LangChain & LangGraph Architecture
技能 已验证 活跃Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
Master modern LangChain 1.x and LangGraph for building sophisticated LLM applications with agents, state management, memory, and tool integration.
功能
- Design LLM applications with LangChain 1.x
- Implement AI agents using LangGraph
- Manage conversation memory and state
- Integrate LLMs with external data and APIs
- Utilize ReAct, Plan-and-Execute, and Multi-Agent patterns
使用场景
- Building autonomous AI agents with tool access
- Implementing complex multi-step LLM workflows
- Managing conversation memory and state across sessions
- Creating modular and reusable LLM application components
非目标
- This skill does not provide direct execution of code or agent workflows.
- It does not replace the need for users to have LangChain and LangGraph installed.
- It is focused on design and architecture, not on specific deployment strategies.
工作流
- Understand core LangChain and LangGraph concepts.
- Explore different agent patterns (ReAct, Plan-and-Execute, Multi-Agent).
- Learn about state management and memory systems.
- Implement document processing pipelines and callback systems.
- Review code examples for practical application.
安装
请先添加 Marketplace
/plugin marketplace add wshobson/agents/plugin install llm-application-dev@claude-code-workflows质量评分
已验证类似扩展
Context Compression
100This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
LangGraph
97Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
Mcp Setup
100Configure popular MCP servers for enhanced agent capabilities
Deepinit
100Deep codebase initialization with hierarchical AGENTS.md documentation
Agent Worker Specialist
100Agent skill for worker-specialist - invoke with $agent-worker-specialist
Orchestrate
100Wire Commands, Agents, and Skills together for complex features. Use when building features that need research, planning, and implementation phases.