Every agent framework that lets an LLM execute code eventually runs into the same problem: how do you isolate that execution safely, and how fast can you spin up a fresh sandbox when you need to run dozens or hundreds of agent attempts in parallel? A new open-source project called forkd is tackling that problem head-on, and the headline number is hard to ignore — 100 isolated microVM sandboxes, forked in roughly 100 milliseconds.
Fork(), But for Whole VMs
forkd describes itself plainly: “Fork 100 microVMs in 101 ms. BRANCH a live VM in 56 ms (v0.4 live mode).” It’s built as “a microVM sandbox runtime for AI agent fan-out,” where children fork from a warmed parent snapshot and inherit its address space copy-on-write, instead of each sandbox cold-booting its own kernel from scratch.
The mechanics are built directly on Firecracker, the KVM-based virtualization technology AWS open-sourced for Lambda and Fargate. Here’s how forkd uses it: a parent VM boots exactly once, imports whatever runtime environment you need — Python with all your dependencies already installed, a JIT-warmed JVM, or an ML model that’s already loaded into memory — and then pauses itself to disk. From that single warmed snapshot, every subsequent child is a separate Firecracker process that memory-maps the paused parent’s state and resumes almost instantly, rather than repeating the expensive cold-boot sequence (loading a kernel, initializing a runtime, importing dependencies) for every single sandbox.
That copy-on-write approach is the entire performance story. The project’s own benchmarks claim roughly 101ms to spin up 100 sandboxes this way, compared to about 759ms for a single raw Firecracker cold boot — meaning forkd isn’t just faster per-sandbox, it’s dramatically faster in aggregate because the expensive setup work happens exactly once and gets amortized across every fork.
Live Branching for Agent Rollouts
Version 0.4 adds a feature that’s particularly relevant for agentic workflows: BRANCH, which lets you fork a live, already-running VM mid-execution in about 56 milliseconds, rather than only forking from a cold, paused parent snapshot. For agent rollout scenarios — where you want to explore multiple continuations from a specific point in an agent’s execution, similar in spirit to Monte Carlo tree search over an agent’s action space — that’s a meaningfully different capability than only being able to fork from a pre-warmed template. You can capture the exact live state of an agent mid-task and branch multiple parallel continuations from that precise moment, which opens up techniques like best-of-N sampling over live agent execution rather than just over independent, freshly-booted attempts.
A Drop-In for E2B
Perhaps the most practically important design decision is that forkd is built to be drop-in compatible with E2B’s Python SDK. E2B has become one of the more popular managed sandboxing services for AI agent code execution, and by matching its SDK surface, forkd gives teams already using E2B a path to self-host equivalent (or, per the benchmarks, faster) sandboxing infrastructure without rewriting their integration code. Community reaction on X has been blunt about the implication, with multiple posts describing forkd as an “E2B killer” for teams that want the performance and control of self-hosted infrastructure without giving up the developer experience E2B popularized.
It’s worth flagging clearly, per forkd’s own documentation: the specific benchmark numbers — 101ms for 100 sandboxes, 56ms for live branching, the 759ms cold-boot comparison — are self-reported by the project itself. They haven’t been independently reproduced in third-party benchmarks as of this writing, so treat them as a strong directional claim from the people who built the thing, not an audited industry benchmark. That said, the underlying mechanism — copy-on-write forking from a warmed Firecracker snapshot instead of cold-booting — is a well-understood technique, so the general performance direction (fork is much faster than cold boot) is architecturally plausible even before independent verification.
forkd is licensed under Apache-2.0, ships as a PyPI package (pip install forkd), and is under active CI with regular releases, per its GitHub repository badges.
Why This Matters
Sandbox spin-up latency is one of those unglamorous infrastructure details that quietly determines whether certain agent architectures are practical at all. If you want an agent to try 50 different approaches to a coding task in parallel and pick the best result, or run a swarm of sub-agents each in their own isolated environment, the cost of creating each of those environments matters enormously at scale. A tool that turns “spin up 100 isolated VMs” from a multi-second operation into a hundred-millisecond one changes what kinds of parallel agent workflows are economically and practically viable to run, especially for teams self-hosting rather than paying per-sandbox to a managed provider.
Sources
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260813-0800
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