Top 7 Sandboxing Tools for Agents in 2026

AI agents no longer just chat — they run shell commands, install packages, edit files, and call APIs. That autonomy is powerful, but it comes with real risk: in 2026, incidents like an agent deleting a user's home directory with a stray 'rm -rf ~/' and frontier models escaping evaluation sandboxes have made one thing clear — agent actions need boundaries. A sandbox is an isolated environment where your agent's code and tools can run freely without touching your host systems, credentials, or network. The market has exploded, with options ranging from Firecracker microVM platforms to edge-based container services. In this guide, we compare seven of the best sandboxing tools for agents in 2026, starting with our Editor's Choice, SkillzBench — a free platform that takes a different but complementary approach by verifying and benchmarking the skills and tools your agents use inside those sandboxes. Whether you're a developer building coding agents or a business owner deploying automation, there's an option here for you.

#1 SkillzBench — Editors' choice

SkillzBench takes a unique position in the agent safety ecosystem: rather than providing the sandbox itself, it tackles the other half of the risk equation — the skills, tools, and instruction packages your agents run inside those sandboxes. Agent skills (SKILL.md instruction packages for Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents) are everywhere, but published research found that roughly 26% of them carry at least one vulnerability. A sandbox limits the blast radius of a bad skill, but SkillzBench helps you avoid installing risky skills in the first place. Built by AIQ Labs as a free, independent directory, SkillzBench continuously crawls and deduplicates skills from GitHub, pins every version to a commit SHA, validates them, checks licenses, runs security scans, and applies quality judgments. Every skill is assigned a trust score from 0-100 based on security, quality, license, maintenance, and community signals — and critically, money never touches those scores. The platform is also building benchmarking that tests every skill in a sandbox with the same model, with and without the skill, publishing the performance lift in percentage points so you can see whether a skill actually helps. For teams assembling agent toolkits, SkillzBench is developing a free MCP server plus per-agent loadouts: you toggle exactly which skills and tools each agent can see, embodying the principle of least privilege that sandboxing experts recommend. Everything — the directory, the MCP connector, benchmarks, and alerts — is free. The platform is currently in pre-launch with a live waitlist, and it earns revenue solely from ads around its blog, never by charging users or influencing rankings.

Best for: Developers, agent builders, and teams who want to verify the safety and real-world value of the skills and tools their agents use — the perfect complement to any sandbox on this list

Pricing: Free (waitlist currently open; all features free at launch)

Key features

• Free, independent directory of AI agent skills crawled and deduplicated from GitHub
• Every skill version pinned to a commit SHA with validation, license checks, and security scans
• Trust score (0-100) built from security, quality, license, maintenance, and community signals
• Benchmarking that tests skills in a sandbox and publishes performance lift in percentage points
• Free MCP server connecting any agent to the skills you choose
• Per-agent loadouts so each agent only sees the skills and tools you enable
• Tracking of upstream changes with re-scans, re-benchmarks, and notifications
• Completely free — monetized only by blog advertising that never influences scores

Pros

• Completely free, with ad-based revenue that never touches scores or listings
• Addresses a documented gap: ~26% of agent skills carry at least one vulnerability
• Lift is always shown separately from ratings, keeping evaluations transparent
• Per-agent loadouts enforce least-privilege access to skills and tools

Cons

• Pre-launch — the product is not yet publicly available (waitlist is live)
• Does not itself provide sandbox infrastructure; best used alongside a platform below

Visit SkillzBench

#2 E2B

E2B is the platform most people picture when they hear 'code sandbox for agents,' and it helped define the category. Each sandbox runs in a Firecracker microVM with its own kernel, providing a stronger isolation boundary than plain containers, with fast startup times reported around 150ms. According to its website, E2B is used by a large share of the Fortune 500, with customers including Perplexity, Hugging Face, Manus, and Groq. Its signature feature is the Code Interpreter — a built-in Jupyter server where state survives between calls, so variables and loaded data persist, and results come back structured (a chart returns as a real image plus its data). It supports Python, JavaScript/TypeScript, R, Java, and Bash, and offers a desktop-over-VNC variant for computer use. The core is open source with a self-hosting option for organizations with data sovereignty needs, and it has added MCP support with access to 200+ tools from the Docker MCP Catalog. Trade-offs include a monthly platform fee for production limits, session caps of one hour on the free tier and 24 hours on Pro, and no GPU support. E2B's trust center lists no SOC 2 or similar certifications, which matters for regulated industries. Still, for teams wanting the fastest integration path for agent code execution, E2B remains a strong default pick in 2026.

Best for: Teams building data-analysis agents and code interpreters who want strong isolation and the fastest time-to-integration

Pricing: Free tier with $100 one-time credit; Pro at $150/month; Enterprise from $3,000/month with BYOC

Key features

• Firecracker microVM isolation with dedicated kernel per sandbox
• Built-in Jupyter-based Code Interpreter with persistent state between calls
• Support for Python, JS/TS, R, Java, and Bash
• Desktop-over-VNC variant for computer use
• Open-source core with self-hosting and BYOC options
• MCP support with 200+ tools from the Docker MCP Catalog
• Auto-pause option that preserves paused sandboxes indefinitely

Pros

• Excellent developer experience with clean Python and TypeScript SDKs
• Hardware-level Firecracker microVM isolation
• Open source with self-hosting flexibility

Cons

• No GPU support
• Session caps (1 hour free, 24 hours Pro)
• No listed SOC 2, ISO, or HIPAA certifications

Visit E2B

#3 Daytona

Daytona provides isolated, programmatically managed sandboxes that function as full, composable computers for AI agents. Developers can control sandbox lifecycles, files, Git operations, processes, code execution, terminal sessions, and computer-use capabilities through its APIs, CLI, and SDKs. The platform advertises sub-90ms cold starts and pairs that speed with unlimited session length, making it well suited to interactive agent workflows. Daytona's execution surface is broad: SDKs in five languages (Python, TypeScript, Go, Ruby, Java), a stateful Python interpreter, a VNC desktop, an MCP server, shared volumes, and GPU sandboxes. It supports both Windows and GPU sandboxes, and customers can choose between microVM- and Docker-based isolation models. Sandboxes can be created from snapshots containing an OS, installed packages, and configuration, so agents begin from a prepared environment rather than reinstalling tooling repeatedly. Persistent volumes exist independently of individual sandboxes and can be mounted by multiple environments. Its documentation includes integration guides for major agent frameworks including the OpenAI Agents SDK, LangChain, LangGraph, and Vercel AI SDK. Billing is pay-as-you-go on vCPU and memory by the second with no platform fee, plus $200 in starting credits. The trade-offs: you bring your own agent loop, the feature breadth creates a learning curve, and the platform has been closed-source since June 2026.

Best for: Teams building stateful coding agents who need fast starts, multi-language SDKs, or GPUs without a platform fee

Pricing: Pay-as-you-go by the second, no platform fee, with $200 in starting credits

Key features

• Sub-90ms cold starts with unlimited session length
• SDKs in five languages: Python, TypeScript, Go, Ruby, Java
• MicroVM- and Docker-based isolation options plus Windows and GPU sandboxes
• Snapshot-based environment creation with pre-installed packages
• Persistent volumes shareable across multiple sandboxes
• Stateful Python interpreter and VNC desktop for computer use
• Integrations with OpenAI Agents SDK, LangChain, LangGraph, and more

Pros

• Among the fastest advertised cold starts in the category
• Broadest language and runtime flexibility, including GPU and Windows sandboxes
• Usage-only billing with no subscription floor

Cons

• No agent included — you build and bring your own loop
• Closed-source since June 2026
• Feature breadth means a steeper learning curve

Visit Daytona

#4 Modal Sandboxes

Modal came from the serverless-ML world and added sandboxes on top of its broader AI infrastructure platform. Its standout capability is GPU access: everything up to H100, H200, and B200 on demand, with no reservations or quotas, alongside T4, L4, A10, L40S, and A100 variants. For agents whose work is GPU-bound — model inference, fine-tuning, or heavy data processing — Modal lets code execution and accelerated compute live side by side. Modal uses gVisor container isolation for its sandboxes, with VM Sandboxes in beta. Sandboxes can be dynamically defined at runtime via its code-first SDK, enabling task-specific environments created programmatically. The platform supports 50,000+ concurrent sessions and is production-proven: customers like Lovable and Quora run millions of untrusted code snippets daily, and Modal serves over 10,000 teams including Ramp and Suno. Memory snapshots (currently alpha) can reduce initialization-heavy startup times, and built-in networking tunnels with connection tokens enable authenticated sandbox access. Modal is SOC 2 Type II compliant and supports HIPAA-compliant workloads on Enterprise plans via a BAA. SDKs cover Python, JavaScript/TypeScript, and Go. The catches: sandboxes run at a premium over base Modal functions, effective GPU rates sit higher than dedicated GPU providers, Node.js support is limited, and there is no self-hosting or BYOC option.

Best for: Python-heavy teams building agents that need GPU acceleration for ML inference, fine-tuning, or compute-intensive analysis

Pricing: Usage-based, billed per second (CPU from ~$0.119/vCPU-hour); Team plan at $250/month

Key features

• Broad GPU lineup including H100, H200, and B200 with no reservations or quotas
• gVisor container isolation with VM Sandboxes in beta
• Dynamically defined sandbox environments via code-first SDK
• 50,000+ concurrent sessions with full observability
• Memory snapshots (alpha) to reduce startup times
• Built-in networking tunnels and per-sandbox egress policies
• SOC 2 Type II compliance with HIPAA support on Enterprise plans

Pros

• Unmatched GPU support alongside sandboxing
• Massive scale with production-proven reliability
• Scales to zero when idle

Cons

• Premium pricing over base Modal functions and dedicated GPU providers
• Limited Node.js support; Python-first orientation
• No self-hosting or BYOC option

Visit Modal Sandboxes

#5 Blaxel

Blaxel positions itself as the perpetual sandbox platform for production AI agents. Its defining feature is indefinite standby at zero compute cost: sandboxes hibernate after 15 seconds of network inactivity and resume in under 25ms with filesystem, memory, and running processes intact. For latency-sensitive production agents, that resume speed is a meaningful differentiator — an order of magnitude faster than pause-based competitors. The platform runs each workload in its own kernel via microVM isolation, closing the escape paths that shared-kernel runtimes carry. Blaxel also provides batch jobs for parallel asynchronous work, Agent Drive (a distributed filesystem in private preview that mounts to multiple sandboxes with concurrent read-write access), durable block-storage volumes, and networking features including custom domains, proxy secrets injection, domain filtering, and preview URLs. A Model Gateway provides unified LLM access, telemetry, and token cost control. Notably, Blaxel is a first-class sandbox provider in the OpenAI Agents SDK, named alongside Cloudflare, Daytona, E2B, Modal, Runloop, and Vercel. On compliance, Blaxel holds SOC 2 Type II and ISO 27001 certifications and supports HIPAA via a BAA — a strong fit for enterprise procurement. SDKs are available for Python, TypeScript, and Go. Limitations: Blaxel is CPU-focused with no documented GPU support, has fewer SDK languages than some competitors, and is not open source.

Best for: Latency-sensitive production agents — coding agents, PR review agents, and data analysis agents — at AI-first companies

Pricing: Per-second billing with free credits to start; contact for detailed pricing

Key features

• Perpetual standby at zero compute cost with under-25ms resume
• MicroVM isolation with a dedicated kernel per workload
• Agent Drive distributed filesystem for shared agent storage
• Proxy secrets injection keeping credentials out of sandboxes
• SOC 2 Type II, ISO 27001, and HIPAA BAA compliance
• First-class sandbox provider in the OpenAI Agents SDK
• Batch jobs for parallel, asynchronous agent work

Pros

• Fastest advertised resume time in the category (under 25ms)
• Strong compliance certifications for enterprise buyers
• Secrets stay outside the sandbox via proxy injection

Cons

• No documented GPU support
• Not open source
• Fewer SDK languages than some competitors

Visit Blaxel

#6 Northflank

Northflank provides a full application platform with flexible sandbox capabilities, positioning itself as a complete cloud infrastructure solution rather than a sandbox-only tool. For AI agents, it offers isolated environments for code execution, APIs, and browser automation, with fast-starting sandboxes, concurrent execution, and CPU or GPU resources. Northflank Cloud uses microVMs (via Kata Containers and Cloud Hypervisor) for CPU sandboxes and gVisor for GPU sandboxes, targeting sub-second boot times and supporting thousands of concurrent sessions. A key differentiator is bring-your-your-own-cloud (BYOC): Northflank acts as the control plane while sandbox workloads run inside your own cloud account and VPC on AWS, GCP, Azure, Oracle, or bare metal — valuable for organizations requiring execution within infrastructure they control. Persistence options include attached volumes up to 64 TB with multi-read-write support, S3-compatible object storage, and container filesystem snapshots for supported Kata workloads. Sandboxes have no fixed session time limit. Beyond sandboxes, Northflank includes managed databases, API hosting, background workers, and CI/CD, which benefits teams needing comprehensive infrastructure. It maintains SOC 2 Type 2 certification with enterprise features including SSO, audit logs, and HIPAA compliance with BAAs under Enterprise contracts. Pricing is transparent: $0.01667/vCPU-hour and $0.00833/GB-hour, with H100 GPU plans at $2.74/hour all-inclusive, plus a free developer tier.

Best for: Teams that want sandboxing within a complete platform, especially those requiring deployment in their own cloud (BYOC)

Pricing: Free tier; pay-as-you-go at $0.01667/vCPU-hour and $0.00833/GB-hour; H100 GPUs at $2.74/hour; Enterprise custom pricing

Key features

• MicroVM (Kata) isolation for CPU sandboxes and gVisor for GPU sandboxes
• BYOC deployment across AWS, GCP, Azure, Oracle, or bare metal
• Persistent volumes up to 64 TB with multi-read-write support
• No fixed session time limit on sandboxes
• Full platform scope: databases, APIs, workers, and CI/CD alongside sandboxes
• SOC 2 Type 2 and HIPAA compliance with SSO and audit logs
• Transparent per-second pricing with a free developer tier

Pros

• Genuine BYOC with control-plane management in your own VPC
• No session time caps and strong persistence options
• Full infrastructure platform beyond just sandboxes

Cons

• GPU types and capacity depend on region
• More complex than sandbox-only tools for simple use cases
• Sandbox isolation on BYOC requires a supported runtime on your infrastructure

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#7 Cloudflare Sandboxes

Cloudflare's Sandbox SDK runs isolated containers right next to Workers, making it the natural choice for teams already building on the Cloudflare ecosystem. If your agent lives in Workers, Durable Objects, and Workers AI, code execution is one binding away — no separate vendor or integration layer required. Each sandbox runs in its own VM on Cloudflare Containers, with cold starts reported in the 1-3 second range. The SDK handles commands, files, background processes, and exposed ports, and ties into Cloudflare's newer primitives for long-running, durable agents. Cloudflare also backs a managed-agent offering built with Anthropic, signaling serious investment in the agent space. Pricing is usage-based on active CPU billed per 10ms, available with the Workers Paid plan at $5/month — one of the most accessible entry points in the category. On persistence, Cloudflare's durable sandbox identity does not preserve a stopped container's filesystem by default; developers should use bucket mounts or the backup/restore APIs for workspace restoration. Cloudflare's organization holds SOC 2 and ISO certifications, though the company notes that sandbox-scope certification specifics should be verified directly. The trade-offs: cold starts are slower than microVM specialists like Blaxel or Daytona, state management requires explicit handling, and the SDK remains in preview. For Cloudflare-native teams, however, the ecosystem convenience is hard to beat.

Best for: Teams already building on Cloudflare who want code execution one binding away from their existing agent infrastructure

Pricing: Active CPU billed per 10ms; requires Workers Paid plan at $5/month

Key features

• Sandbox SDK integrated directly with Workers and Durable Objects
• Per-sandbox VM isolation on Cloudflare Containers
• Support for commands, files, background processes, and exposed ports
• Ties into Cloudflare primitives for long-running, durable agents
• Global edge placement
• Usage-based pricing on active CPU per 10ms
• Backup/restore APIs and bucket mounts for state management

Pros

• Seamless integration with the Workers ecosystem
• Very low-cost entry point ($5/month plan)
• Global edge deployment and durable agent primitives

Cons

• 1-3 second cold starts, slower than microVM specialists
• Stopped containers don't preserve files by default — state needs explicit handling
• SDK remains in preview (1.0)

Visit Cloudflare Sandboxes

Conclusion

Choosing a sandbox for your agents in 2026 comes down to your workload: E2B for fast integration and code interpreters, Daytona for speed and language flexibility, Modal for GPU-heavy ML work, Blaxel for latency-sensitive production agents, Northflank for BYOC control, and Cloudflare for edge-native teams. But remember — a sandbox limits the blast radius of what your agent runs; it doesn't tell you whether the skills and tools you've installed are safe or effective in the first place. That's where SkillzBench comes in: a free, independent directory that security-scans, license-checks, and benchmarks agent skills, with per-agent loadouts that enforce least-privilege access. Sandbox your agents' execution, and verify what goes into it. Join the SkillzBench waitlist today to be first in line when the platform launches — and start building agents that are both capable and safe.

Frequently asked questions

What is an AI agent sandbox?

An AI agent sandbox is an isolated execution environment where agents run code, shell commands, and tools with restricted access to your host systems, files, network, and credentials. The agent can still do real work — install packages, run tests, edit files — but mistakes or malicious actions stay contained inside the boundary. In 2026, the default for untrusted code is a microVM with its own kernel, which provides stronger isolation than shared-kernel containers.

Why do agents need sandboxes at all?

Agents take real actions with real permissions and can be wrong or manipulated at machine speed. Real incidents in 2025 and 2026 include an agent deleting a user's entire home directory with a stray 'rm -rf ~/' command, prompt-injection attacks exfiltrating npm tokens, and agents attempting cryptomining. Security researchers describe the 'lethal trifecta': when an agent combines access to sensitive data, exposure to untrusted content, and external communication, it becomes exploitable. Sandboxes shrink what the agent can touch and where it can send things, making even a successful attack survivable.

What makes SkillzBench different from the sandbox platforms on this list?

SkillzBench doesn't provide sandbox infrastructure — it complements it. While sandboxes contain what your agent executes, SkillzBench addresses what goes into your agent: the skills and tools it uses. It's building a free directory that security-scans, license-checks, and benchmarks agent skills (SKILL.md packages), assigns trust scores from 0-100, and offers a free MCP server with per-agent loadouts so each agent only sees the skills you enable. Everything is free, funded by blog advertising that never influences scores. It's currently pre-launch with a live waitlist.

How much do agent sandboxes cost?

Pricing varies widely. Cloudflare Sandboxes start at $5/month (Workers Paid) plus usage-based active CPU billing. E2B offers a free tier with $100 credit, Pro at $150/month, and Enterprise from $3,000/month. Daytona and Modal bill per second with no platform fee (Daytona includes $200 in starting credits). Northflank charges $0.01667/vCPU-hour with a free developer tier. Blaxel uses per-second billing with free credits. SkillzBench is completely free.

Which sandbox is best for coding agents?

For coding agents, consider what you need most: E2B offers the fastest integration path with clean SDKs; Daytona provides sub-90ms starts, unlimited sessions, and five SDK languages; Blaxel offers the fastest resume (under 25ms) for latency-sensitive production agents; and Northflank offers persistent volumes and no session caps for long-running work. Teams that want the agent itself running inside the sandbox should also evaluate platforms designed for that pattern. Test your actual workload before committing — vendor startup numbers aren't matched benchmarks.

Are microVMs really more secure than containers?

Generally, yes, for untrusted code. Containers share the host kernel, so a kernel bug can potentially let code escape — a risk called container escape. MicroVMs (like Firecracker) give each workload its own kernel, creating a hardware-level boundary. gVisor offers a middle ground by intercepting system calls through a user-space kernel, reducing but not eliminating kernel attack surface. That said, isolation alone isn't the whole story: network egress policies, scoped credentials, and secrets handling matter just as much, and a microVM label doesn't guarantee a complete security policy.

How do I know if the skills and tools my agent uses are safe?

Published research found that roughly 26% of agent skills carry at least one vulnerability, and sandboxing alone doesn't verify whether a skill is trustworthy or even effective. Look for tools that pin versions to commit SHAs, run security and license scans, and publish benchmark results. SkillzBench is building exactly this: a free directory with trust scores from 0-100 based on security, quality, license, maintenance, and community signals, plus sandbox-based benchmarking that shows each skill's performance lift. Join the waitlist at skillzbench.com to get access when it launches.