AgentSphere

AgentSphere is an AI-native cloud infrastructure providing secure code sandboxes for executing AI-generated code and handling files securely. It is designed as an AI agent sandbox for reliable LLM code execution, serving as an alternative to platforms like E2B. It allows seamless connection of MCP clients to isolated cloud sandboxes, purpose-built for AI workflows from rapid prototyping to production-grade tasks, ensuring agents have a first-class runtime.

How to Use

AgentSphere provides an infrastructure for running AI agents reliably and securely. Users connect their MCP clients to AgentSphere's isolated cloud sandboxes to execute AI-generated code, process files, and enable various AI workflows such as data analysis, visualization, virtual desktop agents, and DevOps automation. It acts as an execution backbone for AI-native applications, allowing agents to interact with Git, execute pipelines, and automate deployment in controlled, reviewable sessions.

Key Features

• First MCP-Integrated Cloud Sandboxes for Secure Code Execution
• Purpose-Built for AI Workflows (e.g., AI-Driven Data Analysis, Generative Data Visualization, Secure Virtual Desktop Agents)
• Instant Startup with cold-start latency as low as 100ms
• Enterprise-Grade Security backed by lightweight VMs (e.g., Firecracker) with SOC2 and GDPR compliance
• Stateful Execution supporting long-running tasks with snapshot recovery, storage persistence, and streaming output
• Model & Language Agnostic, supporting any LLM or runtime from Python to TypeScript
• Private Deployment options in AWS, GCP, or on-premise

Use Cases

• Secure Enterprise Code Execution in finance, healthcare, or government scenarios
• Agent-Driven DevOps Automation for self-healing, self-executing agents in CI/CD flows
• Large-Scale Model Evaluation with isolated, reproducible sandboxes and real-time monitoring
• Agent Runtime Core for AI Products, serving as the execution backbone for AI-native apps, copilots, or autonomous systems
• AI-Driven Data Analysis for securely processing internal datasets
• Generative Data Visualization for rendering AI-generated dashboards in isolated environments
• Secure Virtual Desktop Agents for browser or UI automation testing and simulation
• LLM Evaluation & Fine-Tuning for assessing code generation quality and autonomous behavior