📄 中文摘要
在AI代理框架中，生产就绪的声明常常缺乏对实际成本的透明度，包括CPU、RAM和延迟等关键指标。为此，开发团队构建了AutoAgents，这是一个基于Rust的框架，旨在创建能够使用工具的AI代理。通过与其他流行框架如LangChain、LangGraph、LlamaIndex和PydanticAI进行基准测试，评估其在实际应用中的性能表现。该测试旨在提供真实的性能数据，以帮助开发者在选择框架时做出更明智的决策。
2026年AI代理框架基准测试：AutoAgents（Rust）与LangChain、LangGraph、LlamaIndex、PydanticAI等
📄 English Summary
Benchmarking AI Agent Frameworks in 2026: AutoAgents (Rust) vs LangChain, LangGraph, LlamaIndex, PydanticAI, and more
In the realm of AI agent frameworks, claims of being production-ready often lack transparency regarding actual costs in terms of CPU, RAM, and latency. To address this, a team developed AutoAgents, a Rust-native framework for building tool-using AI agents. By benchmarking it against other popular frameworks such as LangChain, LangGraph, LlamaIndex, and PydanticAI, the performance of AutoAgents in real-world applications was evaluated. This benchmarking aims to provide genuine performance data to assist developers in making informed decisions when selecting a framework.