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 +