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理解 Deep Agents 框架 - 它们是什么、如何使用 createDeepAgent 创建,以及 agent harness 架构,包括内置的规划、文件系统和子代理中间件。
Passed 21 of 22 test cases with the skill, and 8 of 22 without it.
and 13 more in the full report
13 fixed8 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
If I call `await createDeepAgent({})` without passing a custom `model` property, which API key environment variable must be set in process environment?
| Checked | Without skill | With skill |
|---|---|---|
| The response specifies ANTHROPIC_API_KEY as the required environment variable | Missed | Passed |
The instructions your model reads when the skill loads.
Deep Agents 是一个基于 LangChain 和 LangGraph 构建的自有代理框架,专为复杂的多步骤任务而设计。它们"开箱即用",具有内置功能:
Deep Agents 使用"agent harness"架构 - 与其他框架相同的核心工具调用循环,但具有预配置的中间件和工具。
| 使用 Deep Agents 当 | 使用 LangChain 的 createAgent 当 |
|---|---|
| 需要规划的多步骤任务 | 简单、单一用途的任务 |
| 需要文件管理的大上下文 | 上下文适合单个提示 |
| 需要专用子代理 | 单个代理足够 |
| 跨会话持久化内存 | 临时的单会话工作 |
| CLI 或编码助手用例 | 简单 API 或聊天应用 |
import { createDeepAgent } from "deepagents";
// 具有默认设置的最小代理
const agent = await createDeepAgent({});
// 调用代理
const result = await agent.invoke({
messages: [
{ role: "user", content: "东京的天气怎么样?" }
]
});
test cases passed, vs 8 / 22 without
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