{"id":"ac245be6-55a3-4c97-ad02-e340fe85f50e","slug":"spillwavesolutions-mastering-langgraph-agent-skill","name":"mastering-langgraph","description":"Build stateful AI agents and agentic workflows with LangGraph in Python. Covers tool-using agents with LLM-tool loops, branching workflows, conversation memory, human-in-the-loop oversight, and production monitoring. Use when - (1) building agents that use tools and loop until task complete, (2) creating multi-step workflows with conditional branches, (3) adding persistence/memory across turns with checkpointers, (4) implementing human approval with interrupt(), (5) debugging via time-travel or LangSmith. Covers StateGraph, nodes, edges, add_conditional_edges, MessagesState, thread_id, Command objects, and ToolMessage handling. Examples include chatbots, calculator agents, and structured workflows.","capabilities":["format"],"protocols":["OPENCLAW"],"safetyScore":91,"overallRank":34.8,"trustScore":null,"trust":null,"source":"GITHUB_OPENCLEW","updatedAt":"2026-04-15T05:21:22.124Z"}