One billion sandboxes created on E2B.
Every one of them powered an AI agent solving a real-world problem.
From early-stage startups to cutting-edge AI labs to the Fortune 100, E2B is the infrastructure behind some of the most widely used agents in production, and the RL
DX matters.
Sandboxes should be infrastructure you barely have to think about: fast to spin up, simple to integrate, and ready for agents from day one.
Appreciate the shoutout @pelaseyed 🫡
Give @Meta's Muse Code an @e2b sandbox and its native video understanding yields an efficient parallel video-processing pipeline.
We handed the same demo recording to Muse, Claude Code, and Codex, each in its own E2B sandbox, and asked for a step-by-step breakdown. Muse Spark
We’re introducing Box Mount.
Mount a Box folder into a sandbox like @e2b and keep it in two-way sync. The agent reads and writes Box content with standard file operations as if they were local. Every operation flows through the Box API so governance applies automatically, no
Paper Instruments benchmarked sandbox providers for their RL rollouts, and E2B's tool execution came in up to 3x faster than every other sandbox provider they tested, reducing idle GPU time and training cost.
@paperinstr trains frontier models for knowledge work (consulting,
