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Install in OpenClaw
/install dive-into-langgraph
Description
A comprehensive guide and reference for building agents using LangGraph 1.0, including ReAct agents, state graphs, and tool integrations.
README (SKILL.md)
Dive Into LangGraph
LangGraph 是由 LangChain 团队开发的开源 Agent 框架。v1.0 是稳定版本，框架能力全面升级，支持中间件、状态图、多智能体等高级功能。本 skill 内容由《LangGraph 1.0 完全指南》提供。
LangGraph 1.0 完全指南：
- 在线文档：https://luochang212.github.io/dive-into-langgraph/
- GitHub：https://github.com/luochang212/dive-into-langgraph
安装依赖
基础依赖：
pip install \
langgraph \
"langchain[openai]" \
langchain-community \
langchain-mcp-adapters \
python-dotenv \
pydantic
环境变量
使用模型供应商的大模型需要设置环境变量，推荐使用阿里云百炼（DashScope）的模型：
# 阿里云百炼 (DashScope)
# 获取地址: https://bailian.console.aliyun.com/
DASHSCOPE_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
DASHSCOPE_API_KEY=your_api_key_here
# 火山方舟 (ARK)
# 获取地址: https://console.volcengine.com/ark/
ARK_BASE_URL=https://ark.cn-beijing.volces.com/api/v3
ARK_API_KEY=your_api_key_here
# 或者其他模型供应商...
请将环境变量添加到 .env 文件，并填入你的 API key。
章节概览
| 序号 | 章节 | 内容简介 | 在线阅读 | 离线阅读 |
|---|---|---|---|---|
| 1 | 快速入门 | 创建你的第一个 ReAct Agent | 在线 | 本地 |
| 2 | 状态图 | 使用 StateGraph 创建工作流 | 在线 | 本地 |
| 3 | 中间件 | 预算控制、消息截断、敏感词过滤、PII 检测 | 在线 | 本地 |
| 4 | 人机交互 | 使用 HITL 中间件实现人机交互 | 在线 | 本地 |
| 5 | 记忆 | 短期记忆、长期记忆 | 在线 | 本地 |
| 6 | 上下文工程 | 使用 State、Store、Runtime 管理上下文 | 在线 | 本地 |
| 7 | MCP Server | 创建 MCP Server 并接入 LangGraph | 在线 | 本地 |
| 8 | 监督者模式 | 两种方法：tool-calling、langgraph-supervisor | 在线 | 本地 |
| 9 | 并行 | 节点并发、@task 装饰器、Map-reduce、Sub-graphs | 在线 | 本地 |
| 10 | RAG | 向量检索、关键词检索、混合检索 | 在线 | 本地 |
| 11 | 网络搜索 | DashScope、Tavily 和 DDGS | 在线 | 本地 |
官方资源
Usage Guidance
This skill is a tutorial and includes example code that will call external LLM endpoints. Before installing or running it: 1) Expect to provide model API keys (e.g., DASHSCOPE_*, ARK_*) in a .env — the registry metadata did not declare these, so don't paste secrets unless you're comfortable. 2) Review the included examples and tools (they appear benign; math/safe_eval uses a safe AST evaluator). 3) Run examples in an isolated environment/virtualenv and avoid exposing production credentials. 4) If you plan to allow autonomous agents to use this skill, be cautious: providing long-lived model keys to a skill gives it network access to those services. 5) If anything is unclear, ask the skill author for a clear list of required env vars and for the upstream repository/source (SKILL.md links point to a GitHub repo and online docs — verify those sources).
Capability Analysis
Type: OpenClaw Skill
Name: dive-into-langgraph
Version: 1.0.5
The skill bundle is a comprehensive educational resource and reference guide for LangGraph 1.0. It contains extensive documentation, example scripts, and utility tools (such as `scripts/tools/tool_math.py`) that are well-implemented with security in mind, specifically using an AST-based whitelist for safe mathematical evaluation. The code and instructions in `SKILL.md` and the `references/` directory are entirely consistent with the stated purpose of teaching agent development, and there is no evidence of data exfiltration, malicious execution, or harmful prompt injection.
Capability Assessment
Purpose & Capability
The name/description (LangGraph guide, ReAct agents, state graphs, tools) matches the files and examples included. The Python examples and tools are consistent with teaching/demo usage of LangGraph and LangChain.
Instruction Scope
SKILL.md instructs users to install many LangGraph/LangChain-related Python packages and to populate a .env with model provider credentials (DASHSCOPE_*, ARK_*). The runtime examples load .env and create LLMs using DASHSCOPE_BASE_URL / DASHSCOPE_API_KEY. This is in-scope for a LangGraph tutorial, but the instructions explicitly require secret API keys and network endpoints — so review before supplying secrets.
Install Mechanism
There is no install spec in the registry (instruction-only), and the SKILL.md shows standard pip installation commands. No downloads from untrusted URLs or archive extraction are present. The dependency list is broad but appropriate for a LangGraph tutorial.
Credentials
The skill expects model-provider environment variables (e.g., DASHSCOPE_BASE_URL, DASHSCOPE_API_KEY, ARK_BASE_URL, ARK_API_KEY) and the code reads these via os.getenv. However, the registry metadata lists no required env vars or primary credential — a mismatch. Requesting API keys is reasonable for examples that call external model providers, but the registry should declare this explicitly so users know what secrets will be needed.
Persistence & Privilege
The skill does not request permanent inclusion (always:false), does not write system-wide configs, and only loads .env (dotenv) for credentials. It does not modify other skills or agent settings. Autonomous invocation (model invocation enabled) is the platform default and not an additional risk here.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install dive-into-langgraph - After installation, invoke the skill by name or use
/dive-into-langgraph - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.5
- Updated documentation links from luochang.ink to luochang212.github.io for all online resources.
- No other content or functionality changes.
v1.0.4
Version 1.0.4
- Removed requirement for DashScope (Alibaba Cloud) or ARK (Volcengine) API keys in the description.
- No code or content changes; documentation updated for clarity and broader usability.
v1.0.3
- Removed the sample script file scripts/tools/tool_fs.py.
- Updated the description to clarify that running example scripts requires API keys from DashScope (Alibaba Cloud) or ARK (Volcengine).
- Minor clarifications in environment variable setup instructions.
v1.0.2
- Updated environment variable documentation for clarity; users are now instructed to add keys to a .env file, and provider sections have been reorganized.
- Removed the sample .env file and the old file tool (tool_file.py).
- Added a new filesystem tool script (tool_fs.py) to replace the previous implementation.
v1.0.1
- Added sample scripts and tool modules for enhanced agent building with LangGraph.
- Introduced example environment file (scripts/env-example.txt) to guide environment variable setup.
- Included utility modules and sample tools for file handling and math operations.
- Provided example usage in scripts/examples.py to demonstrate new capabilities.
v1.0.0
Initial release — a comprehensive LangGraph 1.0 guide.
- Covers core concepts: ReAct agents, state graphs, middlewares, memory, tool integrations, and multi-agent support.
- Includes step-by-step chapters for quickstart, workflow design, HITL, supervisor mode, RAG, web search, parallelization, and more.
- Provides dependency setup and environment variable instructions for major LLM providers (DashScope, Ark, Kimi).
- Links to both online documentation and offline chapter references.
- Includes a list of recommended official LangChain/LangGraph resources and repositories.
Metadata
Frequently Asked Questions
What is dive-into-langgraph?
A comprehensive guide and reference for building agents using LangGraph 1.0, including ReAct agents, state graphs, and tool integrations. It is an AI Agent Skill for Claude Code / OpenClaw, with 438 downloads so far.
How do I install dive-into-langgraph?
Run "/install dive-into-langgraph" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is dive-into-langgraph free?
Yes, dive-into-langgraph is completely free (open-source). You can download, install and use it at no cost.
Which platforms does dive-into-langgraph support?
dive-into-langgraph is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created dive-into-langgraph?
It is built and maintained by Chang Luo (@luochang212); the current version is v1.0.5.
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