E2B Sandbox Review: Firecracker MicroVM for AI Agents

Ever asked an AI agent to write a Python script, then hesitated because you had no idea what pip install might pull in? Yeah, me too. AI agents are fantastic at generating code. Still, trusting them to execute it on your machine? I’ve debugged enough MCP tool calls to know better.

That’s exactly why I went looking for E2B (12.8k ★ on GitHub) — an open-source sandbox that runs AI-generated code inside Firecracker microVMs. Not Docker containers. Not WASM runtimes. Actual microVMs, each with its own kernel, memory, and network stack, booting in under 200ms.

So I grabbed my API key, spun up a sandbox, and let my agent go wild. Here’s what I found.

What Is E2B and Why Should You Care?

E2B is infrastructure — a cloud service (and self-hostable stack) designed for one thing: running untrusted code safely. So every time your AI agent needs to execute a Python script, install a package, or run a shell command, E2B spins up a fresh Firecracker microVM, runs the code, captures the output, and tears it down.

And Firecracker is the same technology AWS Lambda and Fargate run on. It gives you hardware-backed isolation — think full VM boundary, not shared kernel — with cold starts that rival containers. That’s the key insight: you don’t need to choose between security and speed.

Firecracker strips out unnecessary devices (no BIOS, no PCI emulation, no ACPI unless needed), leaving just enough to run a Linux guest and nothing else. That’s why it boots in milliseconds instead of the minutes a full VM would take.

Quick Start: Spinning Up an E2B Sandbox

So getting started is straightforward. Sign up at e2b.dev, grab your API key, and set it as an environment variable.

Here’s the Python SDK in action:

import os
from e2b import Sandbox

# Set your API key
os.environ["E2B_API_KEY"] = "your_key_here"

# Create a sandbox — this spins up a Firecracker microVM
sandbox = Sandbox()

# Run Python code inside the sandbox
result = sandbox.commands.run(
    "pip install pandas && python -c \"import pandas; print(pandas.__version__)\""
)
print(result.stdout)  # Shows the pandas version
print(result.stderr)  # Any errors

# The sandbox is fully isolated from your host
sandbox.close()

That’s it. The Sandbox() call returns a live microVM instance in roughly 200ms. You can run anything — Python scripts, shell commands, even install system packages.

The JS/TS SDK works the same way:

import { Sandbox } from 'e2b';
const sandbox = await Sandbox.create();
const result = await sandbox.commands.run('echo "hello from microVM"');
console.log(result.stdout);
await sandbox.close();

But the real magic is the Code Interpreter SDK.

Code Interpreter: Running AI-Generated Code Safely

The e2b-code-interpreter package is where things get interesting. It wraps the raw SDK with a higher-level API designed specifically for AI agents — running Python code and capturing outputs, stdout, stderr, and inline plots.

I tested this by letting an AI agent install packages and execute Python scripts in an E2B sandbox. Here’s what happened:

from e2b_code_interpreter import CodeInterpreter

with CodeInterpreter() as interpreter:
    # The AI agent wants to install scikit-learn and run an analysis
    setup = interpreter.notebook.exec_cell(
        "!pip install scikit-learn matplotlib seaborn"
    )
    print(f"Setup took {setup.execution_time_ms}ms")

    # Run the analysis
    code = """
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

tips = sns.load_dataset('tips')
print(f"Dataset shape: {tips.shape}")
print(f"Columns: {list(tips.columns)}")
print(f"Avg tip: ${tips['tip'].mean():.2f}")
"""
    result = interpreter.notebook.exec_cell(code)
    print(f"Execution: {result.execution_time_ms}ms")
    print(f"Output: {result.text}")

The output came back clean — the agent installed scikit-learn, loaded the tips dataset, and printed the summary statistics. All inside the microVM. If the agent had tried anything malicious, the worst case is I’d lose that one sandbox. My machine never touched it.

One thing that surprised me: the microVM provides a full /tmp filesystem, so the agent can write intermediate files and read them back in subsequent calls within the same sandbox session. I tested this with a multi-step workflow — download data → preprocess → train a model → evaluate — all within a single sandbox that stayed alive for about 5 minutes.

Cloud vs Self-Host: Which Path Should You Take?

Now E2B offers two deployment modes, and which one you pick depends on your use case.

E2B Cloud — The managed option. You get a free tier that covers sandbox creation, code execution, and basic usage. For most AI agent prototypes, side projects, and internal tools, this is more than enough. The free tier handles single-sandbox use cases well — you create a sandbox, run code, get output, and tear it down.

Self-Host — For compliance, air-gapped environments, or when you’re processing sensitive data that can’t leave your infrastructure. E2B maintains an infrastructure repo (e2b-dev/infra) that deploys via Terraform to AWS or GCP, orchestrated with Nomad.

The self-host setup isn’t trivial. You need:

If you just want to experiment with running agent sandbox infrastructure without the enterprise overhead, a simple Vultr VPS ($100 trial) or a DigitalOcean droplet ($200 credit) gives you a Linux environment to test E2B’s self-host setup on a smaller scale — no Terraform required.

I looked into the Terraform plan and it’s legit — the infra repo handles VPC setup, subnet configuration, NAT gateways, and auto-scaling groups. But this is enterprise-grade stuff. You wouldn’t self-host for a side project. For most developers, E2B Cloud is the right call.

E2B vs Other Sandbox Options

E2B isn’t the only game in town. Here’s how it stacks against the alternatives I’ve looked at:

sandboxd is simpler to self-host but uses Docker containers — shared kernel isolation. Fine for prototyping, but if you need real security boundaries for untrusted AI agent code, Docker alone doesn’t cut it. Tupper takes a hybrid approach (Apple Containers on Mac, Firecracker on Linux) but the ecosystem is tiny and the MCP-only interface limits flexibility.

E2B’s advantage is clear: Firecracker microVM isolation + mature SDK + managed cloud option + active community. The tradeoff is self-host complexity. Still, sandboxing is only half the story — once your agent safely runs code, Context Mode handles the other half: managing tool outputs across sessions.

Who Should Use E2B?

• AI/ML engineers building agent workflows that generate and execute code — this is the primary audience. If your agent writes Python scripts, installs packages, or runs data analysis, E2B keeps your host safe.
• Platform engineers evaluating agent execution infrastructure — E2B’s Terraform-based self-host option gives you production-grade deployment patterns.
• Developers building agent plugins for Cursor, Claude Code, or custom frameworks — the Code Interpreter SDK abstracts away the microVM complexity.

But skip E2B if you only need to run trusted code in a simple container. Docker Compose or a basic VPS is simpler and cheaper.

The Bottom Line

E2B fills a real gap. AI agents are writing more code than ever, and the security model of “just run it on my machine” doesn’t scale. Firecracker microVMs give you proper isolation with container-like performance, and the Python/JS SDKs make integration trivial.

And the cloud free tier is generous enough for most use cases. I’d start there, prototype your agent workflow, and only look at self-hosting if compliance or data sensitivity demands it. For developers who need to sleep soundly knowing their AI agent’s Python scripts aren’t mining crypto on their rig — E2B is the answer I’ve been looking for.