LangGraph vs CrewAI vs AutoGen: 107 Real Data Engineering Tasks at Scale
Subtitle: A code-first, no-nonsense teardown revealing which agent orchestration framework scales, where boilerplate becomes pain, and how cost/performance curves behave past 100+ real-world tasks.
Benchmarks Expose Framework Weaknesses
Framework docs push toy problems and happy-path APIs. Scale to 100+ tasks and those abstractions start leaking. Docs gloss over the chaos of orchestrating stateful LLM agents and the error handling thicket that follows. “Easy orchestration” or “robust error recovery” are hollow promises if your flow deadlocks on task 22 or your LLM bill jumps $48 overnight. Large, heterogeneous benchmarks show which frameworks survive—and which waste your time.
107 Real Data Engineering Tasks: The Testbed
