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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.
Passed 19 of 23 test cases with the skill, and 19 of 23 without it.
1 fixed18 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
Should I install the graph agent library using standard pip install or uv pip install when following recommended workspace setup?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends uv pip install langgraph | Missed | Passed |
The instructions your model reads when the skill loads.
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": ""})
test cases passed, vs 19 / 23 without
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