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