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LangGraph — orchestrate LLM agents as stateful graphs. Multi-agent coordination, persistent state, human-in-the-loop, streaming, checkpointing, and conditional control flow. Build complex agent workflows.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill langgraph命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
| name | langgraph |
| description | LangGraph — orchestrate LLM agents as stateful graphs. Multi-agent coordination, persistent state, human-in-the-loop, streaming, checkpointing, and conditional control flow. Build complex agent workflows. |
| tags | ["langgraph","agent-orchestration","state-machine","multi-agent","langchain","llm","zorai"] |
LangGraph builds stateful, multi-step agent workflows as graphs. Supports conditional routing, human-in-the-loop checkpoints, persistent state, streaming, and multi-agent orchestration. The graph-based design enables complex, controllable agent behavior.
uv pip install langgraph
from typing import TypedDict
from langgraph.graph import StateGraph, END
class AgentState(TypedDict):
messages: list
next_step: str
def research(state):
return {"messages": state["messages"], "next_step": "write"}
def write(state):
return {"messages": state["messages"], "next_step": "review"}
def review(state):
return {"messages": state["messages"], "next_step": "__end__"}
graph = StateGraph(AgentState)
graph.add_node("research", research)
graph.add_node("write", write)
graph.add_node("review", review)
graph.set_entry_point("research")
graph.add_edge("research", "write")
graph.add_conditional_edges("write", lambda s: s["next_step"])
graph.add_edge("review", END)
app = graph.compile()
result = app.invoke({"messages": [], "next_step": ""})