| 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"] |
Overview
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.
Installation
uv pip install langgraph
Simple Graph
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": ""})
References
