LangGraph

LangGraph builds agents as state machines: nodes do work, edges route control, and the agent loop is something you compose explicitly rather than something the framework hides for you. LangChain ships the model wrappers and @tool decorator; LangGraph ships the runtime, plus prebuilt ToolNode and tools_condition helpers that turn three nodes and four edges into a tool-calling agent. The Steel integration runs each @tool against a Steel cloud session, so the agent loop drives a real browser.

Requirements

• Steel API Key: Active Steel subscription
• Model provider key: Anthropic, OpenAI, or any other LangChain-supported provider
• Python: 3.10+
• Packages: langgraph, langchain-anthropic (or another langchain-* provider), steel-sdk, playwright

Connect Steel to LangGraph

Define @tool functions that drive a Steel-backed Playwright page, build a StateGraph with agent and tools nodes, and wire tools_condition between them:

from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.graph import END, START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition
from playwright.async_api import async_playwright
from steel import Steel

steel = Steel(steel_api_key=STEEL_API_KEY)

@tool
async def open_session() -> dict:
    """Open a Steel cloud browser session."""
    global _page
    session = steel.sessions.create()
    playwright = await async_playwright().start()
    browser = await playwright.chromium.connect_over_cdp(
        f"{session.websocket_url}&apiKey={STEEL_API_KEY}"
    )
    _page = browser.contexts[0].pages[0]
    return {"session_id": session.id, "live_view_url": session.session_viewer_url}

tools = [open_session]
model = ChatAnthropic(model="claude-haiku-4-5").bind_tools(tools)

async def agent_node(state: MessagesState) -> dict:
    return {"messages": [await model.ainvoke(state["messages"])]}

graph = StateGraph(MessagesState)
graph.add_node("agent", agent_node)
graph.add_node("tools", ToolNode(tools))
graph.add_edge(START, "agent")
graph.add_conditional_edges("agent", tools_condition)
graph.add_edge("tools", "agent")

app = graph.compile()

tools_condition routes to "tools" when the assistant message has tool calls and to END when it doesn't. The tools -> agent edge is what makes it a loop. For a typed final answer, add a format node that runs model.with_structured_output(Schema) on the conversation; for one-line wiring, swap the explicit graph for langgraph.prebuilt.create_react_agent(model, tools, response_format=Schema).

Full runnable starter: Steel + LangGraph recipe →

Resources

• LangGraph documentation – State graphs, prebuilts, checkpointing, and streaming
• LangChain @tool reference – Tool definitions and Pydantic schemas
• LangSmith – Tracing for every node and tool call
• Steel Sessions API reference – Programmatic session control for Steel browsers
• Steel Discord – Get help and share what you build