Use LangGraph/LangChain to build agents
--- name: langgraph-for-agents description: Use LangGraph/LangChain to build agents --- # 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.
don't have the plugin yet? install it then click "run inline in claude" again.