Does this skill help with managing conversation history?

Absolutely. It covers various memory management strategies such as ConversationBufferMemory, SummaryMemory, WindowMemory, and Semantic VectorStoreMemory.

Is it compatible with streaming LLM responses?

Yes, it includes implementation details for streaming responses and handling real-time output via callback handlers.

How does it handle LangChain observability in production?

The skill provides patterns for the LangChain callback system to enable request/response logging, token usage monitoring, and latency tracking.

Can I use this skill to implement RAG workflows?

Yes, it includes comprehensive patterns for document loading, intelligent text splitting, vector store management, and retrieval-based QA chains.

What types of LangChain agents does this skill support?

It supports a wide variety of agent types including ReAct, OpenAI Functions, Structured Chat, Conversational, and Self-Ask with Search.

LangChain Architecture & Agent Design

Name: LangChain Architecture & Agent Design
Author: HermeticOrmus

byHermeticOrmus

Data Science & ML

Architects sophisticated LLM applications using LangChain patterns for autonomous agents, conversational memory, and complex workflow orchestration.

About

This skill empowers developers to master the LangChain framework for building production-grade LLM applications and autonomous AI agents. It provides standardized implementation patterns for managing conversation state through specialized memory systems, constructing modular chains for multi-step workflows, and integrating external APIs as executable tools. Whether you are building RAG-based systems, implementing ReAct agents, or optimizing performance through streaming and caching, this skill ensures architectural integrity and adherence to LangChain best practices.

Key Features

Production observability via callback handlers for logging, monitoring, and cost tracking
Modular workflow construction using Sequential, Router, and Transform chains
Context management using specialized memory systems like Buffer, Summary, and VectorStore
End-to-end RAG pipelines with document loaders, splitters, and vector store integration
Agent orchestration with ReAct, OpenAI Functions, and structured tool calling
0 GitHub stars

Use Cases

Developing autonomous AI agents capable of using external tools and database APIs
Creating complex, multi-stage LLM processing workflows for data transformation and analysis
Building robust Retrieval-Augmented Generation (RAG) pipelines for domain-specific data

About

Key Features

Production observability via callback handlers for logging, monitoring, and cost tracking
Modular workflow construction using Sequential, Router, and Transform chains
Context management using specialized memory systems like Buffer, Summary, and VectorStore
End-to-end RAG pipelines with document loaders, splitters, and vector store integration
Agent orchestration with ReAct, OpenAI Functions, and structured tool calling
0 GitHub stars

Use Cases

Developing autonomous AI agents capable of using external tools and database APIs
Creating complex, multi-stage LLM processing workflows for data transformation and analysis
Building robust Retrieval-Augmented Generation (RAG) pipelines for domain-specific data