Ephemeral sandboxes eliminate code execution risk

Every code execution runs inside an isolated microVM that is destroyed after use. Unlike running LLM-generated code directly on a server—where a hallucinated command could delete files or access secrets—E2B's sandboxes provide a clean, disposable environment with no persistent state. This architecture is essential for any production application that runs untrusted, AI-generated code.

Business outcome: Zero risk of agent-generated code compromising production infrastructure or sensitive data.

Full Linux filesystem and terminal access for AI agents

E2B sandboxes are not constrained REPLs—they provide a real, full Linux environment with a working filesystem, package managers, and terminal access. AI agents can install dependencies with pip or npm, write and modify files, run shell commands, and execute multi-step workflows exactly as a human developer would. This makes the sandbox genuinely useful for complex coding tasks, not just toy examples.

Business outcome: Agents can perform real-world development tasks—installing libraries, running test suites, and managing files—inside a secure enclosure.

Sub-second sandbox provisioning for real-time agent interaction

Cold-start latency is critical for interactive AI applications. E2B provisions new sandboxes in under one second, enabling agents to spin up fresh environments per user request without perceptible delay. This is a significant advantage over container-based approaches that can take tens of seconds to initialise, making E2B viable for real-time chat interfaces and IDE integrations where users expect instant responses.

Business outcome: Maintain responsive user experiences even when agents create new execution environments on every interaction.

First-class Python and JavaScript SDKs reduce integration time

E2B ships with well-documented Python and TypeScript SDKs that handle sandbox lifecycle, file upload/download, and command execution through clean, modern APIs. Rather than cobbling together Docker APIs and SSH connections, development teams can integrate secure code execution with a few lines of code. The SDKs include built-in streaming support, making them plug-and-play with frameworks like LangChain and custom agent loops.

Business outcome: Engineering teams ship code-execution features in days instead of weeks by using pre-built, battle-tested SDKs.

Custom sandbox templates ensure consistent agent environments

Teams define sandbox environments using Docker-like templates that pre-install specific libraries, tools, or language runtimes. This guarantees that every agent execution starts from the same known-good state, eliminating the debugging nightmare of inconsistent environments. For vertical-specific agents—such as data science tools that need pandas and numpy pre-installed—custom templates remove cold-start dependency installation time.

Business outcome: Predictable, reproducible execution environments eliminate environment-related failures and support use cases.

Execution logs and streaming output for debugging agent behaviour

E2B streams stdout, stderr, and sandbox lifecycle events in real time, giving developers full visibility into what their AI agents are doing inside each sandbox. This is invaluable during development and troubleshooting—when an agent writes buggy code, the team can replay the exact execution trace to understand what went wrong. The logging data also feeds into agent evaluation pipelines for measuring code-generation accuracy over time.

Business outcome: Faster debugging cycles and measurable agent performance tracking through detailed execution telemetry.

• Best-in-class sandbox isolation for untrusted code executionThe ephemeral microVM architecture provides genuine isolation—not just process-level separation—making it suitable for running completely unknown AI-generated code.
• Remarkably fast sandbox cold-start timesSub-second provisioning enables interactive use cases that would be impractical with slower container-based isolation approaches.
• Full Linux environment, not a restricted subsetAgents get a real filesystem, package managers, and shell access—enabling complex multi-step development workflows that shallow REPLs cannot support.
• Apache 2.0 open-source core enables self-hostingTeams can inspect the codebase, run E2B on their own infrastructure, and avoid vendor lock-in—critical for security-conscious enterprises and regulated industries.

• Not a complete AI agent or coding assistantE2B provides only the execution layer—teams must build the LLM integration, tool-calling logic, and orchestration themselves, which requires significant engineering investment.
• Usage-based pricing can escalate with continuous workloadsAgents that maintain long-running sandbox sessions or high concurrency will see costs rise predictably with usage—budget forecasting requires careful monitoring.
• Limited native integrations beyond Python and JavaScriptTeams building agents in Go, Rust, or other languages must wrap the REST API directly rather than using a first-class SDK, adding integration overhead.
• Professional RealityE2B is infrastructure for engineering teams, not a product for end users—if your goal is a pre-built AI coding assistant, look at tools like <a href='https://theaitoolsbox.com/tool/github-copilot-review/' style='color:#4F46E5;font-weight:700;text-decoration:none;'>GitHub Copilot</a> or <a href='https://theaitoolsbox.com/tool/cursor-review/' style='color:#4F46E5;font-weight:700;text-decoration:none;'>Cursor</a> instead of building your own agent with E2B as the runtime.

Building an AI code interpreter for a SaaS product

A B2B SaaS company wants to add a natural-language data analysis feature where users ask questions and the AI writes and runs Python code against their data. E2B provides the secure execution environment so the AI-generated pandas scripts run in isolation, never touching the company's core database or other users' data. Each user session gets a fresh sandbox, destroyed after the query completes, eliminating cross-tenant leakage risk.

Powering an autonomous AI coding agent startup

An early-stage startup is building an open-source autonomous developer agent that can write, test, and debug code across multiple files. E2B gives every agent instance its own Linux environment where it can install dependencies, run test suites, execute linting tools, and iterate on fixes. The team avoids building any sandbox infrastructure themselves, accelerating time-to-market from months to weeks and focusing engineering effort on the agent's reasoning logic rather than execution plumbing.

Adding secure code evaluation to an AI education platform

An edtech platform lets students submit coding problems and receive AI-generated feedback. Student-submitted code is inherently untrusted—it could contain infinite loops, malicious syscalls, or resource-exhausting operations. E2B executes every submission in an ephemeral sandbox with strict CPU and memory limits, then streams results back. The sandbox is terminated after each evaluation, guaranteeing that one student's code cannot affect another's session or the platform infrastructure.

Enterprise internal tooling for LLM-assisted development

A large financial institution wants to deploy internal AI coding assistants but cannot allow LLM-generated code to run on employee workstations or corporate servers due to compliance requirements. E2B's Enterprise plan—with on-premise deployment, audit logging, and custom sandbox templates pre-loaded with approved internal libraries—provides a compliant execution layer that satisfies security and regulatory teams while still empowering developers with AI-assisted coding capabilities.

Sign up for a free Hobby account at e2b.dev and obtain your API key from the dashboard—no credit card required for the free tier.

Install the E2B SDK for your language of choice (Python: `pip install e2b` or JavaScript: `npm install e2b`) and initialise the client with your API key.

Create your first sandbox template—start with the default template or define a custom one specifying the base image and pre-installed packages your agent needs.

Write a simple agent script that creates a sandbox, executes a shell command or runs a Python snippet inside it, streams the output, and then destroys the sandbox.