Do Deepagents Skill
Description
skill指导AI正确的写代码-基于langchain deepagents框架。为什么需要 DeepAgents 这一套?单一提示词撑不住复杂任务真实业务往往是:多步推理、多工具组合、长会话、还要记用户偏好和历史决策。把一切都塞进一个 Chat 里,容易出现指令被淹没、行为漂移、失败不可定位。这就是为什么像Claude Code、Deep Research、Manus这样的应用能够脱颖而出——它们不是简单的工具调用器,而是具备深度思考能力的智能体。
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
do-deepagents-skill - DeepAgents Framework Skill
掌握 [DeepAgents](https://github.com/langchain-ai/deepagents) 框架的技能库。DeepAgents 是 LangChain 团队推出的独立 Python 库,构建在 LangChain + LangGraph 之上,为构建智能代理提供有主见的默认配置。
项目简介
本项目为 AI Agent 提供 DeepAgents 框架的完整知识库,包括:
- 核心 API 参考 (
create_deep_agent) - 内置工具详解(文件系统、任务规划、子代理、上下文压缩)
- 中间件系统架构
- 后端系统(StateBackend、FilesystemBackend、StoreBackend、Sandbox 等)
- 子代理系统设计
- Skills 技能扩展机制
- 流式输出与人机协同
- 生产环境部署指南
快速开始
安装
pip install deepagents
第一个 Agent
from deepagents import create_deep_agent
def get_weather(city: str) -> str:
"""Get weather for a given city."""
return f"It's always sunny in {city}!"
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
tools=[get_weather],
system_prompt="You are a helpful assistant",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What is the weather in SF?"}]}
)
print(result["messages"][-1].content)
核心能力
| 能力 | 内置工具 | 描述 |
|---|---|---|
| 规划 | write_todos |
任务分解与进度追踪 |
| 文件系统 | read_file, write_file, edit_file, ls, glob, grep |
上下文管理与大结果自动 offload |
| Shell 执行 | execute |
沙箱后端中运行命令 |
| 子代理 | task |
上下文隔离的任务委派 |
| 上下文压缩 | compact_conversation |
85% 窗口时自动摘要 |
项目结构
do-deepagents-skill/
├── SKILL.md # 主技能文件
└── references/
├── getting-started.md # 完整入门指南
└── docs/ # 详细文档
├── Deep Agents overview.md
├── Quickstart.md
├── Configuration.md
├── Backends.md
├── Subagents.md
├── Skills.md
├── Memory.md
├── Streaming.md
├── Human-in-the-loop.md
├── Sandboxes.md
├── Interpreters.md
└── ... (更多文档)
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