Key features
- Secure sandboxes: APAC isolated VM environments for LLM-generated code execution
- Fast startup: APAC 150ms sandbox initialization for low-latency agent workflows
- Python/JS: APAC full Python and Node.js runtime with pip and npm package support
- Persistent sessions: APAC stateful sandbox for multi-step agent code workflows
- SDK integration: APAC Python and TypeScript SDKs for agent application wiring
- File system: APAC read/write files within sandbox; upload/download data across runs
Best for
- APAC AI teams building agentic applications where LLMs generate and execute code — particularly APAC organizations with AI data analysis assistants, AI coding assistants, or automated ML pipeline runners where executing LLM output on production infrastructure would create unacceptable security exposure.
Limitations to know
- ! APAC sandbox lifetime limits — long-running computations may hit session timeout
- ! APAC data transfer in/out of sandboxes adds latency for large dataset workflows
- ! Usage-based pricing accumulates for APAC high-frequency agent code execution
About E2B
E2B is a cloud sandbox infrastructure platform providing APAC AI teams with secure, isolated execution environments for LLM-generated code — enabling AI coding assistants, data analysis agents, and automated ML pipelines to run arbitrary Python and Node.js without the security risks of executing LLM output on production infrastructure. APAC organizations building agentic AI applications where LLMs generate and execute code use E2B sandboxes as the safe execution layer.
E2B's sandboxes are ephemeral cloud VMs that spin up in 150ms — APAC AI agent applications create a sandbox, execute LLM-generated code, capture the output (stdout, stderr, files), and destroy the sandbox within seconds. Each sandbox is completely isolated: LLM-generated code cannot access the host infrastructure, other customers' sandboxes, or any network resource not explicitly allowed. APAC security-conscious organizations running code interpreter features in customer-facing AI applications use E2B to contain the execution blast radius of any malicious or broken LLM-generated code.
E2B's Python and JavaScript SDKs integrate directly with APAC LLM application code — an AI agent calls `sandbox.run_code(llm_generated_python)`, receives the output, and passes it back to the LLM for reasoning. APAC data science automation platforms use E2B as the execution layer for AI-driven analysis: the LLM generates pandas/matplotlib/sklearn code for APAC datasets, E2B executes it, and the result (chart images, dataframes, statistics) flows back into the AI conversation.
E2B's persistent sandbox sessions allow APAC AI agents to maintain state across multiple code execution steps — loading an APAC dataset in one step, transforming it in the next, and visualizing results in a third step, all within the same sandbox session. APAC AI-powered Jupyter alternatives and interactive data exploration tools use persistent sessions to give users a continuous coding environment driven by natural language instructions.
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