Scaling CrewAI for Production

Memory & Context

Enable memory for agents to learn from interactions:

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    memory=True,  # Enables short-term, long-term, and entity memory
)

Real-World Use Cases

Content Pipeline:

• Researcher finds topics → Analyst evaluates → Writer drafts → Editor reviews

Customer Support:

• Classifier routes tickets → Specialist resolves → QA checks response

Code Review:

• Scanner finds issues → Security checks vulnerabilities → Reviewer writes feedback

Cost Management

• Use cheaper models for simple agents (GPT-4o-mini for classifiers)
• Reserve powerful models for complex reasoning agents
• Set max_iter to prevent runaway API usage
• Monitor token usage per agent and task

Debugging Tips

• Enable verbose=True on agents and crews during development
• Log all tool calls and their results
• Track task completion times and token usage
• Use deterministic outputs (temperature=0) for testing

Limitations & Alternatives

• CrewAI is best for workflows with clear role separation
• For tighter agent coordination, consider LangGraph
• For simpler pipelines, function calling may suffice
• For autonomous agents, consider AutoGen or custom implementations