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 LangChain 3 tools — connect in under 2 minutes

Name: E2B
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 3 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect E2B through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "e2b": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using E2B, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

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High SecurityEnterprise-grade

IAMAccess control

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DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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.

LangChain's ecosystem of 500+ components combines seamlessly with E2B through native MCP adapters. Connect 3 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the E2B MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 3 tools from E2B via MCP

Why Use LangChain with the E2B MCP Server

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

The largest ecosystem of integrations, chains, and agents — combine E2B MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across E2B queries for multi-turn workflows

E2B + LangChain Use Cases

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

RAG with live data: combine E2B tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query E2B, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain E2B tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every E2B tool call, measure latency, and optimize your agent's performance

E2B MCP Tools for LangChain (3)

These 3 tools become available when you connect E2B to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

E2B + LangChain FAQ

Common questions about integrating E2B MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.