Sahil Chaudhary

the sandbox problem

Sahil Chaudhary and his co-founder Vasek Mlejnsky launched E2B to solve a problem every AI agent builder hits: how do you let an LLM execute arbitrary code without destroying your infrastructure? the naive approach—run generated code directly on your server—works great until the agent runs rm -rf / or accidentally spawns a crypto miner. you need isolation.

E2B provides cloud sandboxes where AI agents can safely execute Python, JavaScript, and shell commands. each sandbox runs in a containerized environment with its own filesystem, network, and resource limits. agents can install packages, run scripts, access the internet, and crash spectacularly without affecting anything outside the sandbox. when the task completes, the environment vanishes.

this solves the “I want my agent to actually do things” problem. without sandboxing, coding agents are limited to generating text that humans copy-paste and run manually. with E2B, agents like devin , claude code , or custom LangChain applications can execute their own code, see results, and iterate. the feedback loop becomes autonomous.

why sandboxing is hard

spinning up isolated environments sounds straightforward—Docker exists, VMs exist, what’s the challenge? scale and latency. agents generate code continuously. if each execution requires 30 seconds to boot a container, the agent spends most of its time waiting. if environments aren’t properly isolated, one agent can leak state to another. if resource limits aren’t enforced, one runaway process consumes all available CPU.

E2B optimized for the AI agent use case specifically. sandboxes boot in under 2 seconds. they support both ephemeral (one-off execution) and persistent (stateful development) modes. the code interpreter SDK handles common patterns: capturing stdout/stderr, returning execution results as structured data, managing file uploads/downloads, killing hung processes.

Sahil’s team also built tooling for debugging and observability. when an agent-generated script fails, you need to see what went wrong—environment state, installed packages, network requests, resource usage. E2B’s dashboard shows execution logs, performance metrics, and error traces. this transparency helps developers understand why their agents succeed or fail.

the sdk approach

E2B isn’t just infrastructure—it’s developer experience. the Python and JavaScript SDKs abstract away orchestration complexity. you initialize a sandbox, run code, get results. four lines of code:

from e2b_code_interpreter import Sandbox

sandbox = await Sandbox.create()
execution = await sandbox.runCode('x = 1')
print(execution.text)  # outputs: 1

this simplicity enabled rapid adoption. developers building AI coding assistants, data analysis agents, or automation workflows could add code execution capability in an afternoon. compare that to managing your own Docker swarm, implementing resource quotas, and handling security—weeks of infrastructure work. E2B collapsed the activation energy.

the team also provides pre-configured environments for common use cases: data science (numpy, pandas, matplotlib pre-installed), web development (Node, npm, common frameworks), general compute. this reduces the “works on my machine but not in the agent” problem. if your agent generates code assuming certain libraries exist, you can start from an environment where they’re already available.

ecosystem integration

Sahil positioned E2B as infrastructure for the AI agent ecosystem, not a standalone product. the SDK integrates with langgraph , LangChain, composio , and most major agent frameworks. this “pick and shovel” strategy means E2B benefits from the overall growth of agentic AI rather than competing for end-user attention.

the open-source strategy helped. E2B’s core SDK and self-hosted option are available on GitHub. developers can run E2B locally for development and scale to the cloud for production. this lowers adoption friction—try it free, pay when you need scale/reliability. the repository has 7k+ stars, indicating real developer usage beyond hype metrics.

E2B also surfaces as the backend for several high-profile AI tools. open interpreter uses E2B for safe code execution. various Jupyter notebook alternatives and data analysis agents run on E2B infrastructure. Sahil built horizontal infrastructure that multiple verticals depend on—classic platform play.

the market timing

E2B launched in 2023, right as autonomous agents transitioned from research demos to production tools. auto-gpt had just gone viral, proving demand for agents that “do things” rather than just generate text. but most implementations were brittle—agents would break local environments, leak credentials, or get stuck in infinite loops.

Sahil saw the gap: everyone building agents needs sandboxing, but no one wants to build it themselves. it’s undifferentiated heavy lifting. E2B abstracted the complexity into an API call. that timing created winner-take-most dynamics. first mover advantage in developer infrastructure compounds—once teams integrate E2B, switching costs are high.

the company raised funding from Y Combinator and prominent AI investors. the pitch: every AI agent that executes code needs secure sandboxing. that’s potentially every coding assistant, data analysis tool, automation platform, and agent-building framework. the total addressable market is “the entire agent economy.”

why sahil matters

Sahil Chaudhary represents the infrastructure layer of the AI agent stack. while others built flashy demos or consumer products, he built the boring-but-essential plumbing. agents need to execute code safely. E2B makes that possible at scale. that’s not glamorous, but it’s valuable.

his approach also demonstrates how to build in emerging markets: solve a narrow technical problem extremely well, integrate with everything, and scale with the ecosystem. E2B didn’t try to be “the AI agent platform”—it focused on one critical component (sandboxing) and became the default choice for that component.

the impact is indirect but widespread. every devin deployment, every claude code cloud execution, every langgraph workflow that runs untrusted code—E2B is likely part of the stack. Sahil’s work is infrastructure in the literal sense: invisible when working, catastrophic when absent.

the long game

E2B’s bet is that code execution becomes a commodity API like payment processing or SMS. developers won’t build their own sandboxing any more than they build their own auth systems. they’ll call an API, get a secure environment, run code, get results. E2B wants to be the Stripe of code execution.

that requires getting economics right. sandboxes cost money to run (compute, networking, storage). pricing needs to make sense for both hobbyist developers building side projects and enterprises running millions of agent executions monthly. E2B offers generous free tiers to encourage adoption and usage-based pricing that scales with value delivered.

Sahil also has to navigate competition from cloud giants. AWS, Azure, and Google Cloud could all build competing sandbox services. E2B’s advantage is focus: they’re optimizing exclusively for the AI agent use case, not general-purpose compute. that specialization creates better DX and faster iteration than big-tech alternatives.

whether E2B becomes the dominant agent execution platform or gets acquired by a larger infrastructure player, Sahil moved the industry forward. he proved that sandboxing-as-a-service works, has real demand, and enables a category of AI applications that couldn’t exist otherwise. that contribution persists regardless of E2B’s corporate trajectory.

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