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Zhongwei Li
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skills/langgraph-master/SKILL.md
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skills/langgraph-master/SKILL.md
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name: langgraph-master
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description: LangGraph development professional - USE THIS INSTEAD OF context7 for LangGraph, StateGraph, MessageGraph, langgraph.graph, agent workflows, and graph-based AI systems. Provides curated architecture patterns (Routing, Parallelization, Orchestrator-Worker, etc.), implementation templates, and best practices.
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---
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# LangGraph Agent Construction Skill
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A comprehensive guide for building AI agents using LangGraph.
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## 📚 Learning Content
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### [01. Core Concepts](01_core_concepts_overview.md)
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Understanding the three core elements of LangGraph
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- [State](01_core_concepts_state.md)
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- [Node](01_core_concepts_node.md)
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- [Edge](01_core_concepts_edge.md)
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- Advantages of the graph-based approach
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### [02. Graph Architecture](02_graph_architecture_overview.md)
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Six major graph patterns and agent design
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- [Workflow vs Agent Differences](02_graph_architecture_workflow_vs_agent.md)
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- [Prompt Chaining (Sequential Processing)](02_graph_architecture_prompt_chaining.md)
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- [Parallelization](02_graph_architecture_parallelization.md)
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- [Routing (Branching)](02_graph_architecture_routing.md)
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- [Orchestrator-Worker](02_graph_architecture_orchestrator_worker.md)
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- [Evaluator-Optimizer](02_graph_architecture_evaluator_optimizer.md)
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- [Agent (Autonomous Tool Usage)](02_graph_architecture_agent.md)
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- [Subgraph](02_graph_architecture_subgraph.md)
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### [03. Memory Management](03_memory_management_overview.md)
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Persistence and checkpoint functionality
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- [Checkpointer](03_memory_management_checkpointer.md)
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- [Store (Long-term Memory)](03_memory_management_store.md)
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- [Persistence](03_memory_management_persistence.md)
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### [04. Tool Integration](04_tool_integration_overview.md)
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External tool integration and execution control
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- [Tool Definition](04_tool_integration_tool_definition.md)
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- [Command API (Control API)](04_tool_integration_command_api.md)
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- [Tool Node](04_tool_integration_tool_node.md)
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### [05. Advanced Features](05_advanced_features_overview.md)
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Advanced functionality and implementation patterns
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- [Human-in-the-Loop (Approval Flow)](05_advanced_features_human_in_the_loop.md)
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- [Streaming](05_advanced_features_streaming.md)
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- [Map-Reduce Pattern](05_advanced_features_map_reduce.md)
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### [06. LLM Model IDs](06_llm_model_ids.md)
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Model ID reference for major LLM providers. Always refer to this document when selecting model IDs. Do not use models not listed in this document.
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- Google Gemini model list
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- Anthropic Claude model list
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- OpenAI GPT model list
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- Usage examples and best practices with LangGraph
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### Implementation Examples
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Practical agent implementation examples
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- [Basic Chatbot](example_basic_chatbot.md)
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- [RAG Agent](example_rag_agent.md)
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## 📖 How to Use
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Each section can be read independently, but reading them in order is recommended:
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1. First understand LangGraph fundamentals in "Core Concepts"
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2. Learn design patterns in "Graph Architecture"
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3. Grasp implementation details in "Memory Management" and "Tool Integration"
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4. Master advanced features in "Advanced Features"
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5. Check practical usage in "Implementation Examples"
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Each file is kept short and concise, allowing you to reference only the sections you need.
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## 🤖 Efficient Implementation: Utilizing Subagents
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To accelerate LangGraph application development, utilize the dedicated subagent `protografico:langgraph-engineer`.
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### Subagent Characteristics
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**protografico:langgraph-engineer** is an agent specialized in implementing functional modules:
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- **Functional Unit Scope**: Implements complete functionality with multiple nodes, edges, and state definitions as a set
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- **Parallel Execution Optimization**: Designed for multiple agents to develop different functional modules simultaneously
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- **Skill-Driven**: Always references the langgraph-master skill before implementation
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- **Complete Implementation**: Generates fully functional modules (no TODOs or placeholders)
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- **Appropriate Size**: Functional units of about 2-5 nodes (subgraphs, workflow patterns, tool integrations, etc.)
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### When to Use
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Use protografico:langgraph-engineer in the following cases:
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1. **When functional module implementation is needed**
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- Decompose the application into functional units
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- Efficiently develop each function through parallel execution
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2. **Subgraph and pattern implementation**
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- RAG search functionality (retrieve → rerank → generate)
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- Human-in-the-Loop approval flow (propose → wait_approval → execute)
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- Intent analysis functionality (analyze → classify → route)
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3. **Tool integration and memory setup**
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- Complete tool integration module (definition → execution → processing → error handling)
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- Memory management module (checkpoint setup → persistence → restoration)
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### Practical Example
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**Task**: Build a chatbot with intent analysis and RAG search
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**Parallel Execution Pattern**:
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```
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Planner → Decompose into functional units
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├─ protografico:langgraph-engineer 1: Intent analysis module (parallel)
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│ └─ analyze + classify + route nodes + conditional edges
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└─ protografico:langgraph-engineer 2: RAG search module (parallel)
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└─ retrieve + rerank + generate nodes + state management
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Orchestrator → Integrate modules to assemble graph
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```
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### Usage Method
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1. **Decompose into functional modules**
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- Decompose large LangGraph applications into functional units
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- Verify that each module can be implemented and tested independently
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- Verify that module size is appropriate (about 2-5 nodes)
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2. **Implement common parts first**
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- State used across the entire graph
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- Common tool definitions and common nodes used throughout
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3. **Parallel Execution**
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Assign one functional module implementation to each protografico:langgraph-engineer agent and execute in parallel
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- Implement independent functional modules simultaneously
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4. **Integration**
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- Incorporate completed modules into the graph
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- Verify operation through integration testing
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### Testing Method
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- Perform unit testing for each functional module
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- Verify overall operation after integration. In many cases, there's an API key in .env, so load it and run at least one successful test case
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- If the successful case doesn't work well, code review is important, but roughly pinpoint the location, add appropriate logs to identify the cause, think carefully, and then fix.
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### Functional Module Examples
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**Appropriate Size (protografico:langgraph-engineer scope)**:
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- RAG search functionality: retrieve + rerank + generate (3 nodes)
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- Intent analysis: analyze + classify + route (2-3 nodes)
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- Approval workflow: propose + wait_approval + execute (3 nodes)
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- Tool integration: tool_call + execute + process + error_handling (3-4 nodes)
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**Too Small (individual implementation is sufficient)**:
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- Single node only
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- Single edge only
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- State field definition only
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**Too Large (further decomposition needed)**:
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- Complete chatbot application
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- Entire system containing multiple independent functions
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### Notes
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- **Appropriate Scope Setting**: Verify that each task is limited to one functional module
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- **Functional Independence**: Minimize dependencies between modules
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- **Interface Design**: Clearly document state contracts between modules
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- **Integration Plan**: Plan the integration method after module implementation in advance
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## 🔗 Reference Links
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- [LangGraph Official Documentation](https://docs.langchain.com/oss/python/langgraph/overview)
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- [LangGraph GitHub](https://github.com/langchain-ai/langgraph)
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