Code Mode FAQs

Question 1

What execution environments does Code Mode support?

Accepted Answer

By default, Code Mode uses a Local Python Executor to ensure robust network I/O for MCP tool calls. A stricter Pyodide sandbox is also available, offering a more isolated environment, though it may have limitations with network-dependent operations.

Question 2

Can Code Mode integrate with other tools or services?

Accepted Answer

Yes, it features configuration-driven tool discovery via `mcp_servers.json`, allowing it to connect to and call any external Model Context Protocol (MCP) server. This enables AI agents to interact with a wide array of tools from within their generated Python code.

Question 3

How does Code Mode address common challenges in AI agent development?

Accepted Answer

It solves critical issues like context window bloat, high token costs, and cumulative latency by leveraging LLMs to write orchestrating Python code. Instead of multiple sequential tool calls, agents create a single script that manages all necessary operations.

Question 4

What security measures does Code Mode provide for code execution?

Accepted Answer

Code Mode enforces configurable guardrails, including strict execution limits (e.g., 8k characters / 400 lines), an allowlist for permitted Python imports, and disallowing potentially dangerous tokens like 'subprocess', 'exec', or 'eval' to prevent malicious activity.

Question 5

What is Code Mode?

Accepted Answer

Code Mode is a tool that enables AI agents to generate and execute Python code within a secure, sandboxed environment. It seamlessly integrates with external Model Context Protocol (MCP) tools, allowing AI agents to orchestrate complex workflows efficiently.

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