When an AI agent generates code and runs it, that code has to execute somewhere. Running it on your local machine with your user permissions, files, and network access is dangerous. E2B solves this by spinning up isolated cloud sandboxes where AI-generated code runs inside its own filesystem, process tree, and network namespace. When execution finishes, the sandbox is destroyed. This treat-every-execution-as-untrusted model is the same principle behind CI/CD runners, applied to the AI agent context.
The technical foundation is Firecracker, the microVM technology behind AWS Lambda. Each E2B sandbox boots a minimal Linux kernel in under 200 milliseconds with no cold starts, providing hardware-level isolation between workloads. This is meaningfully stronger than container-based isolation where workloads share a kernel. For executing untrusted AI-generated code that might attempt network access, filesystem operations, or process manipulation, microVM isolation provides a genuine security boundary.
The developer experience centers on remarkably simple SDKs in Python and JavaScript. Creating a sandbox, running code, and reading results takes fewer than ten lines. The Code Interpreter package adds Jupyter notebook-style execution with support for data visualization and file operations. Custom templates let you pre-install dependencies and configure environments that sandboxes inherit at startup, ensuring reproducible execution without paying the setup cost on every invocation.
LLM provider compatibility is universal. E2B works with OpenAI, Anthropic, Google, Mistral, and any model provider through straightforward SDK integration. The pattern is consistent: your LLM generates code, you pass it to E2B for execution, and return the results to the LLM for interpretation. This model-agnostic design means E2B slots into any AI stack without vendor coupling, whether you are building with GPT, Claude, Gemini, or open-source models through Ollama.
The Desktop sandbox extends E2B beyond code execution into full computer use. It provides a graphical Linux desktop environment that LLMs can control visually, enabling AI agents to interact with GUI applications, browse the web, and perform tasks that require a visual interface. Products like Manus use this capability to give their AI agents full virtual computer access, and the open-source Computer Use project demonstrates how to connect desktop sandboxes to vision-capable models.
Pricing follows a per-second billing model where you pay only for actual compute time. A single vCPU sandbox costs approximately five cents per hour with RAM included in the CPU price. The Hobby plan is free with a one-time 100 dollar usage credit and supports up to 20 concurrent sandboxes with one-hour sessions. Pro at 150 dollars per month extends sessions to 24 hours with more concurrency. Enterprise plans offer BYOC deployment, on-premise options, and self-hosting for organizations with strict data residency requirements.
The MCP server integration lets AI coding agents use E2B sandboxes directly within their workflows. Claude Code, Cursor, and other MCP-compatible tools can create sandboxes, execute code, and retrieve results without leaving the development environment. The Fragments template provides an open-source starting point for building Claude Artifacts-style experiences where users see AI-generated code execute in real time within an isolated sandbox.