Harness 是目前 AI Agent 基础设施领域最具参考价值的架构框架之一。它将 Agent 的运行所需拆解为七大核心模块：从工具接入、编排协调，到记忆管理、安全防护、网络通信，清晰地勾勒出一个完整 Agent 系统的技术骨架。 围绕这七大模块，开源社区已经涌现出大量 ...

LangGraph has been used to create a multi-agent large language model (LLM) coding framework. This framework is designed to automate various software development tasks, including coding, testing, and ...

要做这样一个 AI 助手:能上网查资料、能读写文件、能记住过去的对话,还能在执行有风险的操作前先征询人类的意见。听起来是不是很复杂，其实并不是LangChain 生态里现成的几套工具,把开发时间压到了几个小时。不过这里就多了一个问题： create_agent、Deep Agents ...

What if you could build your own AI agent, one that operates entirely on your local machine, free from cloud dependencies and API costs? Imagine having complete control over your data, making sure ...

A new learning paradigm developed by University College London (UCL) and Huawei Noah’s Ark Lab enables large language model (LLM) agents to dynamically adapt to their environment without fine-tuning ...

Google has introduced Agent Executor, an open-source runtime standard for AI agent execution, resumption, and deployment.