Build AI Agent Teams with CrewAI

CrewAI orchestrates multiple AI agents with distinct roles working together. Unlike single agents handling everything, CrewAI creates specialized agents (researcher, writer, reviewer) that collaborate. This mirrors human teams—each agent focuses on what it does best, with handoffs and coordination managed by the framework.

Use CrewAI when tasks benefit from specialization and collaboration: research requiring multiple perspectives, content needing writing and editing passes, or complex workflows with distinct phases. Simple chains work for linear tasks. CrewAI adds value when you'd naturally describe the solution as 'a team working together.'

Define: 1) Role (job title/function), 2) Goal (what they aim to achieve), 3) Backstory (expertise context), 4) Tools (capabilities they need). Good agents have focused roles—don't create 'general assistant' agents. Example: 'Senior Market Analyst' with web search and data analysis tools, goal of identifying market trends.

Sequential: tasks execute in order, each building on previous output. Hierarchical: manager agent delegates and coordinates worker agents. Consensual: agents discuss and agree on approach (experimental). Choose sequential for linear workflows, hierarchical for complex coordination requiring oversight.

Create tools using the @tool decorator or Tool class. Define function, description, and schema. Agents use tools based on need and context. Common tools: web search, code interpreter, API calls, file operations. LangChain tools integrate directly. Keep tools focused—one capability per tool.

CrewAI works with any LLM via LiteLLM: OpenAI (GPT-4, GPT-3.5), Anthropic (Claude), Azure OpenAI, local models (Ollama, vLLM), and more. Configure at agent level—use powerful models for complex reasoning, faster/cheaper models for simple tasks. Mix models within a crew for cost optimization.

Enable memory for context persistence across interactions. Use callbacks to checkpoint progress. For very long tasks, implement job queues (Celery, RQ) with crew execution as workers. Consider async execution and status monitoring. Break mega-tasks into smaller crew invocations with state management.

Enable verbose mode for step-by-step logging. Use callbacks to intercept agent thoughts and actions. Review task delegation in hierarchical processes. Check tool invocations and their outputs. Start with simple crews and add complexity incrementally. CrewAI's logs show the full execution trace.

Yes, via tools. Create tools that call APIs, query databases, send emails, or interact with any service. Agents decide when to use tools based on their goals. For safe execution, implement proper authentication, rate limiting, and input validation in your tools. Sandbox untrusted operations.

CrewAI supports short-term (within task), long-term (across executions), and entity memory (facts about people/things). Enable memory in crew config. Uses embeddings for semantic retrieval. Memory helps agents learn from past interactions and maintain context in ongoing workflows.

Containerize crews for consistent environments. Use queue-based architecture for scalability. Implement comprehensive error handling and retries. Monitor LLM costs per agent/task. Add human approval gates for critical actions. Version control crew definitions. Consider rate limiting and caching for API-heavy tools.

Costs depend on: number of agents, task complexity (tokens consumed), LLM choice, tool usage (API calls). GPT-4 can cost $5-50+ per complex crew run. Use GPT-3.5/Claude Haiku for simpler agents. Cache tool results. Monitor token usage in development. Estimate by running sample workloads and extrapolating.