langchain-architecture

version	4.1.0-fractal
name	langchain-architecture
description	Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

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

🧠 Knowledge Modules (Fractal Skills)

1. 1. Agents

2. 2. Chains

3. 3. Memory

4. 4. Document Processing

5. 5. Callbacks

6. Pattern 1: RAG with LangChain

7. Pattern 2: Custom Agent with Tools

8. Pattern 3: Multi-Step Chain

9. Choosing the Right Memory Type

10. Custom Callback Handler

11. 1. Caching

12. 2. Batch Processing

13. 3. Streaming Responses