├── app/
│   ├── api/ # API 端点
│   ├── backend/ # LangGraph/LangChain 逻辑
│   └── frontend/ # 用户界面
├── .env.example
├── requirements.txt
└── README.md

# LangGraph for Agents

When to use

• Use this skill when the user asks to build agents or multi-agent systems using LangGraph/LangChain.

How to refer

Integrated Reference Examples

Read the examples in "./references/" to understand common patterns. Start with "./references/README.md" for an overview, then read the target file, it will show more details. !Important: To build an agent, API_KEY credentials is necessary. This is user privacy, please do not hard-code it, just hold a placeholder, e.g. API_KEY=your-api-key, and let the user manage the actual keys.

External Resources

[Search] If the "search" tool is available, you can refine the query keywords and execute the search. [Browse] If the "browse" tool is available, you can visit the following three websites:

• LangGraph Official GitHub Repository (https://github.com/langchain-ai/langgraph)
• LangGraph Official Documentation (https://docs.langchain.com/oss/python/langgraph/overview)
• LangChain Official Documentation (https://docs.langchain.com/oss/python/langchain/overview)

[Fetch] If the "fetch" tool is available, you can retrieve content from the following URL:

• Context-7 LangGraph (https://context7.com/websites/langchain_oss_python_langgraph/llms.txt?tokens=10000)

You may adjust the number of tokens by modifying the tokens parameter in the URL. The default value is 10,000.

Project Structure

For demos or tests, use a single .py file. For production-grade applications, use: `` ├── app/ │ ├── api/ # API endpoints │ ├── backend/ # LangGraph/LangChain logic │ └── frontend/ # User interface ├── .env.example ├── requirements.txt └── README.md `

Process for Agent System Design

Step 1: Determine System Level

• Single-Agent System: Focus on the internal structure of one agent.
• Multi-Agent System: Focus on collaboration and communication between multiple agents.

Step 2: Choose Framework

• LangGraph: Best for stateful, complex workflows.
• LangChain: Best for standard agent patterns based on tool calling.

Step 3: Design Specific Implementation

For Single-Agent Systems:

• With LangGraph: Build a workflow with several nodes, or implement a ReAct Agent with manual tool_node.
• With LangChain: Build a ReAct Agent by create_agent` API.

For Multi-Agent Systems:

• With LangGraph:

- Option 1: Treat each node as an independent agent, connecting them via the Graph API. - Option 2: Encapsulate a multi-node workflow as a single agent, calling other agents as tools.

• With LangChain:

- Create a main ReAct Agent and encapsulate other agents as tools for collaboration.

Build Philosophy

• Prefer Native: Check if a tool or integration already exists in LangChain before custom building.
• Single File First: Keep core logic in one file initially to simplify debugging.
• Clean Code: Provide only essential comments and use clear, descriptive variable names.
• Real Data: Use actual API URLs and schemas whenever possible.