> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orq.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LangGraph framework integration

> Connect LangGraph to Orq.ai's AI Gateway for complete observability, built-in reliability, and access to 300+ LLMs across 20+ providers.

## AI Gateway

### Overview

LangGraph is a framework for building stateful, multi-actor AI applications with LLMs. It extends LangChain with graph-based agent orchestration, cycles, and controllability. Connecting LangGraph to Orq.ai's AI Gateway enables production-ready agentic workflows with access to 300+ models.

### Key Benefits

Orq.ai's AI Gateway enhances LangGraph applications with:

<CardGroup cols={2}>
  <Card title="Complete Observability" icon="chart-line">
    Track every agent step, tool use, and graph transition with detailed traces
  </Card>

  <Card title="Built-in Reliability" icon="shield-check">
    Automatic fallbacks, retries, and load balancing for production resilience
  </Card>

  <Card title="Cost Optimization" icon="chart-pie">
    Real-time cost tracking and spend management across all AI operations
  </Card>

  <Card title="Multi-Provider Access" icon="cubes">
    Access 300+ LLMs and 20+ providers through a single, unified integration
  </Card>
</CardGroup>

### Prerequisites

Before integrating LangGraph with Orq.ai, ensure the following are in place:

* An Orq.ai account and [API Key](/docs/ai-gateway/configuration/api-keys)
* Python 3.8 or higher

<Info>
  To set up an API key, see [API keys & Endpoints](/docs/ai-gateway/configuration/api-keys).
</Info>

### Installation

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
pip install langgraph langchain-openai langchain-core
```

### Configuration

Configure LangGraph to use Orq.ai's AI Gateway by passing a `ChatOpenAI` instance with a custom `base_url`:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from langchain_openai import ChatOpenAI
import os

llm = ChatOpenAI(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)
```

> **base\_url**: `https://api.orq.ai/v3/router`

### Basic Agent Example

Here's a complete example using `create_react_agent` with a tool:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
import os

llm = ChatOpenAI(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

@tool
def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"The weather in {location} is sunny and 72°F"

agent = create_react_agent(llm, tools=[get_weather])

result = agent.invoke({"messages": [("user", "What's the weather in San Francisco?")]})
print(result["messages"][-1].content)
```

### Agent with Multiple Tools

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
import os

llm = ChatOpenAI(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

@tool
def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"The weather in {location} is sunny and 72°F"

@tool
def add(a: int, b: int) -> int:
    """Add two integers."""
    return a + b

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers."""
    return a * b

agent = create_react_agent(
    llm,
    tools=[get_weather, add, multiply],
    system_prompt="You are a helpful assistant with access to weather and math tools.",
)

result = agent.invoke({
    "messages": [("user", "What is 15 * 4? Also check the weather in Tokyo.")]
})
print(result["messages"][-1].content)
```

### Streaming

Stream agent steps as they happen:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
import os

llm = ChatOpenAI(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

@tool
def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"The weather in {location} is sunny and 72°F"

agent = create_react_agent(llm, tools=[get_weather])

for chunk in agent.stream(
    {"messages": [("user", "What's the weather in Paris?")]},
    stream_mode="updates",
):
    print(chunk)
```

### Model Selection

With Orq.ai, any supported model from 20+ providers can be used:

```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
import os

@tool
def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"The weather in {location} is sunny and 72°F"

# Use Claude
claude_agent = create_react_agent(
    ChatOpenAI(
        model="anthropic/claude-sonnet-4-6",
        api_key=os.getenv("ORQ_API_KEY"),
        base_url="https://api.orq.ai/v3/router",
    ),
    tools=[get_weather],
)

# Use Gemini
gemini_agent = create_react_agent(
    ChatOpenAI(
        model="google-ai/gemini-2.5-flash",
        api_key=os.getenv("ORQ_API_KEY"),
        base_url="https://api.orq.ai/v3/router",
    ),
    tools=[get_weather],
)

result = claude_agent.invoke({"messages": [("user", "What's the weather in London?")]})
print(result["messages"][-1].content)

result = gemini_agent.invoke({"messages": [("user", "What's the weather in Tokyo?")]})
print(result["messages"][-1].content)
```