langchain-langgraph

Name: langchain-langgraph
Availability: InStock
Author: Umbral-Bot

Develop AI agents and orchestration pipelines using LangChain and LangGraph.

Updated Feb 26, 2026

Search Tags:#langchain #rag #ai agent #orchestration #langgraph #state machine

Ready to run

npx skills add https://github.com/Umbral-Bot/umbral-agent-stack --skill langchain-langgraph

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-langgraph
Source: https://github.com/Umbral-Bot/umbral-agent-stack/tree/main/openclaw/workspace-templates/skills/langchain-langgraph
Command: npx skills add https://github.com/Umbral-Bot/umbral-agent-stack --skill langchain-langgraph

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, langchain-anthropic, langchain-openai, langgraph, langmem, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the creation of sophisticated AI agents and complex orchestration pipelines by leveraging powerful frameworks like LangChain and LangGraph.

Core Features & Use Cases

Agent Development: Build intelligent agents capable of using tools and reasoning.
Orchestration Pipelines: Design multi-step workflows and state machines for AI tasks.
RAG Implementation: Integrate retrieval-augmented generation for context-aware AI.
Use Case: Develop an AI customer support agent that can answer questions, access a knowledge base, and escalate complex issues to a human agent based on predefined logic.

Quick Start

Use the langchain-langgraph skill to create a simple ReAct agent that can search the web and perform calculations.

Frequently Asked Questions about langchain-langgraph

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build an AI agent with LangChain and LangGraph?▼

You build an AI agent with LangChain and LangGraph by defining tools, reasoning logic, and state machines to orchestrate multi-step workflows. This enables creating intelligent agents capable of accessing knowledge bases and escalating complex issues.

What is the best way to implement retrieval-augmented generation for AI agents?▼

Retrieval-augmented generation is implemented by integrating context-aware retrieval pipelines into your AI agents. This approach allows agents to access external knowledge bases, providing accurate, context-aware responses for complex queries.

Do I need LLM API keys to run LangGraph state machines?▼

Yes, you need LLM API keys to run LangGraph state machines. Execution requires specific Python libraries including langchain-anthropic and langchain-openai, along with API credentials to power the underlying language models.

Can I use LangGraph for complex orchestration pipelines?▼

Yes, you can use LangGraph for complex orchestration pipelines. It provides state machine capabilities to design multi-step workflows, allowing you to build advanced AI applications like customer support agents with predefined escalation logic.

How does a ReAct agent work with LangChain?▼

A ReAct agent works by combining reasoning and action within a LangChain pipeline, enabling the agent to search the web and perform calculations. It uses tools and memory to process inputs and execute multi-step orchestration tasks.

What are the limitations of building AI pipelines with LangGraph?▼

Limitations of building AI pipelines with LangGraph include the strict dependency on specific Python libraries like langmem and external LLM API keys. Complex state machines also require advanced orchestration design to manage reasoning, tools, and memory effectively.