langgraph

Build stateful directed graphs for AI agents with Python's LangGraph library.

3|Updated Jan 20, 2026
One-click install
npx skills add https://github.com/Sameeh07/AGENT-SKILLS --skill langgraph-sameeh07
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: langgraph
Source: https://github.com/Sameeh07/AGENT-SKILLS/tree/main/skills/LangGraph
Command: npx skills add https://github.com/Sameeh07/AGENT-SKILLS --skill langgraph-sameeh07

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the creation of complex, stateful AI agent workflows and pipelines, enabling sophisticated agent orchestration and data processing.

Core Features & Use Cases

  • Stateful Graph Construction: Define and build directed graphs with typed states for AI agents.
  • Conditional Routing & Parallelism: Implement dynamic branching, loops, and parallel execution of nodes.
  • Checkpointing & Persistence: Save and resume graph execution states for robust experimentation.
  • Subgraph Composition: Modularize complex workflows by composing smaller graphs.
  • Agent Integration: Easily incorporate LLMs and tools within graph nodes for agentic behavior.

Quick Start

Use the langgraph skill to define a stateful graph that increments a counter until it reaches 5.

Frequently Asked Questions about langgraph

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

FAQPage Schema
How do I build stateful AI agent workflows with conditional routing?

Build stateful AI agent workflows by constructing directed graphs with typed states, enabling dynamic branching, loops, and conditional routing for sophisticated agent orchestration and data processing pipelines.

What is the best way to orchestrate complex LLM pipelines with loops and parallel execution?

Orchestrate complex LLM pipelines by defining stateful directed graphs that support parallel node execution and loops, allowing dynamic workflow automation and granular control over agent behavior.

Can I save and resume AI agent execution states during workflow automation?

Yes, you can save and resume AI agent execution states using checkpointing and persistence features, which allow robust experimentation by capturing and reloading graph execution states.

How do I modularize complex AI agent workflows into smaller components?

Modularize complex AI agent workflows through subgraph composition, enabling you to build and integrate smaller directed graphs into larger, manageable stateful pipelines.

Do I need Python state management knowledge to use LangGraph for agent orchestration?

Yes, developing with LangGraph requires a solid understanding of Python state management, node functions, and graph compilation to effectively orchestrate AI agents and data processing pipelines.

How do I integrate LLMs and tools within stateful graph nodes for agentic behavior?

Integrate LLMs and tools directly within stateful graph nodes to facilitate agentic behavior, enabling dynamic conditional routing and complex workflow automation across the directed graph.