E2B API

Provides secure, long-lived cloud sandbox environments for AI agents to safely execute arbitrary code, run processes, and interact with filesystems in isolated containers.

Evaluated Mar 07, 2026 (0d ago) vcurrent
Homepage ↗ Repo ↗ Developer Tools sandbox code-execution ai-agent python node secure-runtime mcp interpreter
⚙ Agent Friendliness
85
/ 100
Can an agent use this?
🔒 Security
83
/ 100
Is it safe for agents?
⚡ Reliability
83
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
83
Documentation
90
Error Messages
85
Auth Simplicity
88
Rate Limits
82

🔒 Security

TLS Enforcement
100
Auth Strength
80
Scope Granularity
65
Dep. Hygiene
87
Secret Handling
85

Sandboxes run in gVisor-based isolated containers providing strong host isolation. API key has no scope controls. Agents should never pass host-system credentials into sandbox environments.

⚡ Reliability

Uptime/SLA
83
Version Stability
85
Breaking Changes
82
Error Recovery
83
AF Security Reliability

Best When

Your AI agent needs to safely execute untrusted or LLM-generated code in an isolated environment with fast cold start and Python/Node SDK integration.

Avoid When

Your code execution requirements need GPU compute, extended multi-hour runtimes, or access to private network resources from within the sandbox.

Use Cases

  • Execute LLM-generated Python or JavaScript code in an isolated sandbox to implement a safe code interpreter agent tool
  • Run data analysis scripts produced by an agent against uploaded datasets without risking host environment contamination
  • Build an AI coding assistant that can iteratively write, run, and debug code inside a persistent sandbox session
  • Execute shell commands and install packages dynamically during agent task execution without infrastructure provisioning
  • Test and validate agent-generated code snippets in a throwaway environment before promoting to production pipelines

Not For

  • Long-running persistent compute workloads measured in hours where dedicated VMs or containers are more cost-effective
  • Execution environments requiring GPU access for ML model inference within the sandbox
  • Scenarios requiring network egress to arbitrary internal corporate services from within the sandbox

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
Yes
SDK
Yes
Webhooks
No

Authentication

Methods: api_key
OAuth: No Scopes: No

API key passed as E2B_API_KEY environment variable when using SDKs, or as Authorization header for REST calls. No scope granularity; single key controls all sandbox operations.

Pricing

Model: usage_based
Free tier: Yes
Requires CC: No

Billed per sandbox compute-hour regardless of actual CPU usage. Idle sandboxes still consume quota. Auto-suspend after inactivity on free tier to prevent runaway costs.

Agent Metadata

Pagination
none
Idempotent
Partial
Retry Guidance
Documented

Known Gotchas

  • Sandboxes have a default timeout (5 minutes on free tier); agents running long tasks must explicitly extend the sandbox lifetime using the keep_alive method or the sandbox will be killed mid-execution
  • File writes inside a sandbox are ephemeral by default; agents that need to persist output must explicitly copy files out via the download API before the sandbox is closed
  • The sandbox has limited network egress; agents should not assume internet access from within the sandbox is unrestricted — some outbound connections may be blocked depending on plan
  • Code execution is not atomic; if an agent sends a multi-step script and the sandbox times out halfway through, partial state changes will have occurred with no rollback mechanism
  • The MCP server implementation covers a subset of SDK functionality; agents integrated via MCP may not have access to all filesystem and process management features available in the Python/Node SDKs

Alternatives

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Scores are editorial opinions as of 2026-03-07.

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