Build a typed browser agent with LangGraph

Use Steel with LangGraph to build a typed browser agent with an explicit state-machine loop and a structured-output formatter node.

examples/langgraph

Jun RyuUpdated Apr 29, 2026

Terminal

Scaffolds a starter project locally. Requires the Steel CLI.

LangGraph builds agents as state machines: nodes do work, edges route control, and the agent loop is something you compose explicitly. LangChain ships the model wrapper (ChatAnthropic) and the @tool decorator; LangGraph ships the graph runtime, plus prebuilt ToolNode and tools_condition helpers.

This recipe is a four-tool browser agent: open_session, navigate, snapshot, extract. Each tool drives a Steel cloud session over Playwright. The graph has three nodes (agent, tools, format) and runs against github.com/trending/python, returning a Pydantic-validated FinalReport.

graph = StateGraph(State)
graph.add_node("agent", agent_node)
graph.add_node("tools", ToolNode(tools))
graph.add_node("format", format_node)

graph.add_edge(START, "agent")
graph.add_conditional_edges(
    "agent",
    tools_condition,
    {"tools": "tools", END: "format"},
)
graph.add_edge("tools", "agent")
graph.add_edge("format", END)

app = graph.compile()

If you'd rather skip the explicit construction, langgraph.prebuilt.create_react_agent(model, tools, prompt=SYSTEM, response_format=FinalReport) builds the same three-node graph in one call.

Run it

cd examples/langgraph
cp .env.example .env          # set STEEL_API_KEY and ANTHROPIC_API_KEY
uv sync
uv run playwright install chromium
uv run main.py

Get keys at app.steel.dev/settings/api-keys and console.anthropic.com. Each tool call prints its latency; the open_session tool returns a Live View URL you can open in another tab to watch the agent work.

Your output varies. Structure looks like this:

Steel + LangGraph Starter
============================================================
    open_session: 1840ms
  step: agent -> navigate | 1207 tokens
    navigate: 712ms
  step: agent -> snapshot | 1502 tokens
    snapshot: 412ms (3812 chars, 49 links)
  step: agent -> extract | 1741 tokens
    extract: 198ms (3 rows)
  step: agent -> (text only) | 4998 tokens
  step: format

Agent finished.

{
  "summary": "Three trending Python repos focused on agentic workflows...",
  "repos": [
    {
      "name": "owner/repo",
      "url": "https://github.com/owner/repo",
      "stars": "1,240",
      "description": "..."
    },
    ...
  ]
}

Releasing Steel session...
Session released. Replay: https://app.steel.dev/sessions/ab12cd34...

A run takes ~20 to 40 seconds and a few cents of Steel session time plus Anthropic tokens. The finally block calls steel.sessions.release(). Skip it and the session keeps billing until the default 5-minute timeout.

Make it yours

Use the prebuilt. Replace the explicit graph with create_react_agent(model, tools, prompt=SYSTEM, response_format=FinalReport) from langgraph.prebuilt. Same behavior, three lines.
Add a checkpointer. Pass checkpointer=MemorySaver() to graph.compile(...) and a thread_id in the run config. The graph snapshots state after every node, so a crashed run can resume from the last checkpoint. Use SqliteSaver (from langgraph-checkpoint-sqlite) for persistence across processes.
Stream events. Swap app.ainvoke(...) for async for event in app.astream_events(..., version="v2"). You'll see on_tool_start, on_tool_end, and on_chat_model_stream events you can pipe to a UI.
Trace with LangSmith. Set LANGSMITH_API_KEY and LANGSMITH_TRACING=true in .env. No code changes; every node and tool call shows up at smith.langchain.com.
Swap the model. Any langchain-* chat model works. ChatOpenAI(model="gpt-5-mini") swaps Anthropic for OpenAI without touching the graph.