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Company's Building AI Agent Sandbox SDKs & APIs

AI agent sandbox SDKs

Sep 12, 2026
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Company's Building AI Agent Sandbox SDKs & APIs

I wrote about the fight for AI sandboxes back in March — the short version was that every AI agent that writes code needs somewhere safe to run it, and that "somewhere" is not cheap to build or operate. Six months later, that fight has a scoreboard. Money moved, categories collapsed, and a genuinely new pattern showed up: sandbox platforms that don't pick between human developers and AI agents anymore — they enroll both on the same infrastructure.
This is the competitor map. Who's building it, what they're actually selling, and where the open-source repos are if you want to self-host instead of paying per vCPU-hour.

What an AI agent sandbox actually is

An agent sandbox is an isolated compute environment — a microVM, a container, or a policy-wrapped process — where an AI agent (or a human dev, increasingly on the same platform) can execute arbitrary code without touching your production systems. Every serious offering in this space gives you three things: an SDK to spin one up programmatically, an API to manage its lifecycle, and some flavor of isolation between the sandbox and the host kernel.
The reason this become its own infra category instead of "just use a container" is that agents fail differently than humans. A human developer doesn't accidentally rm -rf a directory because a prompt injection told them to. Agents do. So the isolation boundary, the blast radius, and the cleanup story all matter more here than in normal dev-environment tooling.

Why the demand curve bent upward this year

A few things happened at once:
  • Every hyperscaler shipped a sandbox product. AWS AgentCore Code Interpreter, Google Agent Sandbox, Cloudflare's Sandbox SDK, and Vercel Sandbox all went GA or hit preview within roughly one quarter of each other. When the clouds show up in force, it's a signal the category graduated from "interesting infra bet" to "expected platform feature."
  • The ephemeral-vs-persistent debate basically ended. Early sandbox platforms picked a side — spin up fast and throw away, or keep a VM alive indefinitely. Now checkpoint/restore and pause/resume are close to table stakes across the board, which means the differentiation moved to isolation depth, billing model, and GPU access instead.
  • Real revenue showed up. Modal raised at a $4.65B valuation with roughly $300M in annualized revenue. Daytona closed a Series A and reportedly hit a $1M run rate within three months of launch. That's not hype-cycle money — that's usage-backed money, which tells you agent workloads are actually shipping to production, not just staying in demos.
This lines up with a pattern I flagged in the GPT-6 Astra piece — the interesting opportunity was never the frontier model itself, it's everything you have to build around it: sandboxing, permission gates, observability. Sandboxes are the first layer of that stack that's now got real competitors and real money behind it.

The independent leaders

These are the standalone companies competing purely on sandbox infrastructure, not bundling it into a bigger cloud.
  • E2B — the scale leader. Over a billion sandboxes started, used by roughly 94% of the Fortune 100, and no longer purely ephemeral: it now supports pause/resume that preserves full memory state on top of Firecracker microVMs. Raised a $21M Series A from Insight Partners.
  • Daytona — the open-source pick, sitting around 72.5K GitHub stars under an AGPL license. Sub-90ms sandbox creation, mid-execution snapshots with forking, and GPU sandboxes (H100s) if your agent needs to run inference inside the same environment it codes in. Closed a $24M Series A with Datadog and Figma as strategic backers.
  • Modal — the GPU-heavy option. If your agent needs T4-through-B200 access inside its sandbox, not just CPU, Modal is currently the deepest bench. It's also the only GPU-accelerated provider built into the OpenAI Agents SDK.
  • Sprites — persistent-first from day one, running on Fly.io infrastructure with ~300ms checkpoints and effectively zero idle cost when a sandbox is sleeping.
  • Blaxel — bets entirely on hibernation economics: sub-25ms resume with memory and processes intact, and $0 compute cost while standing by.
  • Northflank — the enterprise/VPC play. Deploy their sandbox runtime inside your own AWS, GCP, Azure, or Oracle account instead of theirs, with Kata or gVisor isolation per workload.
  • Runloop — built specifically around agent evals and benchmarking, with SWE-bench and Terminal-Bench wired in out of the box. If you're training or grading agents rather than just running them, this is the more specialized option.

The hyperscalers showing up late but heavy

  • AWS AgentCore Code Interpreter — managed Python/JS sessions up to 8 hours, billed per-second, with CloudTrail audit logging built in. Worth noting: security researchers demonstrated DNS-exfiltration and credential-extraction paths in its default network mode this year. AWS called the behavior intended and published hardening guidance rather than shipping a patch — so if you're evaluating it, read the network egress docs closely before trusting the word "sandboxed."
  • Google Agent Sandbox — Gemini Enterprise's execution layer, announced this spring, with sub-second creation and stateful sessions up to 14 days. Still preview-limited to one region as of this writing.
  • Cloudflare Sandbox SDK — runs on Cloudflare Containers plus a Dynamic Worker Loader for millisecond-start V8 isolates. If your stack is already on Workers, this is the path of least resistance, though Cloudflare itself notes isolates carry a larger attack surface than a hardware microVM.
  • Vercel Sandbox — Firecracker-based, persistent by default, with active-CPU-only billing (idle I/O wait isn't charged). Already in production under v0 and a few AI coding tools.

Open source repos worth actually cloning

If you'd rather self-host than pay per vCPU-hour, these are the live GitHub projects to watch:
  • agent-sandbox/agent-sandbox — self-hosted, Kubernetes-native, and explicitly E2B-protocol-compatible, so it works as a drop-in replacement for existing E2B-based agent code. No etcd, no message queue — it stores sandbox state as native Kubernetes objects, which keeps the operational surface small.
  • OpenSandbox — now org-independent and CNCF Landscape-listed, sitting around 11.5K stars with gVisor/Kata/Firecracker isolation options and Go/Python/Java/JS SDKs.
  • Microsandbox — local-first, running libkrun microVMs on your own machine with secrets injected at the network layer so sandbox code never sees them directly. Cloud version is in closed beta; the local version is free forever.
  • AIO Sandbox — a single Docker image bundling shell, browser, Jupyter, VS Code, and MCP support in one container. ByteDance-affiliated, Apache 2.0.
  • ComputeSDK — not a sandbox itself but an abstraction layer: one TypeScript API over roughly nine underlying providers (E2B, Modal, Daytona, Runloop, Cloudflare, Vercel and others), useful if you don't want to hard-lock into one vendor's SDK.
  • Anthropic's Sandbox Runtime (srt) — OS-level sandboxing using Seatbelt on macOS and bubblewrap on Linux plus a network-filtering proxy, no container required. Good reference if you're building something lighter-weight than a full microVM platform.
Worth checking these against a live GitHub trending scan too — I covered how to turn that kind of scan into new product ideas in an earlier piece on mining GitHub trends for SaaS ideas, and the open-source AI tooling landscape moves fast enough that a monthly re-check is worth the ten minutes.

The part you actually asked about: sandboxes for humans and agents

This is an interesting shift. A handful of platforms stopped treating "dev environment for a human" and "execution sandbox for an agent" as two products:
  • Daytona started as a self-hostable, git-based dev-environment manager for human developers and extended the same infrastructure to agent code execution — same provisioning, same isolation, same SDK either way. It also ships Computer Use across Linux, Windows, macOS, and Android, which matters if you want one platform where a human can pair-program inside the same box an agent is operating.
  • CodeSandbox SDK (mid-rebrand to Together Code Sandbox) runs on the same microVMs that power its browser-based human IDE product — a human can open a live fork of the exact sandbox an agent just wrote code in, with git-versioned filesystem state carried over.
  • Coder's ai-sdk monorepo bundles an agent package alongside a sandbox package, explicitly built to let a coding agent and a human developer share the same workspace lifecycle tooling instead of maintaining two separate systems.
If you're building a product where both a founder and their agent need to poke at the same running environment — which is increasingly the actual workflow for solo builders — this is the category to watch instead of the pure-agent-only sandboxes above. It's a smaller list today, but it's where the "one person AI organisation" pattern I wrote about here actually needs its infrastructure to live.

How to actually pick one

Skip the marketing pages and check these four things instead:
  • Isolation tier. Hardware microVM (Firecracker, libkrun) beats gVisor/Kata beats shared-kernel containers beats V8 isolates, in that order, for anything running untrusted or agent-generated code.
  • Billing while idle. Some platforms bill per-second regardless of activity; others (Vercel, Sprites, Blaxel) charge close to zero while a sandbox is asleep. For agent loops that spin up and tear down constantly, this is the number that actually moves your bill.
  • Cold start time. If a human is waiting on the result — a coding assistant that creates an environment per interaction — cold start compounds fast. Sub-100ms creation (Daytona, Vercel) versus multi-second creation (some GPU-backed platforms) is a real UX difference, not a benchmark footnote.
  • GPU access. Only a handful — Modal, Daytona, Northflank — let an agent run inference or fine-tuning inside the same sandbox that executes its tool calls. If your agent never touches a model directly, skip this filter entirely.
None of this is settled yet. The next year decides whether "agent sandbox" stays its own product category or just becomes a checkbox feature on every cloud you already pay for — and given how fast the open-source side (Daytona, OpenSandbox, agent-sandbox) is closing the gap on the funded players, self-hosting is a genuinely competitive option now, not just the budget one.
Lists of AI Sanboxex for AI Agents
PlatformModelIsolationCreationGPUOpen Source
E2B
Ephemeral + pause/resume
Firecracker microVM
~150ms
No
Partial
Daytona
Persistent
Docker (Kata opt-in)
~90ms
H100
Yes (72.5K★, AGPL)
Modal
Serverless
gVisor
Sub-second
T4–B200
No
Sprites
Persistent
Firecracker
1–2s
No
No
Blaxel
Hibernating
microVM
Sub-25ms resume
No
No
Vercel Sandbox
Persistent-default
Firecracker
Milliseconds
No
No
Cloudflare Sandbox SDK
Containers + isolates
Container/V8
~sec / ms
No
Partial
AWS AgentCore
Managed sessions (≤8h)
microVM
Not published
No
No
Google Agent Sandbox
Managed (preview)
Hardened containers
Sub-second
No
No
Northflank
Both
microVM/gVisor
~200ms
Yes (H100)
No
Runloop
Persistent
Custom hypervisor
~100ms
No
No
CodeSandbox SDK
Persistent
microVM
2.7s P95
No
No
OpenSandbox
Self-hosted
gVisor/Kata/Firecracker
Pool pre-warm
No
Yes (11.5K★)
Microsandbox
Local-first
libkrun microVM
~320ms
No
Yes (6.5K★)
agent-sandbox
Self-hosted
K8s-native
Not benchmarked
No
Yes
AIO Sandbox
All-in-one
Docker
~seconds
No
Yes (5K★)
ComputeSDK
Abstraction
Varies
Varies
Varies
Yes (212★)
Coder ai-sdk
Persistent
Varies
Varies
No
Partial
Quilt
Dormant
Namespaces
~200ms
No
Yes (dead)
Zeroboot
Stalled
Firecracker CoW
0.79ms
No
Yes (stalled)
More on the AI tooling landscape: the fight for AI sandboxes · what's actually worth building around frontier models · full blog index · monthly magazine digest
Cheers
Shrey

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