Use when specifying or implementing LangGraph applications - from architecture planning and specification writing to actual code implementation. Also use for designing agent workflows or learning LangGraph patterns. This is a comprehensive guide for building AI agents with LangGraph, covering core concepts, architecture patterns, memory management, tool integration, and advanced features.
Provides comprehensive guidance for building AI agents with LangGraph, covering core concepts, architecture patterns, memory management, and tool integration. Use when planning, specifying, or implementing LangGraph applications, designing agent workflows, or learning LangGraph patterns.
/plugin marketplace add hiroshi75/ccplugins/plugin install hiroshi75-langgraph-master-plugin-langgraph-master-plugin@hiroshi75/ccpluginsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
01_core_concepts_edge.md01_core_concepts_node.md01_core_concepts_overview.md01_core_concepts_state.md02_graph_architecture_agent.md02_graph_architecture_evaluator_optimizer.md02_graph_architecture_orchestrator_worker.md02_graph_architecture_overview.md02_graph_architecture_parallelization.md02_graph_architecture_prompt_chaining.md02_graph_architecture_routing.md02_graph_architecture_subgraph.md02_graph_architecture_workflow_vs_agent.md03_memory_management_checkpointer.md03_memory_management_overview.md03_memory_management_persistence.md03_memory_management_store.md04_tool_integration_command_api.md04_tool_integration_overview.md04_tool_integration_tool_definition.mdA comprehensive guide for building AI agents using LangGraph.
Understanding the three core elements of LangGraph
Six major graph patterns and agent design
Persistence and checkpoint functionality
External tool integration and execution control
Advanced functionality and implementation patterns
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.
Practical agent implementation examples
Each section can be read independently, but reading them in order is recommended:
Each file is kept short and concise, allowing you to reference only the sections you need.
To accelerate LangGraph application development, utilize the dedicated subagent langgraph-master-plugin:langgraph-engineer.
langgraph-master-plugin:langgraph-engineer is an agent specialized in implementing functional modules:
Use langgraph-master-plugin:langgraph-engineer in the following cases:
When functional module implementation is needed
Subgraph and pattern implementation
Tool integration and memory setup
Task: Build a chatbot with intent analysis and RAG search
Parallel Execution Pattern:
Planner → Decompose into functional units
├─ langgraph-master-plugin:langgraph-engineer 1: Intent analysis module (parallel)
│ └─ analyze + classify + route nodes + conditional edges
└─ langgraph-master-plugin:langgraph-engineer 2: RAG search module (parallel)
└─ retrieve + rerank + generate nodes + state management
Orchestrator → Integrate modules to assemble graph
Decompose into functional modules
Implement common parts first
Parallel Execution
Assign one functional module implementation to each langgraph-master-plugin:langgraph-engineer agent and execute in parallel
Integration
Appropriate Size (langgraph-master-plugin:langgraph-engineer scope):
Too Small (individual implementation is sufficient):
Too Large (further decomposition needed):
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