How agentkernel Compares
The AI agent sandbox space is growing fast. Every major cloud provider now offers some form of isolated execution for AI-generated code. This page explains where agentkernel fits and how it differs from the alternatives.
The short version
Most sandboxes are cloud-hosted services -- you send code to someone else's infrastructure and pay per execution. agentkernel runs on your machine. Your code never leaves your network. No API keys, no vendor accounts, no per-execution billing. Just a single binary that gives each sandbox its own virtual machine.
At a glance
| agentkernel | E2B | Daytona | Docker | Cloudflare | Vercel | Modal | Deno | |
|---|---|---|---|---|---|---|---|---|
| Runs locally | yes | -- | -- | yes | -- | -- | -- | -- |
| VM isolation | yes | yes | yes | yes | -- | yes | -- | yes |
| Open source | yes | partial | yes | -- | -- | -- | -- | -- |
| Free / self-hosted | yes | -- | -- | yes | -- | -- | -- | -- |
| CLI tool | yes | -- | -- | yes | -- | yes | -- | -- |
| HTTP API | yes | yes | yes | -- | yes | yes | yes | yes |
| MCP server | yes | -- | -- | -- | -- | -- | -- | -- |
| SSE streaming | yes | yes | yes | -- | yes | -- | -- | -- |
| Agent compatibility modes | yes | -- | -- | yes | -- | -- | -- | -- |
| Multi-backend | yes | -- | -- | -- | -- | -- | -- | -- |
| File read/write in sandbox | yes | yes | yes | yes | yes | yes | yes | yes |
| Network policy controls | yes | yes | -- | -- | -- | -- | -- | yes |
| Security profiles | yes | -- | -- | -- | -- | -- | -- | -- |
Details
| agentkernel | E2B | Daytona | Docker | Cloudflare | Vercel | Modal | Deno | |
|---|---|---|---|---|---|---|---|---|
| Isolation | Firecracker microVM | Firecracker microVM | VM | microVM | Container | VM | Container | Firecracker microVM |
| Boot time | <1µs warm, ~220ms cold | <200ms | <90ms | -- | -- | -- | -- | <200ms |
| Pricing | Free | Pay-per-use | Pay-per-use | Free | Pay-per-use | Pay-per-use | Pay-per-use | Pay-per-use |
| Backends | Firecracker, Docker, Podman, Apple Containers, Hyperlight | Firecracker | Proprietary | Docker | Proprietary | Proprietary | Proprietary | Firecracker |
| SDKs | Python, Rust, Node.js | Python, JS, Go, Rust | Python, TS, Go | CLI only | TypeScript | TypeScript | Python | TypeScript |
| Agents | Claude, Codex, Gemini, OpenCode | Any | Any | Claude, Codex, Gemini | Any | Any | Any | Any |
| Runtimes | 12+ (auto-detected) | Python, JS, Ruby, C++ | Any | Any | Python, JS | Node.js, Python | Any | JS/TS, any Linux binary |
E2B
e2b.dev is the most established cloud sandbox for AI agents. It uses Firecracker microVMs (same as agentkernel), boots in under 200ms, and has strong SDK support across Python, JavaScript, Go, and Rust. Over 200 million sandboxes started.
When to use E2B: You want a managed cloud service, don't want to run infrastructure, and are building a SaaS product that needs sandboxed execution at scale.
When to use agentkernel instead: You want to run sandboxes locally on your own machine. You don't want to send code to a third-party cloud. You want to avoid per-execution costs. You need multiple backend options (Firecracker on Linux, Docker on macOS, Apple Containers on macOS 26+).
Daytona
daytona.io provides cloud sandbox infrastructure with sub-90ms boot times, Git integration, and LSP support. Open source with 50k+ GitHub stars. SDKs in Python, TypeScript, and Go.
When to use Daytona: You want a cloud-hosted sandbox with strong Git integration and IDE features. You're building tools that need persistent development environments with file sync.
When to use agentkernel instead: You want local execution without a cloud dependency. You want hardware-level VM isolation (Firecracker) rather than Daytona's isolation model. You need built-in agent compatibility modes for Claude Code, Codex, and Gemini.
Docker Sandboxes
Docker AI Sandboxes run lightweight microVMs with private Docker daemons on your local machine. Supports Claude Code, Codex, Gemini, and Docker's own agent (cagent).
When to use Docker Sandboxes: You already use Docker Desktop and want sandboxing integrated into that workflow. You need a private Docker daemon inside each sandbox.
When to use agentkernel instead: You want an independent tool that isn't tied to Docker Desktop. You want Firecracker microVMs on Linux for stronger isolation. You need an HTTP API and MCP server for programmatic access. You want to use Podman, Apple Containers, or Hyperlight as alternative backends.
Cloudflare Sandbox
Cloudflare Sandbox provides container-based sandboxes with a TypeScript SDK, preview URLs, and WebSocket support. Integrated with Cloudflare's edge network.
When to use Cloudflare: You're building on Cloudflare Workers and want sandboxes close to your edge infrastructure. You need automatic preview URLs for web applications.
When to use agentkernel instead: You want local execution. You want VM-level isolation instead of container isolation. You don't want to depend on Cloudflare's platform.
Vercel Sandbox
Vercel Sandbox provides ephemeral Linux VMs for running untrusted code on Vercel's infrastructure. TypeScript SDK with Node.js and Python runtimes. Integrated with Vercel's observability tools.
When to use Vercel: You're building on Vercel and want sandboxes integrated into your existing deployment pipeline. You need observability dashboards for sandbox usage.
When to use agentkernel instead: You want to run sandboxes locally. You want support for 12+ language runtimes with auto-detection. You don't want to depend on Vercel's platform.
Modal Sandboxes
Modal Sandboxes provide container-based isolated environments with a Python SDK. Strong in the ML community, with dynamic image definition at runtime and up to 24-hour session support.
When to use Modal: You're already using Modal for ML workloads and want sandboxes in that ecosystem. You need long-running sessions (up to 24 hours).
When to use agentkernel instead: You want local execution and VM-level isolation. You want a CLI tool, not a Python-only SDK. You want to avoid cloud dependency.
Deno Sandboxes
Deno Sandboxes use Firecracker microVMs with a JavaScript API. Under 200ms boot, full VM isolation, and network policy controls. TypeScript/JavaScript runs natively; any Linux binary is supported.
When to use Deno: You're building in the Deno ecosystem and want sandboxes integrated with Deno Deploy. You want a managed Firecracker service without running your own infrastructure.
When to use agentkernel instead: You want to run sandboxes locally. You want multi-language auto-detection beyond JavaScript. You want multiple backend options. You need agent-specific compatibility modes.
Other notable projects
Fly.io Machines -- Firecracker-based VMs with millisecond boot times. General-purpose compute, not specifically designed for AI agent sandboxing but usable as a sandbox backend.
Blacksmith Sandboxes -- Full VMs with Docker support, optimized for CI/CD and build performance. Currently in beta.
Rivet Sandbox Agent SDK -- Not a sandbox itself, but a universal API that normalizes interactions across multiple coding agents and sandbox providers (Daytona, E2B, Vercel, Docker). An abstraction layer rather than infrastructure.
agentkernel's position
agentkernel occupies a specific niche: local-first, VM-isolated sandboxing for AI coding agents.
Most alternatives are cloud services. They're the right choice if you're building a SaaS product that needs sandboxed execution at scale and you don't want to manage infrastructure. But they require sending your code to a third party, they charge per execution, and they introduce a network dependency.
agentkernel is for developers who want to run AI agents on their own machines with real isolation. Your code stays local. There's no account to create, no API key to manage, no bill at the end of the month. On Linux, you get Firecracker microVMs -- the same technology that powers E2B, Deno Sandboxes, and AWS Lambda. On macOS, you get Docker, Podman, or Apple Containers as fallback backends.
The trade-off is clear: you run the infrastructure yourself. For a single developer or small team running agents locally, that's a feature. For a platform serving thousands of users, a managed cloud service is the better fit.