E2B
Enterprise AI Agent Cloud for code execution and automation
E2B Overview
E2B is an enterprise-grade AI agent cloud platform that enables developers to execute and automate code in secure sandbox environments. It provides seamless integration with popular AI models like OpenAI, Anthropic, Mistral, and more, allowing users to run generated code in isolated Jupyter notebook cells. E2B solves the pain points of safely executing untrusted code, managing computational resources, and integrating AI-powered code generation with actual execution. The platform is designed for developers, data scientists, and enterprises looking to build AI-powered applications that require reliable code execution capabilities.
E2B Screenshot
E2B Official screenshot of the tool interface
E2B Core Features
Secure Code Execution
E2B provides isolated sandbox environments for executing Python and JavaScript code safely. The sandboxes prevent unauthorized access to system resources while maintaining full functionality for legitimate code execution. This is particularly valuable when running AI-generated code that might contain unexpected operations.
Multi-LLM Integration
The platform supports integration with various large language models including OpenAI's GPT-4, Anthropic's Claude, Mistral's Codestral, and more. This allows developers to choose their preferred AI model for code generation while using E2B's consistent execution environment.
Developer-Friendly SDKs
E2B offers easy-to-use SDKs for both JavaScript/TypeScript and Python, with simple installation via npm or pip. The SDKs provide straightforward methods for creating sandboxes, running code, and retrieving execution results, making integration into existing workflows seamless.
LangChain & LlamaIndex Compatibility
E2B works natively with popular AI frameworks like LangChain and LlamaIndex, allowing developers to incorporate code execution as part of their AI agent workflows. This enables building sophisticated AI applications that can generate and execute code in a single pipeline.
Resource Management
The platform offers fine-grained control over computational resources including vCPUs, memory allocation (from 512 MiB to 8,192 MiB), and storage. This ensures optimal performance for different workloads while maintaining cost efficiency.
E2B Use Cases
AI-Powered Data Analysis
Data scientists can use E2B to execute AI-generated data analysis scripts safely. The AI can generate Pandas or NumPy code to process datasets, and E2B executes it in isolation, preventing any potential security risks from untrusted code.
Educational Coding Assistant
Educational platforms can integrate E2B to provide students with AI-assisted coding help. The AI can generate example solutions or corrections, which are then executed safely in E2B sandboxes to demonstrate the results.
Automated Code Review
Development teams can build automated code review systems where AI suggests improvements or fixes, and E2B verifies the suggested changes by executing them in a sandbox before recommending them to developers.
How to Use E2B
Install the E2B SDK using npm or pip depending on your preferred language. For JavaScript/TypeScript: 'npm install @e2b/code-interpreter'. For Python: 'pip install e2b-code-interpreter'.
Import the Sandbox class from the E2B package in your code. Create a new sandbox instance which will automatically provision an isolated execution environment.
Generate code using your preferred AI model (OpenAI, Anthropic, Mistral, etc.) or write your own code to be executed. The code should be formatted for execution in a single Jupyter notebook cell.
Pass the generated code to the sandbox's runCode (JavaScript) or run_code (Python) method. The sandbox will execute the code in isolation and return the results, including output text, execution logs, and any errors.
Process the execution results in your application. The output can be used for further processing, displayed to users, or fed back into the AI model for additional refinement.