LangGraph, CrewAI, AutoGen Stress-Tested Across 107 Real Data Engineering Tasks

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A developer benchmark published on DEV Community evaluated three AI agent orchestration frameworks — LangGraph, CrewAI, and AutoGen — across 107 production-style data engineering tasks involving extraction, transformation, loading, and QA. Each task was I/O-bound, LLM-assisted, and required between one and six calls to GPT-4-turbo or Claude 3 Opus, with billing and performance logs drawn from June 2024. The analysis found that all three frameworks exhibited significant issues at scale, including LangGraph deadlocks from missing DAG edges, cryptic variable errors in deep CrewAI flows, and AutoGen silently resending full conversation histories to all agents, inflating token costs. Tasks exceeding three agents or long message threads consistently showed at least 30 percent cost bloat, undermining vendor claims of seamless orchestration and robust error recovery. The author concludes that only large-scale benchmarks with real billing data reveal how these frameworks truly perform beyond the simplified examples highlighted in official documentation.

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