E2B

the agent execution layer

E2B solves the “how do I let my AI agent run code without destroying everything” problem. you give an LLM the ability to execute arbitrary Python or JavaScript, and eventually it will try something catastrophic—intentionally or accidentally. without isolation, that’s game over. E2B provides sandboxed Linux environments where agents can run wild safely.

co-founded by Sahil Chaudhary , E2B optimizes specifically for AI agent use cases. sandboxes boot in under 2 seconds. they support both ephemeral (one-shot execution) and persistent (stateful development) modes. the code interpreter SDK handles stdout/stderr capture, structured result parsing, file uploads/downloads, and killing hung processes.

the value proposition: instead of managing Docker swarms, implementing resource quotas, and handling security yourself (weeks of infrastructure work), you call an API and get a secure execution environment. agents can install packages, run scripts, access the internet, and crash without consequences. when done, the sandbox vanishes.

who uses it

E2B powers execution backends for devin , open interpreter , and various LangChain-based agents. any tool promising “AI that writes and runs code” likely uses E2B or built similar infrastructure themselves. the SDK integrates cleanly with langgraph , composio , and most agent frameworks.

developers building data analysis agents, coding assistants, or automation workflows get code execution capability in an afternoon. the Python/JS SDKs abstract complexity:

sandbox = await Sandbox.create()
execution = await sandbox.runCode('import pandas as pd')

that simplicity enabled adoption. compare to standing up secure sandboxing infrastructure yourself—VMs, networking, resource limits, monitoring. E2B collapsed activation energy from weeks to minutes.

the economics

E2B operates on usage-based pricing: pay for compute time consumed. free tier is generous enough for development and side projects. production pricing scales with execution volume. this aligns incentives—E2B succeeds when agents execute more code, which happens when AI agents become more useful.

the business bet: code execution becomes commodity infrastructure like payment processing. developers won’t build sandboxing any more than they build auth systems. they’ll call an API. E2B wants to be the Stripe of code execution.

competition comes from cloud giants (AWS, Azure, Google could build competing services) and self-hosted alternatives. E2B’s edge is specialization: optimizing exclusively for AI agents creates better DX and faster iteration than general-purpose compute platforms.

why it matters

E2B represents the infrastructure layer enabling autonomous agents. without safe code execution, agents are limited to generating text that humans copy-paste. with E2B, agents close the loop: generate code → run it → see results → iterate. that feedback loop is how auto-gpt and devin work.

the open-source SDK and self-hosted option lowered adoption friction. developers can run E2B locally for development, migrate to cloud for production. that try-before-you-buy model built trust and GitHub stars (7k+). the repository activity indicates real usage beyond hype.

Sahil Chaudhary positioned E2B as horizontal infrastructure, not a vertical product. it powers multiple agent platforms rather than competing for end users. that pick-and-shovel strategy captures value from the overall agent economy growth, regardless of which specific agent tools win.

the open questions

E2B’s trajectory depends on autonomous agents reaching production scale. if agents remain experimental, sandboxing stays niche. if agents become standard (every IDE, workflow tool, and data platform includes agentic features), E2B becomes essential infrastructure.

current adoption is developer-heavy: people building agent experiments, AI research projects, coding assistants. enterprise deployment is slower—sandboxing untrusted AI-generated code in production requires trust layers E2B is still building. observability, audit logs, compliance certifications—the boring enterprise requirements.

whether E2B captures the market or gets acquired by a cloud platform, Sahil Chaudhary proved sandboxing-as-a-service works. the company exists, has customers, and enables agent capabilities that couldn’t exist otherwise. that infrastructure contribution persists regardless of E2B’s corporate outcome.

→ related: sahil chaudhary | langgraph | composio