Feature: Documentation for Production Code Execution in Crews

Problem

CrewAI teams building production crews need documented patterns for:

• Agents executing generated code safely
• Local repository and environment access
• Production safety (sandboxing, resource limits, timeouts)

Currently this is undocumented, unlike other agent frameworks.

Today's Gap

Teams choose between:

1. No code execution — limits crew capabilities
2. Cloud sandboxes — external cost, context loss
3. Custom implementations — inconsistent, fragile

Comparison

• AutoGen — documents code_execution_config pattern
• MetaGPT — documents local executor patterns
• CrewAI — [no standard pattern documented]

Proposed Solution

Add to CrewAI documentation:

1. Code Execution Strategy Guide — cloud vs local tradeoffs
2. Production Patterns — sandboxing, resource limits, error handling
3. Example Crew — multi-agent code execution workflow
4. Best Practices — security, performance, monitoring

Why This Matters

This helps CrewAI teams build production crews that safely execute generated code, matching capabilities in competing frameworks.

Teams need official guidance to make code execution production-safe, not DIY solutions.

Reference

MCP servers and similar patterns demonstrate how to implement this safely and securely.

Start by locating the existing CrewAI documentation for agent execution, MCP servers, and production guidance, then review how competing frameworks describe code execution. The work is done when the documentation covers cloud versus local strategies, a production example crew, sandboxing, resource limits, error handling, security, performance, and monitoring.