E2B (pronounced “edge-to-backend”) is an open-source infrastructure platform that provides secure, isolated cloud sandboxes for running AI-generated code. Built on Firecracker microVMs (the same technology behind AWS Lambda), E2B enables AI agents to execute arbitrary code safely without risking the host system. With 11,000+ GitHub stars, $21M in Series A funding, and adoption by Fortune 100 companies, it has become the standard for sandboxed code execution in agentic AI systems.

E2B's architecture centers on Firecracker microVMs for hardware-level isolation:

• Firecracker MicroVMs — Each sandbox runs in its own lightweight virtual machine with a dedicated kernel, root filesystem, and network namespace. MicroVMs boot in under 200ms with minimal attack surface.
• Jailer Process — Wraps each Firecracker instance with cgroups, namespaces, and seccomp filters for defense-in-depth isolation.
• Resource Controls — Configurable CPU, memory, and storage limits prevent denial-of-service from runaway code.
• Network Isolation — Segmented network namespaces with configurable ingress/egress rules for secure internet access.
• Ephemeral by Default — Sandboxes auto-destroy on exit, ensuring no state leaks between sessions. Optional persistence for long-running agents (up to 24 hours).

The Code Interpreter SDK is E2B's primary interface, launching Jupyter servers inside sandboxes for LLMs to execute code:

• Available for Python (pip install e2b-code-interpreter) and JavaScript (npm install @e2b/code-interpreter)
• Supports Python execution via Jupyter kernel
• Returns execution results including text output, logs, errors, and generated files
• Integrates with OpenAI, Anthropic, and any LLM that supports function calling
• Context manager pattern for automatic sandbox lifecycle management

1. Creation — Sandbox.create() provisions a new Firecracker microVM in under 200ms
2. Code Execution — sandbox.run_code() sends code to the Jupyter kernel inside the sandbox
3. File Operations — Read, write, and manage files within the sandbox filesystem
4. Internet Access — Install packages via pip/npm, make API calls, download data
5. Process Management — Spawn child processes, run shell commands
6. Teardown — Sandbox destroys automatically on exit or timeout

• Sub-200ms Boot Time — Sandboxes start globally in milliseconds, not seconds
• Full Linux Environment — Complete OS with filesystem, networking, and process management
• Multi-Language Support — Primary Python support via Jupyter; extensible to JavaScript, shell, and more
• Secure Isolation — Hardware-level isolation via Firecracker prevents guest-to-host escapes
• Scalable — Spin up hundreds of concurrent sandboxes for parallel agent workloads
• 228K+ Weekly Downloads — Widely adopted npm package for the JavaScript SDK

from openai import OpenAI
from e2b_code_interpreter import Sandbox
 
# Create OpenAI client for code generation
client = OpenAI()
 
# Define the task
system_prompt = (
    "You are a data analyst. Write Python code to answer questions. "
    "Only output executable code, no explanations."
)
user_prompt = "Generate a bar chart of the top 5 programming languages by popularity in 2025."
 
# Get code from LLM
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt},
    ],
)
code = response.choices[0].message.content
 
# Execute in secure E2B sandbox
with Sandbox() as sandbox:
    # Install dependencies
    sandbox.run_code("!pip install matplotlib")
 
    # Run the generated code
    execution = sandbox.run_code(code)
    print("Output:", execution.text)
 
    # Check for errors
    if execution.error:
        print(f"Error: {execution.error.name}: {execution.error.value}")
 
    # Access generated files (e.g., chart images)
    for artifact in execution.results:
        print(f"Generated: {artifact}")

  ┌──────────────┐     ┌──────────────┐
  │   AI Agent   │     │   LLM API    │
  │  (your app)  │◄───▶│  (GPT-4o,    │
  └──────┬───────┘     │   Claude)    │
         │             └──────────────┘
         │ E2B SDK
         │
  ┌──────▼─────────────────────────────┐
  │         E2B Cloud Platform         │
  │                                    │
  │  ┌──────────────────────────────┐  │
  │  │    Firecracker MicroVM       │  │
  │  │  ┌────────────────────────┐  │  │
  │  │  │    Jupyter Server      │  │  │
  │  │  │  ┌──────────────────┐  │  │  │
  │  │  │  │  Python Kernel   │  │  │  │
  │  │  │  │  (code execution)│  │  │  │
  │  │  │  └──────────────────┘  │  │  │
  │  │  │  ┌──────────────────┐  │  │  │
  │  │  │  │   Filesystem     │  │  │  │
  │  │  │  └──────────────────┘  │  │  │
  │  │  └────────────────────────┘  │  │
  │  │  Isolated: CPU | RAM | Net   │  │
  │  └──────────────────────────────┘  │
  └────────────────────────────────────┘

• E2B GitHub Repository
• E2B Documentation
• E2B Website
• Build AI Data Analyst with E2B

• Modal — Serverless GPU compute for agent workloads
• Browser-Use — AI browser automation
• Composio — Tool integration platform for agents
• AutoGen Studio — Visual multi-agent workflow builder