E2B

E2B is a cloud sandbox platform purpose-built for AI code execution. It provides isolated, ephemeral environments where large language models can safely run generated code — a core infrastructure requirement for AI Agent Development workflows. The platform ships with TypeScript and Python SDKs under the @e2b/code-interpreter and e2b_code_interpreter packages respectively, making it accessible to the two dominant ecosystems in AI engineering.

How It Works

Developers initialize a sandbox via the SDK, which provisions an isolated cloud environment. The LLM — whether OpenAI, Anthropic, or a Hugging Face model — can then execute code within this sandbox through function calling (also referred to as tool use). The sandbox handles execution, captures output, and returns results to the calling agent. For LLMs that do not natively support tool use, E2B recommends prompting the model to output code snippets and managing execution programmatically through the SDK.

LLM Integration

E2B is explicitly designed to be LLM-agnostic. The official quickstart documentation provides integration examples for OpenAI's Python and JavaScript SDKs, Vercel AI SDK, LangChain, and Hugging Face's Inference API. LangChain examples include both basic chaining patterns and full tool-calling agent implementations, demonstrating depth beyond a simple wrapper. The Vercel AI SDK integration includes Zod schema validation for structured outputs, indicating attention to production-quality agent patterns.

MCP Protocol Support

A distinguishing feature is E2B's support for the Model Context Protocol (MCP). Sandboxes can be configured with MCP integrations that connect agents to external services during execution. Documented integrations include Browserbase for browser automation, Exa for web search, and Notion for workspace access. The MCP transport layer uses Streamable HTTP, and integration patterns are provided for the OpenAI Agents SDK in both JavaScript and Python. This positions E2B not merely as an execution environment but as a runtime that mediates between AI agents and external APIs.

Custom Sandbox Templates

Users can define custom sandbox templates through a builder API (Template class). Templates support base images, environment variables, and custom start commands with configurable timeouts. The build system distinguishes between development and production pipelines — development builds use a dev-specific entry point while production builds use a separate configuration — suggesting that E2B anticipates templates graduating from experimentation to deployment. Generated templates include a README.md with build and usage instructions.

File Operations and Environment Management

The platform supports uploading and downloading files at single, multiple, and directory levels. Custom packages can be preinstalled into sandbox templates, allowing teams to define environments with project-specific dependencies before agent execution begins.

Access and Pricing

New accounts receive one hundred dollars in credits, providing a substantial evaluation runway. Authentication uses an API key set as the E2B_API_KEY environment variable. Plan limits apply to template and sandbox usage; higher limits are available by contacting the E2B support team.

Positioning

E2B occupies a specific niche in the AI infrastructure landscape: it is an execution layer, not an agent framework. It does not build agents — it provides the sandbox where agents execute code. This separation of concerns makes it composable with any agent architecture (LangChain, Vercel AI SDK, OpenAI Agents SDK) and any model provider. The MCP integration extends this composability to external services, making E2B a candidate for multi-service agent workflows that require sandboxed execution with API access.