Build stateful multi-actor AI agent workflows with LangGraph.
npx skills add https://github.com/htooayelwinict/claude-config --skill langgraph
Please help me install this Agent Skill. Skill: langgraph Source: https://github.com/htooayelwinict/claude-config/tree/main/skills/langgraph Command: npx skills add https://github.com/htooayelwinict/claude-config --skill langgraph
LangGraph enables you to design and run stateful, cyclic AI agent workflows with memory, checkpoints, and dynamic routing. It helps coordinate multiple agents and tools across complex tasks without losing context.
Define a simple graph with an agent node and a tool node, compile the workflow, and invoke it with an initial message history.
High-intent search queries and answers about installing and using this skill.
LangGraph enables building stateful AI agent workflows by defining nodes, edges, and conditional routing within a StateGraph. You compile the graph and invoke it with initial state, and LangGraph automatically manages checkpointing and memory persistence across multi-step interactions.
Yes. LangGraph supports cyclic workflows with typed state definitions and memory persistence, allowing agents to maintain context across repeated interactions. Checkpointing automatically saves state at each step for recovery and long-running conversations.
LangGraph supports multi-agent orchestration through subgraphs and conditional routing. Define separate nodes for different agents or tools, connect them with edges, and use conditional logic to route between them based on state, enabling complex multi-actor workflows.
LangGraph includes human-in-the-loop capabilities that pause workflow execution at specified nodes, allowing human review or intervention. You define checkpoint points and control resume logic, integrating human decisions into the agent graph without losing state.
LangGraph uses intuitive graph concepts—nodes represent steps, edges define connections, and START/END provide boundaries. While understanding graphs helps, the framework provides prebuilt components and straightforward APIs, making it accessible for building agent workflows without deep graph expertise.
Yes. LangGraph's checkpointing and persistence features store agent state at each step, allowing you to pause workflows and resume them later without losing context. This is essential for long-running conversations and multi-session agent applications.
collat-labsDesign and execute stateful LangGraph workflows with StateGraph, nodes, edges, and commands.
imehrLog AI executions and learn from feedback using JSONL logs and JSON recommendations.
krzysztofsurdyReference LangChain components for building agents, RAG pipelines, and LangGraph workflows.
pazdedavDefine DAG workflow graphs with JSON schema for agent orchestration.
