Langchain Orchestration

If you're building with LangChain beyond toy examples, this covers the orchestration patterns you'll actually need. It walks through LCEL composition, sequential and parallel chains, routing logic, and the Runnable interface that ties everything together. The examples show real patterns like map-reduce for parallel processing and conditional branching for dynamic workflows. What's helpful here is seeing how chains compose using the pipe operator and how to handle streaming, async, and batch operations consistently. It doesn't cover agents or RAG deeply despite mentioning them in tags, but the chain orchestration fundamentals are solid. Good reference when you're moving from proof of concept to something that needs proper flow control.

skills.sh installs: 452
GitHub stars: 63
last updated: Aug 21, 2026

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Install

$ npx -y skills add manutej/luxor-claude-marketplace --skill langchain-orchestration --agent claude-code

Installs into .claude/skills of the current project.

Files

LangChain Orchestration Skill

Complete guide for building production-grade LLM applications with LangChain, covering chains, agents, memory, RAG patterns, and advanced orchestration techniques.

Core Concepts
Chains
Agents
Memory Systems
RAG Patterns
LLM Integrations
Callbacks & Monitoring
Retrieval Strategies
Streaming
Error Handling
Production Best Practices

Core Concepts

LangChain Expression Language (LCEL)

LCEL is the declarative way to compose chains in LangChain, enabling streaming, async, and parallel execution.

from langchain_core.runnables import RunnablePassthrough
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

SKILL.md on GitHub