做智能体自动化和系统优化的开发者，LangChain 这套循环体系从基础到元循环都覆盖了，直接拿来用就能提升迭代效率，值得点开看看。
LangChain 团队展示了如何用 LangChain 和 DeepAgents 构建多种循环结构，从基础的 Token 循环到复杂的元循环。Token 循环由模型支持，create_agent 提供智能体循环（模型+工具+重复直到完成），DeepAgents 引入自我验证循环（智能体循环+验证+重复直到满意），部署则实现元循环（根据事件触发智能体运行以改进系统）。团队还提到正在探索“引擎”循环，用于分析每次追踪并调整提示、工具等，使元循环更高效。这为开发者提供了从简单到复杂的循环构建路径，适合需要自动化迭代和系统优化的场景。
It's easy to build loops with LangChain/DeepAgents!
It's easy to build loops with LangChain/DeepAgents! Sydney Runkle @sydneyrunkle everybody's talking about loops!! how can you instrument them with langchain? 1. token loop supported by a model (choose any model with langchain) 2. create_agent gives you the agent loop (model + tools + repeat until done) 3. deepagents gives you the self verification loop (agent loop + verify + repeat until satisfied) 4. deployments give you the meta loop (trigger agent runs in reaction to events that help improve a system) 5. i think the ??? loop is what we're trying to close with engine: run an agent over each trace and figure out what to tweak - prompts, tools, self verification, etc so that your meta loop is more effective per cycle. 🔗 View Quoted Tweet 💬 3 🔄 1 ❤️ 9 👀 1104 📊 3 ⚡