What LangChain agent types does this skill support?

The skill provides patterns for various agents including ReAct, OpenAI Functions, Structured Chat, Conversational, and Self-Ask with Search.

How does this skill handle conversation history?

It covers multiple memory strategies such as ConversationBuffer, SummaryMemory for long contexts, and VectorStoreMemory for semantic retrieval.

Does it provide advice on managing LLM costs?

It includes best practices for token usage tracking via callbacks and performance optimization through caching and batch processing.

Can I use this skill to build RAG systems?

Yes, it includes specialized patterns for document loading, text splitting, vector store integration, and retrieval-based QA chains.

Is there support for monitoring production LLM apps?

Yes, it provides custom callback handler patterns for logging request/response data, monitoring latency, and tracking error rates.

LangChain Architecture

Name: LangChain Architecture
Author: HermeticOrmus

byHermeticOrmus

Data Science & ML

Designs and implements sophisticated LLM applications using the LangChain framework with advanced patterns for agents, memory, and tool integration.

About

This skill empowers developers to architect production-grade AI applications by leveraging the full power of the LangChain framework. It provides expert guidance on building autonomous agents, orchestrating complex multi-step chains, and managing persistent conversation state across diverse memory types. Whether you are implementing Retrieval-Augmented Generation (RAG) pipelines or creating custom tool-using agents, this skill ensures best practices for modularity, observability, and performance optimization in your LLM workflows.

Key Features

Autonomous agent design using ReAct and function-calling patterns
0 GitHub stars
Comprehensive callback integration for logging and monitoring
Robust RAG pipeline implementation using vector stores and loaders
Advanced memory management strategies for long-term context retention
Complex chain orchestration including Sequential and Router chains

Use Cases

Building autonomous AI agents with access to custom internal APIs
Implementing document-based Q&A systems using RAG architectures
Creating stateful conversational interfaces with sliding window memory

About

Key Features

Autonomous agent design using ReAct and function-calling patterns
0 GitHub stars
Comprehensive callback integration for logging and monitoring
Robust RAG pipeline implementation using vector stores and loaders
Advanced memory management strategies for long-term context retention
Complex chain orchestration including Sequential and Router chains

Use Cases

Building autonomous AI agents with access to custom internal APIs
Implementing document-based Q&A systems using RAG architectures
Creating stateful conversational interfaces with sliding window memory