sandbox-execution

You can isolate and execute AI-generated Python code in a sandboxed environment, ensuring file writes, dependencies, and network access remain separate from the host process. This prevents experiments and prototypes from disrupting your main runtime.

Dry running agent-generated code uses a sandbox environment to validate API snippets and prototypes safely. It supports dry-run, LangSmith, and E2B backends to mock sandbox calls in CI without affecting the live system.

Yes, E2B is supported as a backend selection for isolating agent workflows. It executes Python code in a strictly separated sandbox, enforcing quotas and managing stateful executions safely.

Sandbox code execution manages file transfers into the isolated environment and applies output truncation to prevent excessive data returns. This ensures analytical workflows receive manageable, safe results from the sandboxed runtime.

Stateful executions in a sandboxed environment are bound by quota enforcement and backend availability. While they maintain runtime state safely across calls, users must configure strict isolation controls to avoid unexpected resource limits.