langgraph-workflows

What problem does it solve?

Building complex, stateful multi-agent AI systems often requires intricate control flow, state management, and error handling, which can be difficult to design and implement. This Skill provides expert guidance for architecting LangGraph workflows.

Core Features & Use Cases

• Stateful Graph Design: Design robust LangGraph state machines with nodes, edges, and shared state for dynamic AI orchestration.
• Advanced Node Types: Utilize AGENT_NODE, CONDITIONAL_NODE, LOOP_NODE, APPROVAL_NODE, and CHECKPOINT_NODE for sophisticated control flow.
• Workflow Patterns: Implement common patterns like sequential pipelines, conditional branching, reflection loops, and supervisor patterns.
• Use Case: You want to automate the process of generating, reviewing, and revising marketing copy. Use this Skill to design a LangGraph workflow with a "Writer" agent, a "Reviewer" agent, and a CONDITIONAL_NODE to loop back for revisions until the copy is approved, saving manual oversight.

Quick Start

Build a workflow for writing blog posts that includes research, writing, editing, and a review step.