E2B's secure cloud sandboxes provide the fastest, safest way to let AI agents execute code — purpose-built for the AI era with sub-second spin-up and full isolation.
E2B (short for 'edge to browser') provides secure, sandboxed cloud environments where AI agents can write and execute code safely. Each sandbox is an isolated micro-VM that spins up in milliseconds, letting AI models run code, install packages, access the filesystem, and use the internet without risking your infrastructure. E2B is designed specifically for AI agent use cases — coding assistants, data analysis agents, and autonomous AI that needs to execute generated code. The platform offers SDKs for Python and JavaScript, supports custom sandbox templates, and handles the infrastructure complexity of running untrusted AI-generated code at scale.
Provides a safe environment for your AI to write and run code without touching your real systems.
E2B (short for "Environment to Boot") provides secure cloud sandboxes purpose-built for AI agents and AI-generated code execution. The core problem E2B solves is deceptively important: when an LLM generates code, where does it run safely? Running AI-generated code on production servers or local machines is a security nightmare — the code could be malicious, buggy, or resource-hungry. E2B provides isolated, ephemeral sandboxes that spin up in ~150ms, let the AI run arbitrary code, and tear down cleanly afterward.
Each E2B sandbox is a lightweight microVM (based on Firecracker, the same technology behind AWS Lambda) running a full Linux environment. Unlike containers, these microVMs provide hardware-level isolation — a sandbox cannot escape to the host or affect other sandboxes even if the code running inside is intentionally malicious. Each sandbox gets its own filesystem, networking stack, and process namespace.
The developer experience centers on E2B's SDK (Python and JavaScript/TypeScript). You create a sandbox with a single API call, execute code in it, read/write files, install packages, start long-running processes, and interact with the results programmatically. The sandbox supports Python, JavaScript, R, Java, and any language you can install in a Debian environment. For AI agent frameworks, E2B provides pre-built integrations with LangChain, CrewAI, AutoGen, Vercel AI SDK, and OpenAI's function calling — making it trivial to give any agent a "code execution" tool.
E2B's custom sandbox templates let teams pre-configure environments with specific packages, data files, and system configurations. A template is defined by a Dockerfile, built once, and then every sandbox instance from that template starts in the pre-configured state in milliseconds. This is critical for production use cases where agents need specific libraries (pandas, sklearn, playwright) available without waiting for installation.
Pricing is usage-based: you pay per second of sandbox runtime with different rates based on CPU/RAM allocation. The free tier includes generous sandbox hours for development. For enterprise use, E2B supports dedicated infrastructure, custom regions, and VPC peering.
Key limitations include no GPU support yet (ruling out ML training or inference workloads inside sandboxes), the ephemeral nature of sandboxes (no persistent storage between sessions without explicit data export), and the requirement for internet connectivity (no offline/on-premise option). Despite these constraints, E2B has become the de facto standard for AI code execution, used by companies like Anthropic, Salesforce, and numerous AI coding assistants.
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E2B is the go-to choice for AI code execution sandboxes, praised for its fast startup times, strong security isolation, and clean SDK design. Integration with major agent frameworks is seamless. Users note the lack of GPU support as a significant limitation, and ephemeral-only storage can complicate workflows that need persistence. Pricing is fair for moderate usage but can add up for high-volume automated workloads.
Isolated sandbox environments for running untrusted code with strict resource limits, network policies, and filesystem isolation.
Use Case:
Letting AI agents write and execute code safely without risking the host system or accessing sensitive data.
Support for Python, JavaScript, TypeScript, and 10+ languages with pre-installed libraries and package management.
Use Case:
AI coding assistants that can write, test, and iterate on code in any popular programming language.
Long-running sandbox sessions that maintain state, installed packages, and file system changes across multiple executions.
Use Case:
Interactive development workflows where agents build on previous results without re-initializing the environment.
Sub-second environment provisioning with pre-warmed containers and snapshot-based restoration.
Use Case:
Real-time code execution in chatbots and agents where users expect instant results without waiting for setup.
Managed file system within sandboxes for reading, writing, and sharing files between execution steps.
Use Case:
Data processing pipelines where agents read input files, process data, and produce output files.
Simple REST API and language-specific SDKs for creating, managing, and interacting with sandbox environments.
Use Case:
Integrating code execution capabilities into existing applications and AI agent frameworks.
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E2B sandboxes use Firecracker microVMs, not Docker containers, providing hardware-level isolation that's significantly more secure. A malicious process in a Docker container can potentially escape to the host through kernel exploits — a microVM cannot, as it runs its own kernel. E2B also starts faster (~150ms vs seconds for Docker), is designed for ephemeral use, and provides a purpose-built SDK for programmatic interaction, unlike Docker which requires orchestrating docker exec commands.
Yes, E2B sandboxes have outbound internet access by default, allowing code to install packages via pip/npm, make API calls, fetch data from URLs, and interact with external services. This is configurable per sandbox. Inbound access is available via assigned sandbox URLs for running web servers. For security-sensitive workloads, network access can be restricted at the firewall level.
E2B sandboxes are ephemeral — when they're terminated, all data inside is permanently deleted. If your agent needs to persist results, it must explicitly export data before the sandbox is destroyed, either by writing to an external storage service, returning results through the SDK, or uploading files to S3/GCS. E2B does not currently offer persistent volumes between sandbox sessions.
As of early 2026, E2B does not support GPU-equipped sandboxes. This means you cannot run ML model training, GPU-accelerated inference, or CUDA-dependent code inside E2B. For workloads that need GPU access, consider Modal or Replicate for serverless GPU compute, or run those specific tasks outside the sandbox while using E2B for general code execution.
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In 2026, E2B enhanced its code execution sandbox with persistent sandboxes that maintain state across sessions, built-in file system management, improved cold start times under 500ms, native integration with major agent frameworks, and a new Desktop sandbox for GUI-based interactions.
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