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 CrewAI 3 tools — connect in under 2 minutes

Name: E2B
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 3 Tools Framework

Connect your CrewAI agents to E2B through the Vinkius — pass the Edge URL in the `mcps` parameter and every E2B tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="E2B Specialist",
    goal="Help users interact with E2B effectively",
    backstory=(
        "You are an expert at leveraging E2B tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in E2B "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 3 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, E2B becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call E2B tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 3 tools from E2B

Why Use CrewAI with the E2B MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with E2B through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

E2B + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries E2B for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries E2B, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain E2B tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries E2B against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

E2B MCP Tools for CrewAI (3)

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

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

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

E2B + CrewAI FAQ

Common questions about integrating E2B MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.