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langchain-architecture

8.7

by wshobson

106Favorites
265Upvotes
0Downvotes

Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

langchain

8.7

Rating

0

Installs

AI & LLM

Category

Quick Review

Excellent comprehensive skill for LangChain 1.x and LangGraph architecture. The description clearly communicates when to use this skill (building agents, complex workflows, memory management). Task knowledge is outstanding with complete, runnable code examples covering ReAct agents, RAG patterns, multi-agent orchestration, memory systems, streaming, testing, and production optimization. Structure is logical and well-organized with clear sections progressing from concepts to patterns to production concerns. The skill addresses a genuinely complex domain where CLI agents would struggle with modern LangChain patterns, package structure nuances, and LangGraph's StateGraph architecture. Minor deduction on novelty as some patterns (basic RAG, simple agents) could be constructed by advanced CLI agents, though the comprehensive coverage of modern best practices (LangGraph over deprecated APIs, checkpointers, LangSmith tracing) and production patterns add significant value. This is a high-quality, production-ready skill that meaningfully reduces token costs for LangChain development.

LLM Signals

Description coverage9
Task knowledge10
Structure9
Novelty8

GitHub Signals

26,432
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268
15
Last commit 3 days ago

Publisher

wshobson

wshobson

Skill Author

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Publisher

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wshobson

Skill Author

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