E2B is an open-source cloud runtime designed for executing AI-generated code within secure sandboxes. It serves as a crucial tool for developing and deploying agentic and AI-powered applications, providing a robust and isolated environment for code execution.
What it does: E2B offers virtual machines (sandboxes) where AI agents can run code, perform data analysis, visualize data, interact with the internet, and execute terminal commands. It's built to be LLM-agnostic, working seamlessly with models from OpenAI, Anthropic, Mistral, Llama, and more.
How to use:
Developers integrate E2B into their applications using client SDKs. For JavaScript/TypeScript, use @e2b/code-interpreter (via npm install @e2b/code-interpreter). For Python, use e2b-code-interpreter (via pip install e2b-code-interpreter).
Example (Python):
from e2b_code_interpreter import Sandbox
with Sandbox() as sandbox:
sandbox.run_code("x = 1")
execution = sandbox.run_code("x+=1; x")
print(execution.text)
Key Features:
- Fast Startup: Sandboxes launch in less than 200ms with no cold starts.
- Language Agnostic: Supports any AI-generated code (Python, JavaScript, Ruby, C++, etc.) that can run on a Linux environment.
- Secure: Powered by Firecracker microVMs for running untrusted code safely.
- Long Sessions: Sandboxes can run for up to 24 hours.
- Customization: Install any package or system library, and create custom sandbox templates.
- Self-hosting: Option to deploy E2B within your own AWS or GCP VPC.
E2B is trusted by leading companies like Perplexity, Hugging Face, Groq, and Lindy for various use cases including data analysis, code testing, deep research, and workflow automation.
Recommend MCP Servers 💡
cfbd-mcp-server
An MCP server that provides access to comprehensive college football statistics and data from the College Football Data API V2, enabling AI assistants like Claude Desktop to query game results, team records, player statistics, and more using natural language.
theishangoswami/exa-mcp-server
An MCP server enabling AI assistants like Claude to use the Exa AI Search API for web searches
JavaFilesystem
An MCP server implemented in Java that provides filesystem operations (read, write, edit, search, list, grep, create directory, bash command) and web access tools (fetch webpage, extract HTML content) for Large Language Model agents.
typesense-mcp-server
MCP server providing AI models with access to Typesense search capabilities
@kukapay/blockbeats-mcp
An MCP server that delivers blockchain news and in-depth articles from BlockBeats for AI agents.
@startreedata/mcp-pinot
A Python-based Model Context Protocol (MCP) server for interacting with Apache Pinot, enabling real-time analytics and metadata queries, designed for integration with Claude Desktop.