I Tested 5 AI Agent Frameworks — Here's Which One Actually Works

AI agents are the hottest thing in tech right now. Every framework promises autonomous AI that can browse the web, write code, and make decisions. I tested 5 of them on the same task: research a topic, find relevant APIs, and write a summary with working code examples. Here's what actually worked. The Contenders AutoGen (Microsoft) — multi-agent conversations CrewAI — role-based agent teams LangGraph — stateful agent graphs Phidata — production AI assistants Raw API calls — just Claude/GPT with function calling Results Framework Task Completed? Time Code Quality Setup Time AutoGen Partial 4 min Medium 30 min CrewAI Yes 3 min Good 15 min LangGraph Yes 5 min Good 45 min Phidata Yes 2 min Good 10 min Raw API Yes 1 min Best 5 min What I Learned 1. Raw API calls beat every framework for simple tasks If your agent needs to do one thing well (search, summarize, extract), just use the API directly: import anthropic client = anthropic . Anthropic () response = client . messages . create ( model