How secure are E2B sandboxes?

E2B sandboxes run as dedicated Firecracker microVMs — the same technology used by AWS Lambda and Fargate. Each sandbox has its own Linux kernel, filesystem, and network stack, providing hardware-level isolation. Code running in a sandbox cannot access your host system, other sandboxes, or any external resources unless explicitly configured.

What programming languages are supported?

E2B supports Python, JavaScript/TypeScript, R, Java, and Bash out of the box. You can also create custom sandbox templates with any pre-installed tools, libraries, or system dependencies. The base template provides a full Ubuntu Linux environment where you can install anything via apt or pip.

How does E2B pricing work?

E2B uses usage-based pricing billed per second of compute time. The free Hobby plan includes a one-time $100 credit (no credit card required), up to 20 concurrent sandboxes, and 1-hour maximum session length. The Pro plan starts at $150/month with 24-hour sessions and higher concurrency limits.

E2B MCP Server for Pydantic AI 3 tools — connect in under 2 minutes

Name: E2B
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 3 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect E2B through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to E2B "
            "(3 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in E2B?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About E2B MCP Server

Connect your AI agent to E2B — the leading sandbox platform for AI code execution, trusted by OpenAI, Anthropic, and thousands of AI companies.

Pydantic AI validates every E2B tool response against typed schemas, catching data inconsistencies at build time. Connect 3 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Create Sandboxes — Spin up isolated Linux environments in ~150ms. Each sandbox is a Firecracker microVM with its own kernel, filesystem, and network
List Sandboxes — Monitor all active sandbox environments, their templates, and resource usage
Kill Sandboxes — Terminate environments when done to release resources and reduce costs

The E2B MCP Server exposes 3 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect E2B to Pydantic AI via MCP

Follow these steps to integrate the E2B MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 3 tools from E2B with type-safe schemas

Why Use Pydantic AI with the E2B MCP Server

Pydantic AI provides unique advantages when paired with E2B through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your E2B integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your E2B connection logic from agent behavior for testable, maintainable code

E2B + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the E2B MCP Server delivers measurable value.

Type-safe data pipelines: query E2B with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple E2B tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query E2B and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock E2B responses and write comprehensive agent tests

E2B MCP Tools for Pydantic AI (3)

These 3 tools become available when you connect E2B to Pydantic AI via MCP:

create_sandbox

The sandbox is an isolated Linux VM that starts in ~150ms. Use templates like "base" (default), "python3", or "node" for pre-configured environments. Default timeout is 300 seconds. Create a new isolated cloud sandbox for running code securely. Each sandbox is a Firecracker microVM with its own filesystem

kill_sandbox

The sandbox and its filesystem contents are permanently deleted. Terminate a running sandbox by its ID

list_sandboxes

Useful for monitoring active environments and managing resources. List all currently active sandboxes in your E2B account

Example Prompts for E2B in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with E2B immediately.

"Create a Python sandbox so I can run a data analysis script."

"Show me all my running sandboxes."

"Kill sandbox sbx_ghi789 — I'm done with it."

Troubleshooting E2B MCP Server with Pydantic AI

Common issues when connecting E2B to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

E2B + Pydantic AI FAQ

Common questions about integrating E2B MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your E2B MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.