E2B Sandbox

Description

Cloud-based code interpreter sandboxes for AI agents. Run AI-generated code safely in isolated environments with JavaScript/TypeScript and Python SDK support.

AI-native score

How ready this product is for agents to use, on a 0–100 scale across four weighted dimensions. Security is one of them, so a high score is not a substitute for your own security or vendor-risk review. How is this scored?

Unrated. Only 31% of the scored decision fields are researched, so the grade is held until more are confirmed. The breakdown below shows what's been scored.

Trust & data policy

Trust center

Vendor trust center

Sandbox environment

✓Yes

Standard protocol access

✓Yes

Open source

✓Yes

Source code

https://github.com/e2b-dev/E2B

Not yet researched Multi-factor authentication, Zero data retention, No generative AI training, Hard budget caps, Data export, Data import, Open standards, No proprietary-SDK lock-in, Documented provider swap, Self-hostable

Security & compliance

Certifications and access controls this product reports on its own pages. We confirm the vendor lists each one; for certifications we don't audit the underlying report. Part of the 15% security score.

A shared trust or security page can support several certifications. We do not require separate public report links because reports may require login, vendor approval, or a nondisclosure agreement. Ask the vendor for reports and applicable coverage before a procurement decision. These listings expose no per-claim check date; the entry's updated date does not date the supporting evidence.

Compliance certifications

Not yet researched Access controls

What it does

Jobs to be done

From cited vendor documentation, each completion verdict says whether an agent can deliver the job's main outcome by and large through this product and which steps still need a person. Scope may limit cases or prerequisites while preserving that outcome. It's a separate axis from the AI-native score. Expand a job for the human steps, delegation, and scope. An unknown verdict means the answer is not yet established.

• Build AI agents ✓Finishes end to end

  Develop applications in which language models use tools and context to carry out tasks.

  Completion detail · via SDK / developer kit

  Setup needs a human

  Unknown

  Per-task human step

  None

  On behalf of a principal

  Supported

  A developer or coding agent builds an LLM application that calls a Python-execution tool in E2B Sandbox and feeds the execution result back to the model to answer its principal's request. The documented function-calling example provides the tool definition, sandbox execution and final model response after SDK and credential setup.

  Conditions: Requires E2B and model-provider credentials, the E2B code-interpreter SDK, a supported model client and caller-supplied application code. This completion covers the documented code-execution agent application; other tools and application deployment require additional implementation. Setup human requirement remains unknown.

  View 6 products

Integration surfaces

How an agent connects to this product and finds its documentation. The largest dimension of the AI-native score (35%).

llms.txt

https://e2b.dev/llms.txt

Command-line interfaces

• E2B CLI

Developer kits

• E2B SDKs

  Languages: JavaScript , Python , TypeScript

Not yet researched WebMCP, Headless signup, MCP servers, Agent2Agent (A2A) servers, Native connectors, API specs, Agent skills

Reviews

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Catalog activity

Track how this entry changes as proposals are opened and merged.

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