138 lines
5.1 KiB
Markdown
138 lines
5.1 KiB
Markdown
# langgraph-master
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**PROACTIVE SKILL** - Comprehensive guide for building AI agents with LangGraph. Claude invokes this skill automatically when LangGraph development is detected, providing architecture patterns, implementation guidance, and best practices.
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## Installation
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```
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/plugin marketplace add hiroshi75/ccplugins
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/plugin install protografico@hiroshi75
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```
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## Automatic Triggers
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Claude **automatically invokes** this skill when:
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- **LangGraph development** - Detecting LangGraph imports or StateGraph usage
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- **Agent architecture** - Planning or implementing AI agent workflows
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- **Graph patterns** - Working with nodes, edges, or state management
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- **Keywords detected** - When user mentions: LangGraph, StateGraph, agent workflow, node, edge, checkpointer
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- **Implementation requests** - Building chatbots, RAG agents, or autonomous systems
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**No manual action required** - Claude provides LangGraph expertise automatically.
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## Workflow
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```
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Detect LangGraph context → Auto-invoke skill → Provide patterns/guidance → Implement with best practices
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```
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## Manual Invocation (Optional)
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To manually trigger LangGraph guidance:
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```
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/protografico:langgraph-master
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```
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For learning specific patterns:
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```
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/protografico:langgraph-master "explain routing pattern"
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```
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## Learning Resources
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The skill provides comprehensive documentation covering:
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| Category | Topics | Files |
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| ----------------- | --------------------------------------------- | --------------------------- |
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| **Core Concepts** | State, Node, Edge fundamentals | 01*core_concepts*\*.md |
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| **Architecture** | 6 major graph patterns (Routing, Agent, etc.) | 02*graph_architecture*\*.md |
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| **Memory** | Checkpointer, Store, Persistence | 03*memory_management*\*.md |
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| **Tools** | Tool definition, Command API, Tool Node | 04*tool_integration*\*.md |
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| **Advanced** | Human-in-the-Loop, Streaming, Map-Reduce | 05*advanced_features*\*.md |
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| **Models** | Gemini, Claude, OpenAI model IDs | 06_llm_model_ids\*.md |
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| **Examples** | Chatbot, RAG agent implementations | example\_\*.md |
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## Subagent: langgraph-engineer
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The skill includes a specialized **protografico:langgraph-engineer** subagent for efficient parallel development:
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### Key Features
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- **Functional Module Scope**: Implements complete features (2-5 nodes) as cohesive units
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- **Parallel Execution**: Multiple subagents can develop different modules simultaneously
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- **Production-Ready**: No TODOs or placeholders, fully functional code only
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- **Skill-Driven**: Always references langgraph-master documentation before implementation
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### When to Use
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1. **Feature Module Implementation**: RAG search, intent analysis, approval workflows
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2. **Subgraph Patterns**: Complete functional units with nodes, edges, and state
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3. **Tool Integration**: Full tool integration modules with error handling
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### Parallel Development Pattern
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```
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Planner → Decompose into functional modules
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├─ langgraph-engineer 1: Intent analysis module (parallel)
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│ └─ analyze + classify + route nodes
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└─ langgraph-engineer 2: RAG search module (parallel)
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└─ retrieve + rerank + generate nodes
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Orchestrator → Integrate modules into complete graph
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```
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## How It Works
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1. **Context Detection** - Claude monitors LangGraph-related activities
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2. **Trigger Evaluation** - Checks if auto-invoke conditions are met
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3. **Skill Invocation** - Automatically invokes langgraph-master skill
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4. **Pattern Guidance** - Provides architecture patterns and best practices
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5. **Implementation Support** - Assists with code generation using documented patterns
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## Example Use Cases
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### Automatic Guidance
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```python
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# Claude detects LangGraph usage and automatically provides guidance
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from langgraph.graph import StateGraph
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# Skill auto-invoked → Provides state management patterns
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class AgentState(TypedDict):
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messages: list[str]
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```
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### Pattern Implementation
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```
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User: "Build a RAG agent with LangGraph"
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Claude: [Auto-invokes skill]
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→ Provides RAG architecture pattern
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→ Suggests node structure (retrieve → rerank → generate)
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→ Implements with checkpointer for state persistence
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```
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### Subagent Delegation
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```
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User: "Create a chatbot with intent classification and RAG search"
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Claude: → Decomposes into 2 modules
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→ Spawns langgraph-engineer for each module (parallel)
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→ Integrates completed modules into final graph
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```
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## Benefits
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- **Faster Development**: Pre-validated architecture patterns reduce trial and error
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- **Best Practices**: Automatically applies LangGraph best practices and conventions
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- **Parallel Implementation**: Efficient development through subagent delegation
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- **Complete Documentation**: 40+ documentation files covering all aspects
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- **Production-Ready**: Guidance ensures robust, maintainable implementations
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## Reference Links
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- [LangGraph Official Docs](https://docs.langchain.com/oss/python/langgraph/overview)
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- [LangGraph GitHub](https://github.com/langchain-ai/langgraph)
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