LangGraph

Get Your Nordlys API Key

Overview

LangGraph is a low-level orchestration framework for building stateful, multi-actor applications with AI models. By integrating Nordlys with LangGraph’s ChatOpenAI, you get advanced Nordlys model while building complex agent workflows with graphs, state management, and tool integration.

Key Benefits

Keep existing workflows - No changes to your LangGraph graph structure
Nordlys model - Automatic Nordlys model for each agent interaction
Cost optimization - 30-70% cost reduction across agent executions
Stateful agents - Works seamlessly with LangGraph’s state management
Tool support - Nordlys selects function-calling capable models automatically
Streaming support - Real-time responses in agent workflows

Installation

pip install langgraph langchain-openai

Basic Usage

Initialize ChatOpenAI with Nordlys

The only change needed is to point LangGraph’s ChatOpenAI to Nordlys’s endpoint:

from langchain_openai import ChatOpenAI

model = ChatOpenAI(
api_key="your-nordlys-api-key",
base_url="https://api.nordlyslabs.com/v1",
model="nordlys/hypernova",
temperature=0,
)

Simple Chatbot with StateGraph

from langgraph.graph import StateGraph, MessagesState, START, END
from langchain_openai import ChatOpenAI

# Initialize model with Nordlys

model = ChatOpenAI(
api_key="your-nordlys-api-key",
base_url="https://api.nordlyslabs.com/v1",
model="nordlys/hypernova",
temperature=0,
)

# Define the chatbot function

def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": [response]}

# Create the graph

workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.add_edge(START, "agent")
workflow.add_edge("agent", END)

app = workflow.compile()

# Use the chatbot

result = app.invoke({
"messages": [{"role": "user", "content": "What is LangGraph?"}]
})

print(result["messages"][-1].content)

Advanced Examples

Agent with Tools

Nordlys automatically selects models that support function calling when tools are detected:

from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

# Define tools

@tool
def get_weather(location: str) -> str:
"""Get the current weather for a location.""" # Weather API call
return f"Weather in {location}: 72°F, sunny"

tools = [get_weather]

# Initialize model with tools

model = ChatOpenAI(
api_key="your-nordlys-api-key",
base_url="https://api.nordlyslabs.com/v1",
model="nordlys/hypernova",
temperature=0,
).bind_tools(tools)

# Define the agent function

def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": [response]}

# Model selection function

def should_continue(state: MessagesState):
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools"
return END

# Create the graph

workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.add_node("tools", ToolNode(tools))
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", should_continue, ["tools", END])
workflow.add_edge("tools", "agent")

app = workflow.compile()

# Use the agent

result = app.invoke({
"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]
})

Streaming Agent Responses

import { StateGraph, MessagesAnnotation } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({
  apiKey: process.env.NORDLYS_API_KEY,
  configuration: {
    baseURL: "https://api.nordlyslabs.com/v1",
  },
  modelName: "nordlys/hypernova",
  temperature: 0,
});

async function callModel(state: typeof MessagesAnnotation.State) {
  const response = await model.invoke(state.messages);
  return { messages: [response] };
}

const workflow = new StateGraph(MessagesAnnotation)
  .addNode("agent", callModel)
  .addEdge("__start__", "agent")
  .addEdge("agent", "__end__");

const app = workflow.compile();

// Stream the agent's responses
const stream = await app.stream({
  messages: [{ role: "user", content: "Write a short poem about AI" }],
});

for await (const chunk of stream) {
  if (chunk.agent && chunk.agent.messages) {
    const message = chunk.agent.messages[0];
    if (message && message.content) {
      process.stdout.write(message.content);
    }
  }
}

Multi-Agent Workflow

import { StateGraph, Annotation } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";

// Define custom state with multiple agents
const WorkflowState = Annotation.Root({
  messages: Annotation<any[]>({
    reducer: (x, y) => x.concat(y),
  }),
  currentAgent: Annotation<string>({
    reducer: (x, y) => y ?? x,
    default: () => "researcher",
  }),
});

// Initialize different models for different agents
const researcherModel = new ChatOpenAI({
  apiKey: process.env.NORDLYS_API_KEY,
  configuration: {
    baseURL: "https://api.nordlyslabs.com/v1",
  },
  modelName: "nordlys/hypernova",
  temperature: 0.3,
});

const writerModel = new ChatOpenAI({
  apiKey: process.env.NORDLYS_API_KEY,
  configuration: {
    baseURL: "https://api.nordlyslabs.com/v1",
  },
  modelName: "nordlys/hypernova",
  temperature: 0.7,
});

// Define agent nodes
async function researcher(state: typeof WorkflowState.State) {
  const response = await researcherModel.invoke([
    { role: "system", content: "You are a research specialist." },
    ...state.messages,
  ]);
  return {
    messages: [response],
    currentAgent: "writer",
  };
}

async function writer(state: typeof WorkflowState.State) {
  const response = await writerModel.invoke([
    { role: "system", content: "You are a creative writer." },
    ...state.messages,
  ]);
  return {
    messages: [response],
    currentAgent: "end",
  };
}

// Model layer function
function model layer(state: typeof WorkflowState.State) {
  return state.currentAgent === "writer" ? "writer" : "__end__";
}

// Build the workflow
const workflow = new StateGraph(WorkflowState)
  .addNode("researcher", researcher)
  .addNode("writer", writer)
  .addEdge("__start__", "researcher")
  .addConditionalEdges("researcher", model layer)
  .addEdge("writer", "__end__");

const app = workflow.compile();

// Execute multi-agent workflow
const result = await app.invoke({
  messages: [
    { role: "user", content: "Research and write about quantum computing" },
  ],
});

Integration Patterns

With Memory/Checkpointing

import { MemorySaver } from "@langchain/langgraph";
import { StateGraph, MessagesAnnotation } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({
  apiKey: process.env.NORDLYS_API_KEY,
  configuration: {
    baseURL: "https://api.nordlyslabs.com/v1",
  },
  modelName: "nordlys/hypernova",
  temperature: 0,
});

async function callModel(state: typeof MessagesAnnotation.State) {
  const response = await model.invoke(state.messages);
  return { messages: [response] };
}

const workflow = new StateGraph(MessagesAnnotation)
  .addNode("agent", callModel)
  .addEdge("__start__", "agent")
  .addEdge("agent", "__end__");

// Add memory for persistent conversations
const memory = new MemorySaver();
const app = workflow.compile({ checkpointer: memory });

// Use with conversation threads
const config = { configurable: { thread_id: "user-123" } };

// First message
await app.invoke(
  { messages: [{ role: "user", content: "My name is Alice" }] },
  config,
);

// Follow-up message (remembers context)
const result = await app.invoke(
  { messages: [{ role: "user", content: "What's my name?" }] },
  config,
);

Human-in-the-Loop

import { StateGraph, MessagesAnnotation } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";
import { MemorySaver } from "@langchain/langgraph";

const model = new ChatOpenAI({
  apiKey: process.env.NORDLYS_API_KEY,
  configuration: {
    baseURL: "https://api.nordlyslabs.com/v1",
  },
  modelName: "nordlys/hypernova",
  temperature: 0,
});

async function callModel(state: typeof MessagesAnnotation.State) {
  const response = await model.invoke(state.messages);
  return { messages: [response] };
}

const workflow = new StateGraph(MessagesAnnotation)
  .addNode("agent", callModel)
  .addEdge("__start__", "agent")
  .addEdge("agent", "__end__");

// Compile with interrupt before agent node for human approval
const memory = new MemorySaver();
const app = workflow.compile({
  checkpointer: memory,
  interruptBefore: ["agent"],
});

const config = { configurable: { thread_id: "conversation-1" } };

// Start the workflow (will pause before agent)
await app.invoke(
  { messages: [{ role: "user", content: "Send an email to the team" }] },
  config,
);

// Get current state for human review
const state = await app.getState(config);
console.log("Pending action:", state.values);

// Human approves and continues
await app.invoke(null, config);

Configuration Options

Model Selection

Use the Nordlys model ID:

modelName: "nordlys/hypernova";

Temperature and Parameters

All standard ChatOpenAI parameters work with Nordlys:

const model = new ChatOpenAI({
  apiKey: process.env.NORDLYS_API_KEY,
  configuration: {
    baseURL: "https://api.nordlyslabs.com/v1",
  },
  modelName: "nordlys/hypernova",
  temperature: 0.7,
  maxTokens: 1000,
  topP: 1,
  frequencyPenalty: 0,
  presencePenalty: 0,
});

Best Practices

Use nordlys/hypernova across agent nodes
Different temperatures for different agents (research vs creative)
Leverage checkpointing for stateful conversations with memory
Use conditional edges for complex agent logic
Add human-in-the-loop for critical decisions with interrupt points
Tool integration - keep tool calls explicit and deterministic

Error Handling

Nordlys retries transient errors automatically. For comprehensive error handling patterns, see the Error Handling Guide.

import { StateGraph, MessagesAnnotation } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";

// Nordlys handles Nordlys model failures automatically
const model = new ChatOpenAI({
  apiKey: process.env.NORDLYS_API_KEY,
  configuration: {
    baseURL: "https://api.nordlyslabs.com/v1",
  },
  modelName: "nordlys/hypernova", // Nordlys model with automatic retries
  temperature: 0,
});

async function callModel(state: typeof MessagesAnnotation.State) {
  const response = await model.invoke(state.messages);
  return { messages: [response] };
}

const workflow = new StateGraph(MessagesAnnotation)
  .addNode("agent", callModel)
  .addEdge("__start__", "agent")
  .addEdge("agent", "__end__");

const app = workflow.compile();

// Nordlys handles errors automatically - no try/catch needed for basic usage
const result = await app.invoke({
  messages: [{ role: "user", content: "Hello, how are you?" }],
});

Complete Example

See the complete LangGraph example for a full working implementation including:

Stateful agent with memory
Tool integration with conditional Nordlys model
Multi-agent workflows
Streaming responses
Human-in-the-loop patterns
Error handling

Next Steps

Explore LangGraph prebuilt agents with Nordlys
Build multi-agent systems with Nordlys model
Implement persistence for long-running agents
Add human-in-the-loop approval workflows
Try RAG agents with cost optimization

Getting Started

Developer Tools

Framework Integrations

API Reference

Support

Get Your Nordlys API Key

Overview

Key Benefits

Installation

Basic Usage

Initialize ChatOpenAI with Nordlys

Simple Chatbot with StateGraph

Advanced Examples

Agent with Tools

Streaming Agent Responses

Multi-Agent Workflow

Integration Patterns

With Memory/Checkpointing

Human-in-the-Loop

Configuration Options

Model Selection

Temperature and Parameters

Best Practices

Error Handling

Complete Example

Next Steps

Getting Started

Developer Tools

Framework Integrations

API Reference

Support

Documentation Index

​Get Your Nordlys API Key

​Overview

​Key Benefits

​Installation

​Basic Usage

​Initialize ChatOpenAI with Nordlys

​Simple Chatbot with StateGraph

​Advanced Examples

​Agent with Tools

​Streaming Agent Responses

​Multi-Agent Workflow

​Integration Patterns

​With Memory/Checkpointing

​Human-in-the-Loop

​Configuration Options

​Model Selection

​Temperature and Parameters

​Best Practices

​Error Handling

​Complete Example

​Next Steps

Get Your Nordlys API Key

Overview

Key Benefits

Installation

Basic Usage

Initialize ChatOpenAI with Nordlys

Simple Chatbot with StateGraph

Advanced Examples

Agent with Tools

Streaming Agent Responses

Multi-Agent Workflow

Integration Patterns

With Memory/Checkpointing

Human-in-the-Loop

Configuration Options

Model Selection

Temperature and Parameters

Best Practices

Error Handling

Complete Example

Next Steps