Langchain Framework
技能 已验证 活跃LangChain LLM application framework with chains, agents, RAG, and memory for building AI-powered applications
To provide developers with comprehensive guidance and practical examples for building LLM-powered applications using the LangChain framework, covering everything from basic chains to advanced RAG and agent implementations.
功能
- Detailed documentation of LangChain core concepts (LCEL, RAG, Agents, Memory)
- Illustrative Python code examples for all major components
- Guidance on production deployment, error handling, and caching
- Coverage of async patterns and LangSmith tracing integration
使用场景
- Learning how to build LLM applications with LangChain
- Implementing RAG pipelines for knowledge retrieval
- Developing AI agents that can use tools and memory
- Understanding advanced chain and memory patterns
非目标
- Does not provide pre-built LangChain agents or chains
- Does not execute LangChain code directly
- Does not offer direct integration with specific LLM providers beyond framework examples
安装
npx skills add bobmatnyc/claude-mpm-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
LangChain
99Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
TradeMemory Protocol
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Chat Format
100Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
Rag Architect
100Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Dspy
99Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming