Agent Sandbox
Agent Sandbox is a secure, fast, and programmable runtime environment designed specifically for executing AI agent code with built-in isolation, allowing safe execution of untrusted LLM-generated code through containerization and Kubernetes integration.
https://agentsandbox.co/?ref=producthunt
Product Information
Updated:Feb 9, 2026
What is Agent Sandbox
Agent Sandbox is an enterprise-grade, cloud-native infrastructure platform that provides isolated execution environments for AI agents. Built on Kubernetes, it enables organizations to safely run untrusted code generated by large language models (LLMs) by creating sandboxed environments with persistent storage and stable identity. The platform combines essential tools like browser automation, shell access, file management, and code execution capabilities while maintaining strict security boundaries between different agent instances.
Key Features of Agent Sandbox
Agent Sandbox is a cloud-native controller that provides secure, isolated environments for executing AI agent-generated code. It offers features like sub-second sandbox startup, persistent storage, multi-session support, and comprehensive API access to browser automation, shell commands, and file operations. The platform integrates with Kubernetes and uses technologies like gVisor for isolation, making it particularly suitable for enterprise-grade AI agent deployments.
Secure Isolation: Uses gVisor and container technologies to create secure barriers between applications and the cluster node's OS, preventing unauthorized access and interference between different agents
Fast Startup & Performance: Achieves sub-second latency for sandbox creation through pre-warmed pools, with startup times around 200ms and quick resume capabilities
Comprehensive Development Tools: Includes built-in VNC browser, VS Code, Jupyter, file manager, and terminal access through API/SDK, all running in a single Docker container with shared filesystem
Multi-Session & Multi-Tenant Support: Enables isolation on a per-agent or per-user basis with state persistence across multiple interactions and conversations
Use Cases of Agent Sandbox
AI Code Execution: Safely execute and test LLM-generated code in isolated environments without risking production systems
Enterprise AI Development: Provide secure, scalable environments for developing and testing AI agents in corporate settings with sensitive data
Automated Testing: Create isolated environments for testing complex conversational flows and AI agent behaviors with synthetic data
Cloud-Native AI Deployment: Deploy and manage thousands of sandboxed AI agents in production Kubernetes environments
Pros
Enterprise-grade security with multiple isolation options
High performance with sub-second startup times
Comprehensive API and SDK support for easy integration
Built-in support for multiple development tools and environments
Cons
Requires Kubernetes infrastructure knowledge for deployment
May have higher operational complexity compared to simpler solutions
Usage-based pricing could become expensive for large-scale deployments
How to Use Agent Sandbox
Install the SDK: Install the Agent Sandbox Python SDK using pip: 'pip install agentsandbox-sdk'
Initialize Sandbox Client: Create a Sandbox client by initializing with base URL: 'c = Sandbox(base_url="http://localhost:8080")'
Create a Session: Create a new sandbox session with desired agent type (claude, codex, opencode, or amp) using: 'client.createSession("my-session", {agent: "claude", permissionMode: "auto"})'
Configure Environment: Specify any required dependencies, libraries or system tools in the sandbox manifest file for automatic installation
Upload Files: Upload any input files or data that your agent needs to process into the sandbox environment
Execute Code: Run Python code or shell commands securely inside the sandbox through the API
Stream Events: Monitor execution by streaming events: 'for event in client.streamEvents("my-session"): print(event.type, event.data)'
Retrieve Outputs: Download any output files, charts, or results generated by the agent in the sandbox
Clean Up: Use context managers or explicit cleanup calls to terminate sandbox sessions when done to free up resources
Monitor Usage: Track compute time and storage usage through the dashboard to manage costs ($0.00025/sec for compute, $0.0005/MB for storage)
Agent Sandbox FAQs
Agent Sandbox is a cloud-native controller and secure code execution API for AI agents that provides a sandboxed environment for running Python and shell commands. It combines Browser, Shell, File, MCP operations, and VSCode Server in a single Docker container.
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