Product Introduction

1. E2B is an open-source runtime environment designed to execute AI-generated code securely in cloud sandboxes using microVM technology. It provides isolated environments for AI agents to run code, access browsers, and utilize full operating system capabilities while maintaining safety and scalability. The platform supports multiple programming languages and frameworks through SDKs for JavaScript/TypeScript and Python. Its architecture is optimized for agentic AI workflows requiring both security and performance.

2. The core value of E2B lies in enabling safe execution of untrusted AI-generated code at scale through lightweight Firecracker microVMs. It eliminates security risks associated with running autonomous AI agents in production environments while offering sub-200ms startup times for responsive interactions. The platform serves as critical infrastructure for developers building AI applications that require code interpretation, data analysis, or browser automation capabilities.

Main Features

1. E2B employs Firecracker microVMs to create secure sandboxes with hardware-level isolation, preventing AI-generated code from accessing host systems or network resources. These sandboxes support full OS capabilities including filesystem I/O, package installation, and persistent storage across sessions lasting up to 24 hours. The environment automatically cleans resources post-execution while maintaining compatibility with Linux-based development workflows.

2. The platform operates as LLM-agnostic infrastructure compatible with all major AI models including OpenAI GPT-4o, Anthropic Claude 3, and Meta Llama 3. Developers can integrate E2B through dedicated SDKs that handle sandbox lifecycle management, code execution monitoring, and real-time output streaming. Pre-configured environments support Python, JavaScript, and other languages with options for custom templates through Docker-like configurations.

3. E2B provides enterprise-grade features including VPC deployment options for AWS/GCP and observability tools for monitoring AI agent activities. The runtime enables browser automation through headless Chrome instances and data visualization through built-in chart rendering capabilities. Performance optimizations ensure consistent sub-second latency for code execution, making it suitable for real-time AI applications requiring immediate feedback loops.

Problems Solved

1. E2B addresses critical security concerns when deploying AI agents that generate and execute arbitrary code, preventing potential system breaches or data leaks. Traditional container-based solutions lack sufficient isolation for untrusted AI outputs, while manual code review processes cannot scale with autonomous agent operations. The platform's microVM architecture provides necessary security guarantees without sacrificing development velocity.

2. The product specifically targets developers building production-grade AI agents requiring code interpretation capabilities, including AI chatbot platforms, automated research tools, and code generation systems. Data science teams benefit from secure environments for executing AI-driven data analysis, while enterprises gain compliant infrastructure for deploying AI solutions in regulated industries like finance and healthcare.

3. Typical use cases include autonomous AI agents performing web scraping with browser automation, data science workflows generating visualizations from raw datasets, and coding assistants executing code snippets for user queries. Additional applications involve running AI-generated code evaluations (SWE-benchmark), processing sensitive documents in isolated environments, and testing experimental AI models with full system access constraints.

Unique Advantages

1. Unlike traditional sandbox solutions using Docker containers, E2B leverages Firecracker microVMs developed by AWS for Lambda security, providing stronger isolation through lightweight virtualization. This architecture enables safer execution of untrusted code while maintaining faster startup times (200ms vs 2-5 seconds for containers) crucial for responsive AI applications.

2. The platform uniquely combines browser automation, filesystem access, and package management in a single runtime environment specifically designed for AI workflows. Features like 24-hour persistent sessions and custom sandbox templates allow developers to create specialized environments for complex AI tasks without compromising security protocols or performance benchmarks.

3. Competitive advantages include native integration with major AI model providers through pre-built SDKs and the ability to run sandboxes within enterprise VPCs. As open-source software, E2B offers transparency and customization unavailable in proprietary alternatives while maintaining commercial support options. The platform's battle-tested infrastructure handles over 10 million monthly sandbox executions with proven reliability.

Frequently Asked Questions (FAQ)

1. How does E2B ensure security for AI-generated code execution? E2B uses Firecracker microVMs with hardware virtualization to isolate each sandbox from host systems and other instances. All code executes in ephemeral environments with restricted network policies and automatic resource cleanup post-session. Security teams can audit the open-source runtime and deploy private instances within their cloud infrastructure.

2. What programming languages does E2B support? The platform supports any language that runs on Linux, including Python, JavaScript/Node.js, Ruby, and C++. Developers can install additional packages during runtime or create custom sandbox templates with pre-installed dependencies. Language kernels are managed through Jupyter-like cell execution models via API endpoints.

3. Can E2B integrate with custom LLMs not listed in documentation? Yes, the LLM-agnostic design allows integration with any AI model through standardized API calls. The SDKs provide tools for parsing LLM outputs into executable code blocks and handling error feedback loops. Users maintain full control over prompt engineering and response validation processes.

4. Is self-hosting available for enterprise deployments? Enterprises can deploy E2B in their AWS or Google Cloud environments with private VPC configurations. The platform supports Kubernetes deployments for scalable sandbox orchestration and offers enterprise licensing options with SLA guarantees. All data remains within the organization's cloud infrastructure during operations.

5. How does E2B handle long-running AI agent tasks? Sandboxes support continuous operations for up to 24 hours with configurable idle timeouts. Developers can implement checkpoints for state preservation and utilize persistent storage volumes for data retention between sessions. The platform automatically scales microVM instances based on workload demands through Kubernetes-based orchestration.