I Ran 107 AI Agent Tasks on LangGraph, CrewAI & AutoGen. One Framework Won Everything.

A comprehensive benchmark comparing three major AI agent frameworks (LangGraph, CrewAI, and AutoGen) by running 107 tasks across 24 unique data engineering scenarios with identical prompts. The study evaluates performance, reliability, and effectiveness across different agent orchestration approaches. One framework demonstrated superior performance across the majority of test cases.

Key Points

• •Standardized testing methodology: 107 tasks across 24 unique data engineering scenarios ensure fair comparison across frameworks
• •Identical prompts used for all frameworks to isolate framework differences from prompt engineering variables
• •LangGraph, CrewAI, and AutoGen represent different architectural approaches to agent orchestration and task execution
• •Data engineering tasks provide practical, real-world use cases for evaluating agent framework capabilities
• •Performance metrics likely include task completion rate, accuracy, execution time, and reliability across different task types
• •Framework selection significantly impacts agent system reliability and effectiveness for production deployments
• •Benchmark results can guide developers in choosing the most suitable framework for their specific use cases
• •Testing at scale (107 tasks) provides statistical significance to performance comparisons

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