langchain-architecture

# LangChain Architecture Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration. ## Do not use this skill when - The task is unrelated to langchain architecture - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Use this skill when - Building autonomous AI agents with tool access - Implementing complex multi-step LLM workflows - Managing conversation memory and state - Integrating LLMs with external data sources and APIs - Creating modular, reusable LLM application components - Implementing document processing pipelines - Building production-grade LLM applications ## Core Concepts ### 1. Agents Autonomous systems that use LLMs to decide which actions to take. **Agent Types:** - **ReAct**: Reasoning + Acting in interleaved manner - **OpenAI Functions**: Leverages function calling API - **Structured Chat**: Handles multi-input tools - **Conversational**: Optimized for chat interfaces - **Self-Ask with Search**: Decomposes complex queries ### 2. Chains Sequences of calls to LLMs or other utilities. **Chain Types:** - **LLMChain**: Basic prompt + LLM combination - **SequentialChain**: Multiple chains in sequence - **RouterChain**: Routes inputs t...