LangChain
技能 已验证 活跃Framework 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.
To provide developers with a robust and flexible framework for building sophisticated LLM applications, agents, and RAG systems, streamlining the development process from prototyping to production.
功能
- Framework for LLM applications (agents, chains, RAG)
- Multi-provider LLM support (OpenAI, Anthropic, Google)
- Extensive integrations (500+ tools, vector stores)
- Support for ReAct agents and tool calling
- Memory management for conversational context
- Vector store retrieval for RAG pipelines
使用场景
- Building chatbots with conversation memory
- Implementing retrieval-augmented generation (RAG) pipelines
- Creating agents with tool-using capabilities
- Rapid prototyping of LLM-powered applications
- Production deployments with observability (LangSmith)
非目标
- Specific LLM model training or fine-tuning
- Deep dive into individual vector database management
- Standalone deployment of applications (focus on framework)
- Low-level AI research beyond application development
工作流
- Define LLM models and tools
- Construct chains or agents for task execution
- Integrate memory for conversational context
- Implement RAG pipelines with document loading, splitting, embedding, and retrieval
- Execute and observe application behavior using LangSmith
实践
- LLM Application Development
- Agent Design
- RAG Implementation
- Tool Integration
- Observability
先决条件
- Python 3.10+
- LLM provider API keys (OpenAI, Anthropic, etc.)
- Optional: Vector database setup
- Optional: LangSmith API key for tracing
Scope
- info:Tool surface sizeWhile not explicitly counting tools, the documentation showcases numerous integrations and patterns, suggesting a large but well-categorized surface area.
安装
请先添加 Marketplace
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skills质量评分
已验证类似扩展
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.
Langchain Framework
99LangChain LLM application framework with chains, agents, RAG, and memory for building AI-powered applications
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
Rag Implementation
98Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Hybrid Search Implementation
98Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Init
100创建或优化存储库的 AGENTS.md 文件，提供最少、高信号的说明，涵盖代理无法从代码库推断的不可发现的编码约定、工具怪癖、工作流偏好和项目特定规则。在为新存储库设置代理说明或 Claude 配置时，当现有的 AGENTS.md 文件过长、通用或过时，当代理反复犯可避免的错误，或当存储库工作流发生变化且需要修剪代理配置时使用。应用可发现性过滤器—省略 Claude 可从 README、代码、配置或目录结构中学到的任何内容—并应用质量门，以验证每行是否仍然准确且具有操作意义。