What memory types are supported by these patterns?

The skill covers various memory management strategies including ConversationBuffer, Summary, Window, Entity, and VectorStore memory for handling different context requirements.

Can I use this for RAG (Retrieval-Augmented Generation)?

Yes, it includes detailed patterns for the entire RAG pipeline, including document loading, intelligent text splitting, vector store integration, and retrieval-qa chains.

How does this skill help with agent development?

It provides standardized patterns for ReAct, OpenAI Functions, and Structured Chat agents, including tool integration, reasoning loops, and robust error handling.

Does it include testing strategies for LLM applications?

Yes, it provides specific testing patterns for mocking LLM responses and verifying agent tool selection and memory persistence without incurring API costs.

LangChain Architecture & LLM Development

Name: LangChain Architecture & LLM Development
Author: HermeticOrmus

byHermeticOrmus

Ciencia de Datos y ML

Architects sophisticated LLM applications using LangChain's agent, memory, and tool integration patterns.

Acerca de

This skill empowers developers to master the LangChain framework for building complex, production-grade LLM applications. It provides comprehensive patterns for implementing autonomous agents, managing long-term conversation memory, and creating modular multi-step workflows. Whether you're building RAG (Retrieval-Augmented Generation) pipelines, custom AI agents with tool access, or optimizing performance through caching and streaming, this skill offers the structural guidance and implementation best practices needed to transition from simple prompts to sophisticated, stateful AI systems.

Características Principales

Autonomous Agent Orchestration
RAG & Document Processing Pipelines
Multi-step Sequential Chain Composition
0 GitHub stars
Custom Callback & Monitoring Hooks
Sophisticated Memory Management Patterns

Casos de Uso

Managing complex, stateful conversation history for enterprise chatbots
Implementing RAG-based document retrieval and analysis systems
Building autonomous AI agents with specialized tool and API access

Acerca de

Características Principales

Autonomous Agent Orchestration
RAG & Document Processing Pipelines
Multi-step Sequential Chain Composition
0 GitHub stars
Custom Callback & Monitoring Hooks
Sophisticated Memory Management Patterns

Casos de Uso

Managing complex, stateful conversation history for enterprise chatbots
Implementing RAG-based document retrieval and analysis systems
Building autonomous AI agents with specialized tool and API access