function, no offline capability.
• Platform Limitations: Currently supports only macOS and Linux, excluding Windows users from native usage.

Related Resources

• OpenRouter API Documentation: Guide to setting up and using OpenRouter’s unified AI model API.
• GCC Compiler Documentation: Official documentation for the GNU Compiler Collection required for building Agent-C.
• UPX Binary Packer: Tool for compressing executables on Linux systems, used by Agent-C’s build process.
• curl Manual: Documentation for curl, the command-line tool used by Agent-C for API communication.
• AI Agent Frameworks Comparison: GitHub collection of AI agent projects for comparing different approaches and capabilities.

FAQs

Q: Is it safe to run Agent-C with shell command execution?
A: Agent-C executes commands directly on your system, which carries inherent security risks. Use it only in trusted environments and avoid running it with elevated privileges.

Q: Does Agent-C store conversation history permanently?
A: No, Agent-C uses sliding window memory management that keeps recent conversation context in memory during the session but doesn’t persist data between runs.

Q: Can I modify Agent-C for custom functionality?
A: Yes, Agent-C is released under CC0 license (“No Rights Reserved”), giving you complete freedom to modify, distribute, and use the code for any purpose without restrictions or attribution requirements.