Use LangGraph with Radium

LangGraph does not connect to a model provider by itself. Configure a Radium-backed 
ChatOpenAI model and call it from your graph nodes.
Before you start
You need Python 3.10 or newer, a Radium API key, and a terminal or PowerShell window.
1. Create a project and install the packages
macOS or Linux:
mkdir radium-langgraph
cd radium-langgraph
python3 -m venv .venv
source .venv/bin/activate
python -m pip install langgraph langchain-openai
Windows PowerShell:
mkdir radium-langgraph
cd radium-langgraph
py -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install langgraph langchain-openai
2. Set your Radium credentials
macOS or Linux:
export RADIUM_API_KEY="your-radium-api-key"
export RADIUM_MODEL="hal-1.0"
Windows PowerShell:
$env:RADIUM_API_KEY = "your-radium-api-key"
$env:RADIUM_MODEL = "hal-1.0"
3. Create 
main.py
import os
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, MessagesState, StateGraph
api_key = os.getenv("RADIUM_API_KEY")
if not api_key:
    raise SystemExit("Set RADIUM_API_KEY before running this program.")
llm = ChatOpenAI(
    model=os.getenv("RADIUM_MODEL", "hal-1.0"),
    api_key=api_key,
    base_url="https://api.radium.cloud/v1",
    max_tokens=512,
    temperature=0,
    timeout=90,
    max_retries=0,
)
def call_radium(state: MessagesState):
    return {"messages": [llm.invoke(state["messages"])]}
builder = StateGraph(MessagesState)
builder.add_node("call_radium", call_radium)
builder.add_edge(START, "call_radium")
builder.add_edge("call_radium", END)
graph = builder.compile()
result = graph.invoke(
    {
        "messages": [
            {"role": "system", "content": "Follow the user's instruction exactly."},
            {"role": "user", "content": "Reply with exactly: Radium connected."},
        ]
    }
)
content = result["messages"][-1].content
if not content or not str(content).strip():
    raise RuntimeError("Radium returned no visible text.")
print(f"RADIUM_RESPONSE: {str(content).strip()}")
4. Run it
python main.py
Expected output:
RADIUM_RESPONSE: Radium connected.
Choose a model
export RADIUM_MODEL="tycho-1.0"  # or hal-1.0 or clarke-1.0
python main.py
Migrate an existing graph
Replace the model used inside your existing nodes:
llm = ChatOpenAI(
    model="hal-1.0",
    api_key=os.environ["RADIUM_API_KEY"],
    base_url="https://api.radium.cloud/v1",
    max_tokens=512,
)
Your state schema, nodes, edges, routing, and checkpoints do not need to change.
Troubleshooting
Authentication errors: check the API key in the active terminal.
404: use the exact base URL and one of the three listed model IDs.
State errors: make sure the graph node returns a 
messages list.
Import errors: activate 
.venv and rerun the installation command.
Validation
Version note: these instructions were verified with Python 3.12.7, 
langgraph==1.2.11, and 
langchain-openai==1.6.0 on August 20, 2026. You do not need those exact package versions. Text generation and an agent tool loop passed with 
hal-1.0, 
clarke-1.0, and 
tycho-1.0.
Reference: LangGraph quick start.
Next
API quickstart, for calling Radium directly
Tool calling, for the 
tool_use and 
tool_result contract