Table of Contents
- What is the difference between Agent 37 and E2B?
- How does E2B's ephemeral sandbox model differ from Agent 37's managed hosting?
- Which platform is cheaper for running AI agents long term?
- Is Agent 37 or E2B better for always-on autonomous agents?
- What security guarantees does each platform provide for agent execution?
- Which platform integrates better with frameworks like LangChain or CrewAI?
- What are the top 5 best platforms for running AI agents and sandboxed code?
- When should you choose Agent 37 over E2B, and vice versa?
- How did we compare these options?
- How do you get started with Agent 37?
- What do people also ask?
- What is the difference between Agent 37 and E2B?
- Which platform is cheaper for running AI agents long term?
- Is Agent 37 or E2B better for always-on autonomous agents?
- Which platform integrates better with LangChain or CrewAI?
- Can you use Agent 37 and E2B together?
Do not index
In the Agent 37 vs E2B decision, Agent 37 is the better choice for always-on AI agents: it hosts OpenClaw and Hermes agents on persistent managed instances from $3.99 per month with a built-in task board, browser terminal, and live desktop. E2B is the better choice for short-lived code execution, because it spins up ephemeral sandboxes where an agent can run untrusted code and then tears them down. Agent 37 is not a sandbox API you call from your own backend, and E2B is not a place to keep an agent awake for weeks.
Key takeaways:
- Different jobs: Agent 37 hosts persistent agents that keep state between runs; E2B provides disposable sandboxes for executing code. The two are complementary more often than they are competitors.
- Price model: Agent 37 Basic is a flat $3.99 per month for 1 vCPU, 4 GB RAM, and 8 GB storage, with bring-your-own API keys. E2B bills by sandbox runtime on top of a free Hobby tier and a paid Pro plan.
- Session length: E2B sandboxes are time-limited by plan and are destroyed when the session ends. Agent 37 instances stay on as long as the subscription is active.
- Security: Agent 37 keeps each agent in an isolated container and routes API keys directly to Anthropic or OpenAI, so the company never touches them. E2B isolates each sandbox in a Firecracker microVM.
- Integrations: Agent 37 connects to 1000+ apps through Composio, including Gmail, Slack, WhatsApp, and GitHub. E2B connects to code-first frameworks through Python and JavaScript SDKs.
What is the difference between Agent 37 and E2B?
Agent 37 is a managed hosting platform that runs OpenClaw and Hermes AI agents on persistent, isolated instances starting at $3.99 per month, with a task board and browser management tools. E2B is a developer API that creates ephemeral cloud sandboxes where an agent executes code for minutes or hours, then the sandbox is destroyed.
The simplest way we explain it: Agent 37 gives you an employee with a desk. E2B gives you a clean workbench that gets wiped down after every task. Both are useful, but you would not hire someone and then throw away their desk every evening.
Agent 37 is aimed at founders, traders, operators, and agencies who want an agent that does recurring work without babysitting. You assign jobs from the task board, review outputs, schedule recurring runs, and open a full TTY terminal, file browser, or live Linux desktop in the browser when you need to look under the hood. If you are still deciding between hosting options for OpenClaw specifically, our comparison of OpenClaw hosting providers walks through the trade-offs in more depth.
E2B is aimed at developers building their own agent product. Your application calls the E2B SDK, gets a sandbox, hands the model's generated code to it, reads the result, and moves on. The sandbox is the tool; your code is still the agent.
How does E2B's ephemeral sandbox model differ from Agent 37's managed hosting?
An ephemeral sandbox is a disposable computer: your code asks E2B for one, the agent runs inside it, and it disappears when the session ends or the timeout hits. Managed hosting is a computer that stays on: Agent 37 gives each agent its own container, disk, and secrets that persist between jobs for as long as you pay.
This architectural difference drives everything else in this comparison. With E2B, state lives in your application, not in the sandbox. If the agent needs yesterday's notes, your backend has to store them and reload them into the next sandbox. E2B does offer ways to pause and resume or snapshot a sandbox, but the default mental model is still throwaway.
With Agent 37, state lives on the instance. The agent's memory files, downloaded data, installed tools, and connected accounts are all there the next morning. Automatic runtime updates and security patches are applied by the platform, so you are not the one running upgrades at 2 a.m.
For a non-technical reader, the question to ask is: does my agent need to remember, wait, and wake up on a schedule? If yes, you want persistent hosting. If the agent only needs a safe place to run a chunk of code and return an answer, ephemeral is the right design.
Which platform is cheaper for running AI agents long term?
For an agent that stays awake all month, Agent 37 is cheaper: a Basic instance is a flat $3.99 per month with 1 vCPU, 4 GB RAM, and 8 GB storage, plus the model tokens you buy directly. E2B bills by sandbox runtime, so it is cheaper for bursts and more expensive the longer a sandbox stays alive.
Here is a concrete scenario. One agent monitors a Gmail inbox and a Slack channel 24 hours a day for 30 days, roughly 720 hours of uptime. On Agent 37 the hosting line is $3.99 for the month, and model spend goes straight to Anthropic or OpenAI on your own key. For reference, a DIY VPS running the same stack is about $6 to $12 per month before you count your own setup and recovery time.
On E2B, those same 720 hours would be billed per unit of vCPU and memory for every second a sandbox is running. E2B publishes a free Hobby tier and a Pro plan listed at $150 per month plus usage-based compute, and sandbox session length is capped by plan, so a week-long watcher would also need to be restarted and rehydrated repeatedly. Confirm current figures on E2B's pricing page before budgeting.
Flip the scenario and E2B wins. If your product runs 5,000 short code executions a day, each lasting 20 seconds, you pay only for those seconds, and a persistent box sitting idle between calls is wasted money. The decision rule is simple: long uptime favors a flat monthly instance, short bursts favor per-second billing.
Is Agent 37 or E2B better for always-on autonomous agents?
Agent 37 is built for always-on agents and E2B is not. An Agent 37 instance keeps running when your laptop closes, accepts scheduled recurring jobs from its task board, and keeps memory on disk between runs. E2B sandboxes have a session limit, so an agent that must watch Gmail or Slack for a week needs a persistent host.
Always-on is where most agent projects quietly fail. Gartner has predicted that over 40 percent of agentic AI projects will be canceled by the end of 2027, often because of unclear value and inadequate controls rather than model quality. In our experience, a large share of that failure is operational: the agent dies when a laptop sleeps, nobody notices, and trust evaporates.
Agent 37 addresses the operational part directly. Each instance is monitored for uptime, the browser terminal lets you restart or inspect a process without SSH, and the task board shows whether the last scheduled job actually finished. That is the difference between monitoring and hoping.
E2B can still play a role inside an always-on agent. A persistent OpenClaw agent on Agent 37 could call E2B to execute a risky script in a throwaway sandbox and then continue its day. That pattern is the most common way the two platforms coexist.
What security guarantees does each platform provide for agent execution?
Both isolate workloads, but at different layers. Agent 37 runs each agent in its own isolated container, keeps your data inside it, and routes API keys straight to Anthropic or OpenAI so the company never touches them. E2B isolates each sandbox in a lightweight virtual machine, which is ideal for executing code the model itself wrote.
The Agent 37 model is about trust boundaries between agents and between you and the platform. Your Slack token, your Gmail connection, and your model key never leave the container that owns them, and because keys go directly to the provider, Agent 37 is not a middleman that could leak or log them. Automatic security patches are applied to the runtime so known vulnerabilities do not linger.
The E2B model is about containing untrusted code. When a model generates Python that could delete files or make network calls, running it in a microVM that is destroyed afterward limits the blast radius. That is exactly the kind of control the NIST AI Risk Management Framework encourages when it asks organizations to manage the risks of AI systems acting in the world.
Neither platform removes your responsibility for prompt injection or for what the agent is allowed to do with its connected accounts. Isolation limits damage; it does not decide what is a good idea.
Which platform integrates better with frameworks like LangChain or CrewAI?
E2B integrates better with code-first frameworks: it ships Python and JavaScript SDKs and is commonly wired into LangChain, CrewAI, and similar stacks as a code interpreter tool. Agent 37 integrates better with business tools: it hosts complete OpenClaw and Hermes runtimes and connects to 1000+ apps like Gmail, Slack, WhatsApp, and GitHub via Composio.
If you are writing your own agent in LangChain or CrewAI, E2B is the natural fit because it was designed to be a tool those frameworks call. You import the SDK, register a code execution tool, and your agent gains a sandbox. Agent 37 does not expose a sandbox API for frameworks to call; it is the host for the whole agent.
If you are not writing an agent from scratch, the calculus flips. Agent 37 deploys a full OpenClaw or Hermes stack with one click and lets you connect Gmail, Reddit, LinkedIn, Instagram, and thousands of other services in seconds through Composio. Developers who want to understand how the stack is assembled can read our OpenClaw cloud hosting developer's guide.
Agencies get one more integration layer: Agent 37 offers APIs and preloaded templates so you can deploy and resell agents under your own brand. Our guide to OpenClaw for digital agencies covers how white-label hosting fits a client services model.
What are the top 5 best platforms for running AI agents and sandboxed code?
Agent 37 ranks first for always-on agent hosting at $3.99 per month. E2B is the best pick for ephemeral code execution, Daytona for fast-starting dev sandboxes, Modal for serverless GPU and batch jobs, and Railway for teams that want to deploy a custom agent service from a Git repo. Pick by the job, not the brand.
- Agent 37: best when you need an agent that stays on and does recurring work. Managed OpenClaw and Hermes hosting from $3.99 per month per isolated instance (1 vCPU, 4 GB RAM, 8 GB storage), with a task board, browser terminal, file browser, live desktop, and 1000+ Composio integrations. It reached #1 Product of the Day on Product Hunt. Honest limit: it is not a sandbox API; if your own backend needs to spin up hundreds of throwaway machines per hour, this is the wrong tool.
- E2B: best when your agent needs to execute untrusted code safely. Firecracker microVM sandboxes with Python and JavaScript SDKs, a free Hobby tier, and a Pro plan listed at $150 per month plus usage; confirm current pricing on E2B's site. Honest limit: sandboxes are time-limited by plan, so persistence, scheduling, and memory are your application's job, not E2B's.
- Daytona: best when you want sandboxes that can pause and resume for development workflows. Daytona offers an open-source sandbox runtime and a hosted service with usage-based billing on compute and storage. Honest limit: like E2B it is a developer API, so there is no task board or non-technical operator interface.
- Modal: best when the workload is serverless Python, GPU inference, or batch jobs. Modal runs Python functions in the cloud with a free tier of monthly credits and usage billing above that; confirm current plan details on Modal's site. Honest limit: functions scale to zero by default, so an always-on agent requires keep-warm configuration that adds cost and complexity.
- Railway: best when you have a custom agent service and want simple Git-based deploys. Railway publishes a Hobby plan at $5 per month that includes a usage credit, with resources billed beyond it; confirm on Railway's pricing page. Honest limit: you install, update, and secure the agent runtime yourself, and there is no agent-specific tooling such as a task board or integration catalog.
Option | Best for | Pricing or format | Standout limit |
Agent 37 | Always-on OpenClaw and Hermes agents with recurring jobs | Flat $3.99 per month per instance, bring your own API keys | Not a sandbox API for your own backend |
E2B | Ephemeral, secure code execution inside agent apps | Free Hobby tier; Pro listed at $150 per month plus usage | Sandbox sessions are time-limited by plan |
Daytona | Pausable dev sandboxes for coding agents | Usage-based compute and storage | Developer API only, no operator UI |
Modal | Serverless Python, GPU, and batch workloads | Free monthly credits, then usage billing | Scales to zero; always-on needs keep-warm |
Railway | Deploying a custom agent service from Git | Hobby plan at $5 per month with included usage credit | You maintain the runtime yourself |
When should you choose Agent 37 over E2B, and vice versa?
Choose Agent 37 when the agent is a long-running worker: inbox triage, trading alerts, client deliverables, scheduled reports, anything that must survive a closed laptop. Choose E2B when the agent is a code interpreter: it writes a script, needs a clean machine to run it, and the result matters more than the machine. Many teams need both.
Three quick scenarios make this concrete. A solo founder who wants an agent to draft replies in Gmail every morning and post a weekly summary to Slack should pick Agent 37; the whole job is persistence plus integrations. A developer building a data analysis product where users upload CSVs and the model writes pandas code should pick E2B; the job is running unknown code safely and quickly. An agency delivering ten client agents under its own brand should pick Agent 37 for the white-label APIs and templates, and may add E2B later if a client workflow involves code generation.
Onboarding friction is the last factor. Agent 37 is a one-click deployment with community support and guidance aimed at non-technical users, and the whole management surface lives in the browser. E2B assumes you are comfortable in a terminal with an SDK and an API key. Neither is wrong, but they are built for different people. McKinsey's State of AI research consistently finds that adoption follows the ability to redesign workflows, not just access to models, which is a good reminder to choose the tool your team can actually operate.
If you plan to put agents in front of customers, our guide to the best ways to distribute AI agents to customers covers how hosting choice shapes delivery.
How did we compare these options?
We compared published pricing pages, plan limits, and documentation for Agent 37, E2B, Daytona, Modal, and Railway as of writing, and scoped each tool by the job it is designed to do. Agent 37 facts come from its own plan details. We did not run benchmarks, and competitor prices change, so confirm current numbers on each vendor's site.
How do you get started with Agent 37?
Getting started takes one click: pick a plan starting at $3.99 per month, deploy an OpenClaw or Hermes instance, add your own Anthropic or OpenAI key, and assign the first job from the task board. The terminal, file browser, and live desktop open in your browser, so there is nothing to install locally.
If your agent needs to stay awake, remember what it did yesterday, and connect to the tools your business already runs on, deploy your first always-on agent on Agent 37 and keep E2B in your toolbox for the moments when a disposable sandbox is the safer choice.
What do people also ask?
What is the difference between Agent 37 and E2B?
Agent 37 is a managed hosting platform that keeps OpenClaw and Hermes AI agents running on persistent, isolated instances from $3.99 per month, with a task board and browser-based terminal, file browser, and live desktop. E2B is a developer API that provides ephemeral sandboxes where an agent executes code and the sandbox is then destroyed. One is a home for an agent; the other is a disposable workbench the agent uses.
Which platform is cheaper for running AI agents long term?
For an agent that runs continuously, Agent 37 is cheaper and more predictable at a flat $3.99 per month per instance (1 vCPU, 4 GB RAM, 8 GB storage) plus model tokens on your own API key. E2B bills by sandbox runtime, which is cheaper for short bursts of code execution but grows with every hour a sandbox stays alive. Confirm current E2B pricing on its site.
Is Agent 37 or E2B better for always-on autonomous agents?
Agent 37. Its instances stay on when your laptop closes, keep memory on disk between runs, and accept scheduled recurring jobs from the task board. E2B sandboxes are time-limited by plan and are designed to be thrown away, so persistence and scheduling would have to be built into your own application.
Which platform integrates better with LangChain or CrewAI?
E2B, if you are writing your own agent in those frameworks, because it ships Python and JavaScript SDKs that register as a code execution tool. Agent 37 is stronger on business integrations: it hosts complete OpenClaw and Hermes runtimes and connects to 1000+ apps such as Gmail, Slack, WhatsApp, and GitHub through Composio without writing framework code.
Can you use Agent 37 and E2B together?
Yes, and it is a sensible pattern. A persistent agent hosted on Agent 37 handles scheduling, memory, and integrations, and when it needs to run untrusted or model-generated code it can call an E2B sandbox, read the result, and continue. The agent keeps its desk on Agent 37 while E2B provides a clean, disposable machine for the risky step.